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DistributionalConsequencesofLaborDemandAdjustmentstoaDownturn OlivierBargain,HerwigImmervoll,AndreasPeichl,SebastianSiegloch GINIDISCUSSIONP APER3 DECEMBER2010 AN EWDATASETOFEDUCATIONALI NEQUALITY ElenaMeschiandFrancescoScervini GROWINGINEQUALITIES’IMPACTS

GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

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Page 1: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Distributional!Consequences!of!Labor!Demand!Adjustments!to!a!Downturn!Olivier!Bargain,!Herwig!Immervoll,!Andreas!Peichl,!Sebastian!Siegloch

GIN

I DP!1

GINI!DISCUSSION!PAPER!3DECEMBER!2010

A!NEW!DATASET!OF!EDUCATIONAL!INEQUALITY!

Elena!Meschi!and!Francesco!Scervini!

GROWING!INEQUALITIES’!IMPACTS

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Acknowledgement

We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo

Fiorio, Marco Leonardi for suggestions and useful discussions since the early stages of this work. Precious

insights by participants in the gini’s Measurements and Methods Workshop, Amsterdam, October 2010, are

also gratefully acknowledged.

December 2010

© Elena Meschi, Francesco Scervini, Amsterdam

General contact:[email protected]!

Correspondending addresses: Elena Meschi & Francesco Scervini. University of Milan – deas, Department of Economics, Business and Statistics – Via Conservatorio, 7 – 20122 – Milan (Italy)

Elena Meschi. Department of Quantitative Social Science – Institute of Education, 20 Bedford Way, Lon-don WC1H0AL (UK)

Bibliographic!Information

Meschi, E., Scervini, F. (2010). A new dataset on educational inequality. Amsterdam, AIAS, GINI Discus-

sion Paper 3.

Information may be quoted provided the source is stated accurately and clearly.

Reproduction for own/internal use is permitted.

This paper can be downloaded from our website www.gini-research.org.

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A!New!Dataset!on!Educational!Inequality!

!

!

15!December!2010!DP!3

Elena!Meschi

University!of!Milan!–!DEASDepartment!of!Quantitative!Social!Science!–!Institute!of!Education

[email protected]

Francesco!Scervini

University!of!Milan!–[email protected]

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A!New!Dataset!of!Educational!Inequality

Table!of!contents

ABSTRACT!......................................................................................................................................................................7

1.! INTRODUCTION!..........................................................................................................................................................9

2.! MEASURES!OF!EDUCATIONAL!ATTAINMENT!.........................................................................................................................11

3.! DATA!AGGREGATION!AND!SELECTION!RULES!.......................................................................................................................15

4.! DATA!SOURCES!........................................................................................................................................................17

4.1.!European!Social!Survey!(ess)...................................................................................................................................17

4.2.!European!Union!Statistics!on!Income!and!Living!Conditions!(EU-SILC)!....................................................................17

4.3.!International!Adult!Literacy!Survey!(IALS)!...............................................................................................................18

4.4.!International!Social!Survey!Programme!(ISSP)!.........................................................................................................19

4.5.!Summary!..................................................................................................................................................................20

5.! VARIABLES!............................................................................................................................................................23

6.! CONSISTENCY!OF!MEASURES!ACROSS!SURVEYS!...................................................................................................................25

REFERENCES!..................................................................................................................................................................27

A!CONSISTENCY!GRAPHICS!–!INEQUALITY!INDICES!.....................................................................................................................29

B!CONSISTENCY!GRAPHICS!–!PERCENTILES!.............................................................................................................................35

C!CONSISTENCY!GRAPHICS!–!ATTAINMENTLEVELS!.....................................................................................................................41

GINI!DISCUSSION!PAPERS!................................................................................................................................................43

INFORMATION!ON!THE!GINI!PROJECT!.....................................................................................................................................45

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A!New!Dataset!of!Educational!Inequality

Abstract

This paper describes a new dataset collecting measures of educational level and inequality for 31 countries

over several birth cohorts. Drawing on four representative international datasets (ESS, EIJ­SILC, IALS and ISSP), we

collect measures of individual educational attainment and aggregate them to generate synthetic indices of educa­

tion level and dispersion by countries and birth cohorts. The paper provides a detailed description of the procedures

and methodologies adopted to build the new dataset, analyses the validity and consistency of the measures across

surveys and discusses the relevance of these data for future research.

