István György Tóth – Tamás Keller: Income distributions, inequality perceptions and redistributive claims in European societies WP5: Political and cultural impacts Draft of Discussion paper 5.2.3. Prepared to the Y1 meeting in Milan 3-5 February 2011
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István György Tóth – Tamás Keller: Income distributions, inequality perceptions and redistributive claims in European societies WP5: Political and cultural.
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István György Tóth – Tamás Keller:
Income distributions, inequality perceptions and redistributive claims in European societies
WP5: Political and cultural impactsDraft of Discussion paper 5.2.3.
Prepared to the Y1 meeting in Milan 3-5 February 2011
I. Introduction II. Research questionsIII. Data and definitionsIV. Inequalities, their perceptions and redistributive
attitudes across countries (macro perspectives) V. Micro- and socio-economic correlates (multivariate
analysis, individual and contextual effects) VI. Summary and conclusions
Outline of the paperOutline of the paper
Country U is more unequal than country E. Therefore, it redistributes more (tU > tE)
BUT: Empirically, this is not really the case. The evidence is rather mixed!
againstfor mean
Median (E)Median (U
tEtU
The proposition by The proposition by Meltzer & RichardMeltzer & Richard ( (19811981): ):
No
of p
erso
ns
incomes
Broad frame of understanding: Broad frame of understanding:
An even broader frame of understandingAn even broader frame of understanding
• People base their opinions/judgements on an assessment of their relative positions: what if they misjudge their positions?
• Their motivation depends on self interest: what about alternative motivations (public values, altruism, convictions about „good, caring society” etc)
• Self interest taken at direct money terms– What about expectations (of their mobility, of their potential gains from
redistribution, etc)?– What about the insurance motive?
• Tax rate and expenditure defined unequivocally: in reality both taxes and expenditures are more complex (also in their incidence!)
• Voters do not take moral standing about recipients (what if they do about the deserving and the undeserving poor)?
• The political system translates preferences into public spending in a straightforward way: this is not (always) the case
• The redistribution affects the final shape of inequalities a great deal (also: reverse causality..)
A list of factors why empirics might deviate from MR A list of factors why empirics might deviate from MR predictionspredictions
Theoretical frameworkTheoretical framework
Translation mechanisms (1): socio-economics to redistributive attitudes
Micro (motivations):PerceptionsInterestsAttitudes
Translation mechanisms (1): socio-economics to redistributive attitudes
Micro (motivations):PerceptionsInterestsAttitudes
InequalityInequality
RedistributionRedistribution
Translation mechanisms (from policies to modified inequalities)
Tax-transfer shemesRegulation, etc…
Translation mechanisms (from policies to modified inequalities)
Tax-transfer shemesRegulation, etc…
Translation mechanisms (2): from demand for redistribution to policies
• Q1: What individual socio-economic characteristics drive (the formation of redistributive preferences?
• Q2: How do various contextual factors (most importantly: aggregate income inequalities) shape redistributive preferences?
• Q3: What effect the structure of inequality has on the attitudes of the middle income classes?
Research questionsResearch questions
Data and DefinitionsData and Definitions
The empirical model used in the analysisThe empirical model used in the analysis
We want to predict redistributive preference (RPI) by individual attributes (X) AND by contextual variables (Z)
RPI = a + bXRPI = a + bXijij + cZ + cZjj +U +U0j0j + E + Eijij
i = The number of individuals in the analysis (Level 1)j = The number of countries (Level 2)a = Interceptb and c = Coefficients at individual and country level, respectivelyEij = Level 1 residualU0j = Level 2 residual
The effects of individual attributes on RPI were predicted with simple OLS regression (with clustered standard error)
RPI is an index coming from principal component analysis
CorrCorr.. with RPI with RPICorrCorr.. with RPI with RPI
-0,8
-0,6
-0,4
-0,2
0,0
0,2
0,4
0,6
0,8
1,0
NL BE LT CZ DK SI SE UK FR LU AT DE EE FI EU SK PL PT RO IT MT IE LV ES BGHU CY GR
Data and DefinitionsData and Definitions
The mean value of RPI by countriesThe mean value of RPI by countries
Data and DefinitionsData and Definitions
Measuring material statusMeasuring material status
No objective income data was availableNo objective income data was available!!!!!!
1 2 3 4 5 6 missing• “much higher” income (qa43) than 2000 Euro/months (qa42) = 6
• “much lower” income (qa43) than 500 Euro/month (qa42) = 1
• make ends meet (qa35) “very easy” = 6
• make ends meet (qa35) “with great difficulty” = 1
Data and DefinitionsData and Definitions
Independent Independent variablesvariables (X) (X) in the regression in the regression modelsmodels
I. Basic model Country dummies [ reference: Germany]
II. Demography
(controls only)
Gender: male=1 [female], Variable d10.
Age: 18-30, 31-40, [41-50], 51-60, 61-70 and 70+; Variable vd11.