The .csv, .dta, .xml dataset is readily available to GINI members (see website data portal); other scholars may

put in a request to the authors.

!"#$%&'(()*+',)-./$0123$4563$72

Keywords: new dataset, educational attainment, educational inequality, dispersion indices, cross-country,

cohorts

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A!New!Dataset!of!Educational!Inequality

1.! Introduction

The positive effects of human capital are widely recognised in the economic literature. Accumulation of hu­

man capital is proved to be crucial for economic growth (see, among others, Hanushek and Kimko (2000); Krueger

!"#$%&"#!'($)*++,-.$#/$(!$01/"2/$!"#$345/"/6'$)*++7--8$!"#$9/"/:6&!($24$&"#&;&#1!(<$!"#$<46&/2&/<=$0>45$2'/$

individual point of view, more educated people are more likely to have better labour market outcomes in terms of

employability and wages (see the survey by Harmon et al. (2003)), but they also experience better health, fertility,

well­being, less probability of engaging in crime and other non monetary outcomes (see for example Lochner and

Moretti (2004) and the survey by Grossman (2006)). Moreover, education has wide spill­over effects, generating

"42$4"(?$@>&;!2/8$912$!(<4$@19(&6$9/"/:2<=$A"6>/!<&"B$/#16!2&4"$<//5<$&"$C!62$ 24$&"D1/"6/$@4<&2&;/(?$!(<4$<46&!($

cohesion, citizenship, and political participation (Milligan et al., 2004).

The effects of the distribution of educational attainment have in contrast not been so widely studied by the

economic literature. Nevertheless, the way human capital is distributed across the population may also have im­

portant economic consequences, affecting for example income distribution (see Checchi (2004); De Gregorio and

Lee (2002); Park (1996)), and economic growth (see Lopez et al. (1998)). However, the empirical evidence on this

topic is still very scant, possibly due to the lack of crosscountry comparable data on educational inequality.

We have created an original dataset that provides cross­country measures of educational attainment and in­

equality, drawing on four international representative surveys (ESS1, EU­SILC2, IALS3, ISSP4). The dataset covers a sam­

ple of 31 countries5, and provides a wide set of indicators of educational attainment and educational inequality over

</;/>!($9&>2'$64'4>2<=$A"$@!>2&61(!>$E/$49</>;/$,F$64'4>2<8$!BB>/B!2/#$!2$GH?/!><$&"2/>;!(<8$E'/>/$2'/$:><2$64'4>2$&<$

made of individuals born between 1920 and 1924, and the last one includes people born between 1980 and 1984.

Our dataset presents some remarkable novelties compared to the existing datasets on educational attainment

(e.g. Barro and Lee (1996, 2001, 2010); Cohen and Soto (2007)). First of all, our data are organised by birth cohort

rather than survey years. This allows to enlarge the period covered, as we have information from the beginning of

the 20s, while existing datasets generally start from the 50s or 60s (see, for example, Barro and Lee (2010); Cohen

and Soto (2007)). Moreover, this approach allows to observe the evolution of educational attainment for individu­

als born in different periods, possibly characterized by distinct institutional features of the school systems and this

is particularly useful for analyses of the determinants of educational outcomes. Since formal education occurs

1 European Social Survey2 European Union Statistics on Income and Living Conditions3 International Adult Literacy Survey4 International Social Survey Programme5 The dataset covers 31 countries, 25 EU members states (all EU members except Cyprus and Malta), plus other six sizeable OECD countries

(Australia, Canada, Japan, Norway, Switzerland, United States)

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Elena!Meschi!and!Francesco!Scervini

mainly in early stage of life and remains invariant over the life cycle, a cohort approach seems more sensible in

this case. Second, we use multiple sources to create the aggregate measures and this improves the robustness and

reliability of the data. Third, our dataset presents three alternative (but complementary) measures of education.

I/<&#/$2'/$<2!"#!>#$;!>&!9(/$4"$?/!><$4C$/#16!2&4"$645@(/2/#8$&2$!(<4$64"2!&"<$&"C4>5!2&4"$4"$'&B'/<2$J1!(&:6!2&4"$

achieved and on individual actual competences, that are considered better measures of individual human capital

(Hanushek and Kimko, 2000; Hanushek and Woessmann, 2010). Fourth, our measure of years of schooling is

directly derived from microdata and therefore based on people’s answers and not computed by imputation starting

from the information on educational attainment that typically only distinguishes six or less categories (see Barro

!"#$%//$)*+,+--=$0&"!((?8$41>$#!2!</2$&<$2'/$:><2$4"/$24$@>4;&#/$&"C4>5!2&4"$"42$4"(?$4"$/#16!2&4"!($(/;/(8$912$!(<4$

on educational distribution, using a wide range of synthetic indices.

The aim of this paper is to explain the procedures implemented to generate the set of indices, describe the data

sources, discuss some methodological issues, and check the consistency of the measures across different surveys.

In particular, section 2 describes the original measures of education used to create our statistics, and section 3

explains how we aggregate the individual data and mentions some selection rules adopted to increase the homoge­

neity of the sample. The four data sources are described in section 4 that also discusses methodological issues in

each survey. Section 5 presents the entire set of variables included in our datasets. The consistency of our measures

across surveys is analysed and commented in section 6.

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A!New!Dataset!of!Educational!Inequality

2.! Measures!of!educational!attainment

K/!<1>&"B$/#16!2&4"$&"$!"$&"2/>"!2&4"!($64"2/L2$&<$"42$<2>!&B'2C4>E!>#8$#1/$24$2'/$#&C:61(2$645@!>!9&(&2?$4C$

educational data across countries. We shall use three widely used measures of education, each one presenting ad­

vantages and drawbacks.

First, we use an indicator of the duration of formal schooling, measured by the number of years of educa­

tion. The main advantage of this variable is that years of schooling can be easily computed and compared across

countries. On the other side, this measure refers only to the “quantity” of education, disregarding its effectiveness,

both in terms of results and in terms of different careers and achievements. In other words, focusing on years of

education does not allow to discern general/academic from vocational secondary schools, it does not consider the

/CC/62&;/$!22!&"5/"2$4C$!"?$6/>2&:6!2/M#&@(45!M#/B>//$!"#$&2$5&<(/!#&"B(?$!6641"2<$C4>$?/!>$>/@/2&2&4"<=$N'/>/C4>/8$

one has to keep in mind that the same years of schooling may entail different amount of learning and heterogene­

ous quality in different countries. Because of these reasons, such a measure is intuitively rather bad in assessing the

J1!(&:6!2&4"$!6'&/;/#8$912$&2$&<$<1&2!9(/$24$645@12/$&"/J1!(&2?$&"#&6/<=$A"#//#8$/;/"$&C$&2$&<$"42$@>4@/>(?$64"2&"141<8$

it spans from 0 to 25­30 years, depending on highest level educational institutions, with an acceptable level of

variability.

N'/$</64"#$5/!<1>/$&<$9!</#$4"$!22!&"5/"2$(/;/(<8$6!@21>&"B$2'/$'&B'/<2$J1!(&:6!2&4"$!6'&/;/#=$N'/$!#;!"2!B/$

of this variable with respect to years of education is that it accounts for different duration of analogous school

cycles. Moreover the use of a categorical indicator also allows to specify the type of education completed (i.e.

academic vs. vocational tracks, etc.). On the other hand, a drawback of this measure is that it is a discrete catego­