School: less than primary, primary, [secondary], higher, no education; Variable d8
Settlement: village, [small town], large town; Variable d25
Household size. The sum of the variables vd40a+vd40b+vd40c
III. Material self interest
Material status index : continuous, see the construction above
Labour market position: self employed, [employed], not working; Variable c14.
RPI = a + bXRPI = a + bXijij + E + EijijRPI = a + bXRPI = a + bXijij + E + Eijij
Data and DefinitionsData and Definitions
IV. Expectations
Question used: “What are your expectations for the next twelve months: will the next twelve months be ... when it
comes to the financial situation of your household?” (qa38)Future expectations better, [same], worse
Three binary coded variable
V. Failure attribution
Question used “Why in your opinion are there people who live in poverty? Here are four opinions: which is closest to
yours?” (qa8)Poverty attribution: [unluck], lazy, injust, part of progress
Four binary coded variables.
IndependentIndependent variables variables in the regression modelsin the regression models
IV: the variable on living standard improvement IV: the variable on living standard improvement ≠≠ social mobility.social mobility.V: meaning of the question: isV: meaning of the question: is poverty private failure or poverty private failure or social social failurefailure??
Data and DefinitionsData and Definitions
VI. Social context/values
Poverty perception: Binary coded variable: 1, if someone perceive that poverty is “very widespread” in the country
(qa4), the value is zero otherwise
Perception of („lot of”) conflicts between poor-rich, young-old, managers-workers and between ethnic groups
Binary coded variablesQuestions from qa15_1 to qa15_4,
VII. Inequality sensitivity
Binary coded variable: 1, if someone “totally agreed” the question that “income differences between people are far
too large” (qa14_2), and the value is zero otherwise
IndependentIndependent variables variables in the regression modelsin the regression models
Data and DefinitionsData and Definitions
Contextual variablesContextual variables (Z) (Z) in the regression modelsin the regression models
Contextual variable Definition
Number of
countries
P95/P5The income of the person at the 95th percentile of the
income distribution divided with the income of the person at the 5th percentile
17
P95/P50The income of the person at the 95th percentile of the
income distribution divided with the income of the median income person
17
P50/P5The income of the median income person in the
income distribution divided with the income of the person at the 5th percentile
17
Gini Gini coefficient 17Countries from LIS wave VI: AT, DE, DK, ES, FI, GR, HU, IT LV, PL, SE, UKCountries from LIS wave VI: AT, DE, DK, ES, FI, GR, HU, IT LV, PL, SE, UKCountries from LIS wave V: BE, EE, IE, NL, SICountries from LIS wave V: BE, EE, IE, NL, SI
All contextual data All contextual data ccome fromome from Luxembourg Luxembourg Income StudyIncome Study (LIS) (LIS)We used use distance-based rather than variance based inequality measuresWe used use distance-based rather than variance based inequality measures
RPI = a + bXRPI = a + bXij ij + cZ+ cZjj +U +U0j 0j + E+ EijijRPI = a + bXRPI = a + bXij ij + cZ+ cZjj +U +U0j 0j + E+ Eijij
Macro level analysisMacro level analysisInequalitiesInequalities and redistributive attitudes across countriesand redistributive attitudes across countries
UKSISE
PL
NL
LU
IT
IE
HU
GR
FI
ES
EE
DK
DE
BE
AT
R2 = 0.4806
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1.9 2.1 2.3 2.5 2.7 2.9 3.1
P50/P5, (LIS wave V. and wave VI.)
Red
istr
ibu
tive
Pre
fere
nce
Ind
ex (
RP
I, fa
cto
r lo
adin
gs
fro
m P
CA
), E
B.7
2.1,
200
9.
UKSISE
PL
NL
LU
IT
IE
HU
GR
FI
ES
EE
DK
DE
BE
AT
R2 = 0.2331
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1.5 2 2.5 3
P95/P50, (LIS wave V. and wave VI.)
Red
istr
ibu
tive
Pre
fere
nce
Ind
ex (
RP
I, fa
cto
r lo
adin
gs
fro
m P
CA
), E
B.7
2.1,
200
9.
Positive relationship between inequality and RPIPositive relationship between inequality and RPIRPIRPI is more influenced by the lower part (below median) of the income is more influenced by the lower part (below median) of the income distribution, than by the upper part (above medistribution, than by the upper part (above mediandian).).
Macro level analysisMacro level analysisInequalitiesInequalities and redistributive attitudes across countriesand redistributive attitudes across countries
UKSISE
PL
NL
LU
IT
IE
HU
GR
FI
ES
EE
DK
DE
BE
AT
R2 = 0.3049
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
0.2 0.25 0.3 0.35 0.4
Gini, (LIS wave V. and wave VI.)
Red
istr
ibu
tive
Pre
fere
nce
Ind
ex (
RP
I, fa
cto
r lo
adin
gs
fro
m P
CA
), E
B.7
2.1,
200
9.
UKSISE
PL
NL
LU
IT
IE
HU
GR
FI
ES
EE
DK
DE
BE
AT
R2 = 0.3742
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
3 4 5 6 7 8
P95/P5, (LIS wave V. and wave VI.)