>&O!2&4"$2'!2$#&<>/B!>#<$9?$#/:"&2&4"$&"2/>5/#&!2/$6!</<8$<16'$!<$#>4@412<$4>$@!>2&!($!22/"#!"6/$!"#$C1>2'/>54>/8$

being a categorical variable, it performs poorly in generating aggregation statistics measuring inequality. Another

&5@4>2!"2$#>!E9!6P$4C$2'&<$5/!<1>/$&"$6>4<<H641"2>?$!"!(?</<$&<$2'!2$(/;/(<8$2?@/<$!"#$#1>!2&4"$4C$<@/6&:6$/#1­

cational programmes depend on the institutional structure of educational systems and, given the high degree of

#&CC/>/"2&!2&4"$4C$/#16!2&4"!($<?<2/5<$!6>4<<$641"2>&/<$!"#$4;/>$2&5/8$&2$&<$#&C:61(2$24$64"<2>162$!$6(!<<&:6!2&4"$4C$

/#16!2&4"!($J1!(&:6!2&4"$2'!2$&<$;!(&#$!"#$645@!>!9(/$&"2/>"!2&4"!((?=

A common internationally harmonised measure of educational attainment is the International Standard Clas­

<&:6!2&4"$4C$Q#16!2&4"$)ISCED) developed by UNESCO6 in 1976 and then revised in 19977 that aims at ranking and

classifying individuals according to the maximum level of educational attained. ISCED should make it possible to

7$ R"&2/#$S!2&4"<$Q#16!2&4"!(8$T6&/"2&:6$!"#$U1(21>!($V>B!"&<!2&4"7 See UNESCO (2006); OECD (1999)

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Elena!Meschi!and!Francesco!Scervini

readily draw a comparable picture of education distribution across countries, independently of the duration of

cycles, the length of compulsory schooling, and the heterogeneous structure of vocational and academic tracks. In

@!>2&61(!>8$2'/$6(!<<&:6!2&4"$#&<2&"B1&<'/<$</;/"$(/;/(<$4C$/#16!2&4"$>!"B&"B$C>45$@>/H@>&5!>?$/#16!2&4"$24$</64"#$

stage of tertiary education. Mapping tables to link national educational programmes to these categories are avail­

able in OECD (1999), and EUROSTAT$)*++G-=$W4E/;/>8$!(2'41B'$2'/$&"2/>"!2&4"!($<2!"#!>#<$!>/$6(/!>(?$#/:"/#8$&2$&<$

sometimes problematic for countries to integrate these standards with the national systems. In fact, as pointed out

by Eurydice studies88$"42$!(($<6'44($<?<2/5<$:"#$1"&;46!($64>>/<@4"#/"6/$24$ISCED: among European countries, for

instance, many Nordic and Eastern countries9 do not distinguish institutionally between primary and lower sec­

ondary levels, so that there is no distinction between ISCED level 1 and ISCED$(/;/($*=$A"$42'/>$641"2>&/<$&2$&<$#&C:61(2$

to differentiate between ISCED$F$!"#$X$9/6!1</$2'/$6/>2&:6!2/<$!E!>#/#$!>/$2'/$<!5/8$!"#$Y1<2$2'/$@!2'E!?$2!P/"$

differs; the distinction between ISCED 4 and ISCED 5 is also not straightforward in countries with a unitary university

structure that does not separate vocational and academic/professional tertiary studies (see Schneider (2007, 2009,

2010) for a detailed discussion of these problems). Moreover, information on ISCED levels are not directly asked to

&"#&;&#1!(<$E'/"$64((/62&"B$#!2!$<1>;/?<8$912$2'/?$!>/$#/>&;/#$C>45$2'/$!"<E/><$2'/?$@>4;&#/$24$641"2>?H<@/6&:6$

questions. It may happen, therefore, that national questionnaires include less than 7 categories for educational at­

tainment (this is the case, for instance, in most of the waves of ISSP), or that they do not separate vocational from

academic schools. Furthermore, even if national questionnaires are detailed enough, it is still possible to observe

mistakes in the assignment of national educational programmes to ISCED categories. In fact, Schneider (2007, 2010)

studied the implementation of ISCED in ESS and showed that, in many cases, the national teams did not follow the

#/:"&2&4"<$/<2!9(&<'/#$9?$UNESCO and OECD$!"#$#&CC/>/"2$6(!<<&:6!2&4"$#/6&<&4"<$E/>/$2!P/"$E&2'$>/<@/62$24$<&5&(!>$

educational programmes in different countries

A"$4>#/>$24$>/#16/$5/!<1>/5/"2$/>>4>$#1/$#4$&5@>/6&</$64#&"B8$E/$6>/!2/$!$<&5@(&:/#$;/><&4"$4C$ISCED, that

eliminates some controversial distinctions and includes four category only (ISCED4 hereafter). Table 1 illustrates the

conversion of the original ISCED categories into our variable ISCED4.

8 www.eurydice.org9 Bulgaria, Czech Republic, Denmark, Estonia, Latvia, Hungary, Slovenia, Slovak Republic, Finland, Sweden, Iceland, Norway, Turkey

N!9(/$,Z$VC:6&!($ISCED 6(!<<&:6!2&4"$!"#$64>>/<@4"#/"6/$24$!12'4><[$<&5@(&:/#$;/><&4"

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A!New!Dataset!of!Educational!Inequality

Table!1:!Official!isced!classification!and!correspondence!to!authors’!simplified!versionISCED!classification Simplified!ISCED4!classification

Level Stage!of!education Level Stage!of!education

0 Pre-primary1 Primary!or!below

1 Primary!or!first!stage!of!basic!education

2Lower!secondary

2 Lower!secondaryor!second!stage!of!basic!education

3 (Upper)!secondary3 (Upper)!secondary

4 Post-secondary!non-tertiary

5 First!stage!of!tertiary4 Any!tertiary

6 Second!stage!of!tertiary

The third measure of education consists of achievement data measuring people’s actual competences. We

gather this information from IALS that provides detailed assessment of the literacy skills of respondents (see section

4.3 for a description of the tests). The main advantage of achievement data is that by testing what people actually

know, they measure effective skills and are thus related to both the quantity and quality of schooling. Another

!#;!"2!B/$ 4C$ 2'&<$5/!<1>/$ &<$ 2'!2$ 2'/</$ 2/<2<$E/>/$ <@/6&:6!((?$ #/<&B"/#$ C4>$ 6>4<<H641"2>?$ 645@!>&<4"$ !"#$ !>/$

therefore readily comparable across country. Moreover, test scores are continuous­like variables and are therefore

suitable to create inequality indices. On the other hand, skills are not stable over the life cycle and are likely to be

affected by many factors other than school.

Unfortunately, not all the measures are available and reliable in all the data sources used in the paper. Section

4 explains and discusses which variables were taken from the different surveys.

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A!New!Dataset!of!Educational!Inequality

3.! Data!aggregation!and!selection!rules

After having selected variables on individual education from the four surveys (see section 4 for details), we

aggregate the data by countries and birth cohorts and generate the synthetic indices of educational attainment and

inequality described in the next section. We construct a pseudo panel, exploiting cohorts of birth as time dimen­

sion. Since education occurs mainly in early stage of life, formal education is unchanged over the life cycle of

every individual out of the schooling system. The more immediate consequence of this observation is that yearly

variation in statistics on education are mainly due to the difference between the very low fraction of individuals

who exit the education system in the meanwhile and the even lower fraction of individuals who eventually died.

Therefore observing the education pattern over cohorts is much more variable and informative. Another advantage

of focusing on cohorts rather than survey years is that we can pull the different surveys’ waves together, thus in­

6>/!<&"B$2'/$<!5@(/$<&O/$C4>$/!6'$641"2>?=$A"$@!>2&61(!>$E/$6>/!2/$,F$64'4>2<$!2$GH?/!><$&"2/>;!(<=$N'/$:><2$64'4>2$

is made of individuals born between 1920 and 1924, the second between 1925 and 1929 and so on until the last

cohort made of people born between 1980 and 1984 (see table 4).