Red
istr
ibu
tive
Pre
fere
nce
Ind
ex (
RP
I, fa
cto
r lo
adin
gs
fro
m P
CA
), E
B.7
2.1,
200
9.
Positive relationship between inequality and RPIPositive relationship between inequality and RPIGini performs weaker than the distance based measuresGini performs weaker than the distance based measures
Q2. The Q2. The role of corole of conntextual factorstextual factors
Random intercept models, Random intercept models, different inequality measuresdifferent inequality measures
A. B. C.
Inequality measureInequality measure's
estimated fixed effect
Proportion of variance attributed
to the random between-country
effect
Proportion of between country
variance transmitted through the inequality
measure
P95/P5 0.17*** 5.68% 26.95%
P95/P50 0.69** 6.74% 13.32%
P50/P5 0.72*** 4.60% 40.89%
Gini 5.09** 6.74% 13.32%
*** p<1%; ** p<5%,; * p<10
In countries In countries with largewith large inequalities, respondent are more pro-redistribution inequalities, respondent are more pro-redistribution. . BBetween-country differences in RPI can etween-country differences in RPI can partly partly be attributed to inequalitybe attributed to inequality..
Low inequalitiesLow inequalities: DK, NL, SE, FI: DK, NL, SE, FI / / Middle inequalitiesMiddle inequalities: SI, AT, BE, LU, DE, HU, IE: SI, AT, BE, LU, DE, HU, IE / / Large inequalitiesLarge inequalities: PL, UK, ES, GR, IT, EE: PL, UK, ES, GR, IT, EE
Opinion differences in equal countries
Opinion differences in unequal countries
Is Is the impact of material status differthe impact of material status differentent in various kinds of in various kinds of inequainequallity regimesity regimes??
Q2. The Q2. The role of corole of conntextual factorstextual factors
Standardized regression coefficients of material status and inequalityStandardized regression coefficients of material status and inequality
HU
UK
SE
AT
NL
LU
IT
ES
BE
SI
PLEE
IE
FI
GR
DE
DK
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
1.9 2.1 2.3 2.5 2.7 2.9 3.1
P50/P5
Sta
nd
ard
ized
reg
ress
ion
co
eff
icie
nt
of
ma
teri
al s
tatu
s in
mo
de
l VI.
Not significant Significant
HU
UK
SE
AT
NL
LU
IT
ES
BE
SI
PLEE
IE
FI
GR
DE
DK
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
1.5 2 2.5 3
P95/P50
Sta
nd
ard
ized
reg
ress
ion
co
eff
icie
nt
of
ma
teri
al s
tatu
s in
mo
de
l VI.
Not significant Significant
Standardized regression coefficients are calculated from country level OLS regressions, using Model VI.Standardized regression coefficients are calculated from country level OLS regressions, using Model VI.The level of significance used in the grouping (p<0.1)The level of significance used in the grouping (p<0.1)
Q2. The Q2. The role of corole of conntextual factorstextual factors
TThe difference between he difference between rich and poorrich and poor respondents’ respondents’ RPIRPI is is the the largelargestst in countries where inequalities are in the middle range. in countries where inequalities are in the middle range.
1. Demand for redistribution, in addition to rational self interest, is also driven by general attitudes about the role of personal responsibility in one’s own fate, of general beliefs about causes of poverty and the like.
2. The overall levels of income inequalities do explain (part of) cross country variance in demand for redistribution.
3. Larger aggregate inequalities do correspond to larger redistributive demands (on country level).
4. In countries having larger level of aggregate inequalities the general redistributive preference (of the rich, of the middle and of the poor) is higher.
5. The slope of this socio-economic gradient seems, however, steeper in countries with middle inequality levels.
Summary/ConclusionSummary/Conclusion
Thank you for your attention!
www.tarki.hu
Multivariate analysisMultivariate analysisOne possible explanationOne possible explanation on the difference between rich and on the difference between rich and
poor in various kinds of inequality regimespoor in various kinds of inequality regimes
UK
SI
SE
PL
NL
LU
IT
IE
HU
GR
FI
ES
EE
DK
DE
BE
AT
R2 = 0.3545
0.22
0.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
0 20 40 60 80 100
Self reported high income status (%)
Sta
nd
ard
ized
reg
ress
ion
co
effi
cien
t o
f m
ater
ial
stat
us
in m
od
el V
I. (
abso
lute
val
ue)
TThe richer the society, the less dohe richer the society, the less do income explainincome explainss individuals’ preferencesindividuals’ preferences..
*Economic Development and Happiness: Evidence from 32 Nations
UK
SI
SE
PL
NL
IT
IE
HU
GR
FI
ES
EE
DK
DEBE
AT
R2 = 0.2441
0.22
0.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
10000 15000 20000 25000 30000 35000
GDP in Purchasing Power Standard per inhabitant (2009)
Sta
nd
ard
ized
reg
ress
ion
co
effi
cien
t o
f m
ater
ial
stat
us
in m
od
el V
I. (a
bso
lute
val
ue)
Standardized regression coefficients are calculated from country level OLS regressions, using Model VI.