In order to generate a meaningful and comparable set of indicators, we put some effort in selecting the most

<1&2!9(/$49</>;!2&4"<8$9?$2>&55&"B$2'/$<!5@(/$4;/>$2E4$#&5/"<&4"<$&"$!(($2'/$64"<&#/>/#$#!2!</2<Z$:><2$4C$!((8$E/$

exclude all individuals aged less than 25. The rationale behind this choice is to exclude individuals who did not

complete their study at the time of the survey. This is a source of great disturbances, since demographic trends

could heavily affect the results as far as the share of young individuals still enrolled in formal schooling lowers

2'/$(/;/($4C$/#16!2&4"$!"#$&"6>/!</<$&2<$#&<@/><&4"=$\/$6'44</$*G$?/!><$!<$!$2'>/<'4(#$!<$2'&<$&<$2'/$<2!"#!>#$#/:"&­

2&4"$4C$2'/$9/B&""&"B$4C$!#1(2$!B/$!664>#&"B$24$54<2$4C$2'/$(&2/>!21>/$&"$2'/$:/(#$)<//$C4>$&"<2!"6/$I!>>4$!"#$%//$

(2010)). Second, we drop individuals reporting an amount of education over 30 years, assuming that it is the high­

est reasonable duration of a complete educational career, starting at 5­6 years of age and ending with a Ph.D., and

2'!2$!##&2&4"!($?/!><$4C$/#16!2&4"$#4$"42$>/D/62$>/!($&5@>4;/5/"2<$912$/&2'/>$5&<>/@4>2/#$#!2!$4>$>/@/!2/#$?/!><=$

In principle, there is a window of individuals older than 25 and with less than 30 years of education who are in the

sample even if still enrolled in tertiary education. Such individuals should in principle be excluded, but since it is

not possible to discern whether they are still in education, we are forced to include them in our sample. However,

2'/$<'!>/$4C$<16'$&"#&;&#1!(<$&<$<4$(&22(/$2'!2$&2$&<$"42$/L@/62/#$24$<&B"&:6!"2(?$!CC/62$2'/$>/<1(2<=

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Elena!Meschi!and!Francesco!Scervini

Finally, we keep only the cells (country­cohort) with more than 50 observations. Calculating summary sta­

tistics based on small sample sizes may in fact lead to imprecise measures. Therefore dropping the cells with less

that 50 observations should reduce the measurement error. Moreover, we report the number of observations for

each country and cohort, in order to allow the weighting of the aggregate measures according to the sample size

in each cell.

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A!New!Dataset!of!Educational!Inequality

4.! Data!sources

4.1.! European!Social!Survey!(ESS)

The European Social Survey (ESS) is biennial survey that covers over 30 mostly European countries and pro­

vides detailed information on individuals attitudes, beliefs, and behaviour collected from nationally representative

population samples.10$A2$64"<&<2<$4C$>/@/!2/#$6>4<<H</62&4"<$&"&2&!2/#$&"$*++*M*++F$):><2$>41"#-$!"#$2'/"$6!>>&/#$

out in 2004/2005 (2nd round), 2006/2007 (3rd round) and 2008/2009 (4th round). The survey mainly focuses on

people’s attitudes and values, but it also contains several variables capturing the social background of respondents

as well as their partners and parents. As far as education is concerned, the ESS includes three measures of educa­

2&4"!($!22!&"5/"2Z$!621!($)C1((H2&5/$/J1&;!(/"2-$?/!><$4C$/#16!2&4"8$'&B'/<2$(/;/($4C$/#16!2&4"$&"$!$641"2>?H<@/6&:6$

format, and highest level of education in the internationally harmonised format (ISCED) derived from the national

questions. From ESS, we will thus take the two variables on years of schooling and ISCED levels. Interestingly, since

ESS$ @>4;&#/<$ /#16!2&4"!($ !22!&"5/"2$ 641"2>?H<@/6&:6$ C4>5!28$E/$E/>/$ !9(/$ 24$ 64>>/62$ C4>$5&<6(!<<&:6!2&4"<$ 4C$

641"2>?H<@/6&:6$;!>&!9(/<$&"24$ISCED, using the guidelines provided in Eurydice, OECD and following suggestions

in Schneider (2007, 2009, 2010).

4.2.! European!Union!Statistics!on!Income!and!Living!Conditions!(EU-SILC)

The European Union Statistics on Income and Living Conditions (EU­SILC)11 is a collection of timely and com­

parable multidimensional microdata covering EU countries plus Iceland and Norway.

EU­SILC$ &<$ !$ @>4Y/62$ #/;/(4@/#$ 9?$ EUROSTAT, run yearly since 2004 and including both cross­section

and longitudinal surveys. Since we use birth cohorts as time dimension and since education is time­invar­

iant, we are interested only in the cross­sectional element. Unfortunately, opposite to other surveys, for

EU­SILC we cannot pull all the waves together, because even cross­sectional data contain a longitudinal di­

5/"<&4"8$ !"#$ &2$ &<$ "42$ @4<<&9(/$ 24$ #/2/62$ >/@/!2/#$ 49</>;!2&4"<$ &"$ #&CC/>/"2$ 6>4<<H</62&4"$ :(/<=$ N'&<$ 5/!"<$

that merging different waves would result in including some individuals up to four times. Therefore we

'!#$ 24$ </(/62$ 4"(?$ 4"/$ E!;/$ !"#8$ !54"B$ 2'/$ :;/$ !;!&(!9(/$ 6>4<<H</62&4"<8$ E/$ 6'4</$ 2'/$ *++]$ E!;/8$ 9/­

10 ESS$&<$!$@>4Y/62$<2!>2/#$&"$*++,8$#&>/62/#$9?$!$64"<4>2&15$4C$</;/"$!6!#/5&6$&"<2&212&4"<$!"#$C1"#/#$9?$N'/$Q1>4@/!"$U455&<<&4"8$N'/$European Science Foundation and National academic funding bodies (see http://www.europeansocialsurvey.org).

11 http://epp.EUROSTAT.ec.europa.eu/portal/page/portal/microdata/eu_silc

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Elena!Meschi!and!Francesco!Scervini

cause it includes the largest number of countries and observations, and it is also the most recent available.12

The main advantages of EU­SILC consist in the wide country coverage (all eu­27 countries (plus Iceland, Norway

and Switzerland) and the large sample size within each country. The number of individuals in each country is in

fact substantially higher than that in IALS and ESS and similar to that in ISSP, which is collected using repeated waves

(see section 4.4). Therefore in EU­SILC, no cohorts are excluded either because of the low number of observations

4>$9/6!1</$4C$64"<&<2/"6?$&<<1/<=$W4E/;/>8$2'/>/$&<$!(<4$!$5!Y4>$<'4>2645&"B$&"$2'/$#!2!Z$2'/>/$&<$"4$&"C4>5!2&4"$

on years of schooling, so that we can only compute statistics on the highest level of education attained.

4.3.! International!Adult!Literacy!Survey!(IALS)

The International Adult Literacy Survey (IALS) is a survey collecting information on adult literacy in various

countries, with the aim of comparing literacy levels across countries. The survey was coordinated by Statistics

Canada and it was implemented in different years ­ 1994, 1996, 1998 ­ for different countries using a common

questionnaire. In 1994, nine countries (Canada,13 France,14 Germany, Ireland, Netherlands, Poland, Sweden, Swit­

zerland ­ German and French­speaking regions ­ and United States) were surveyed. Five additional countries

(Australia,15 Flemish Belgium, Great Britain, New Zealand and Northern Ireland) were included in 1996; and other

countries (Chile, Czech Republic, Denmark, Finland, Hungary, Italy, Norway, Slovenia and the Italian­speaking

>/B&4"$4C$TE&2O/>(!"#-$@!>2&6&@!2/#$&"$2'/$2'&>#$>41"#$4C$#!2!$64((/62&4"$&"$,^^]=$V1>$:"!($IALS sample includes

16 countries, namely Flemish Belgium, Czech Republic, Denmark, Finland, Germany, Hungary, Ireland, Italy,

Netherlands, Norway, Poland, Slovenia, Sweden, Switzerland, United Kingdom and United States. Since data

were collected in late 90s, the youngest cohort included in our dataset is the 1970­1974 cohort, available for ten

641"2>&/<$4"(?=$K4>/4;/>8$9/6!1</$4C$2'/$<5!(($<!5@(/$<&O/$C4>$54<2$641"2>&/<8$2'/$:><2$2E4$64'4>2<$),^*+H,^*X$

and 1925­1929) include only Netherlands and Sweden.

The central element of the survey is the direct assessment of the literacy skills of respondents, but the back­

ground questionnaire also includes several information on individual socio­demographic characteristics. Regard­

ing education, the survey contains the standard question on the number of years of formal education completed and

the highest level attained. We can therefore use these questions to create all the indices described above.

12 As in November 2010, data relative to 2008 are available for all countries apart from France, for which we used data in EU­SILC 2007.13 Canada is not included in our dataset since there are no informations available on the year of birth, which makes it impossible to generate

cohorts.14 France decided to withdraw from the study in November 1995, citing concerns over comparability and it is therefore not included in the

dataset.15 Data for Australia are not included in the main IALS$#!2!</28$!"#$!>/$!;!&(!9(/$4"(?$2'>41B'$2'/$_1<2>!(&!"$I1>/!1$4C$T2!2&<2&6<8$C4>$64":­

dentiality reasons.

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A!New!Dataset!of!Educational!Inequality

As measure of individual competences we use the results obtained in different tests. In particular, IALS contains

three tests, each one measuring a particular dimension of literacy: prose, document, and quantitative. Prose literacy

&<$#/:"/#$!<$2'/$P"4E(/#B/$!"#$<P&((<$"//#/#$24$1"#/><2!"#$!"#$1</$&"C4>5!2&4"$C>45$2/L2<$&"6(1#&"B$/#&24>&!(<8$

"/E<$<24>&/<8$@4/5<8$!"#$:62&4"=$34615/"2$ (&2/>!6?$5/!<1>/<$ 2'/$P"4E(/#B/$!"#$<P&((<$ >/J1&>/#$ 24$ (46!2/$!"#$

1</$&"C4>5!2&4"$64"2!&"/#$&"$;!>&41<$C4>5!2<8$&"6(1#&"B$Y49$!@@(&6!2&4"<8$@!?>4(($C4>5<8$2>!"<@4>2!2&4"$<6'/#1(/<8$

5!@<8$2!9(/<8$!"#$B>!@'&6<=$`1!"2&2!2&;/$(&2/>!6?$&<$#/:"/#$!<$2'/$P"4E(/#B/$!"#$<P&((<$>/J1&>/#$24$!@@(?$!>&2'5/2&6$

operations (see IALS User guide for further details). The results of the tests are scaled in the range of 0­500 and our

measure of individual “skills” is calculated by averaging the scores obtained in the three tests.

4.4.! International!Social!Survey!Programme!(ISSP)

ISSP16 is a continuing annual programme of cross­national collaboration on surveys covering a wide range of

topics. Data collection is annual, ranging from 1985 to 2008 (last wave available in late 2010), covering 26 coun­

tries, most of which are surveyed only in a subset of years (only United Kingdom and United States are available

for the whole period), for a total of 388 datasets.

Even if ISSP$#4/<$"42$C461<$<@/6&:6!((?$4"$/#16!2&4"$!"#$2'/$5!&"$24@&6$6'!"B/<$/;/>?$?/!>8$&2<$6'!>!62/>&<2&6<$

make it very attractive for analysing in details the evolution of average years of schooling: data on education are

obtained by the personal characteristics section, which is included in every survey and contains information on two

of our three dimensions, namely years of education and attainment. However, only the former is exploitable, since

&2$&<$"42$<19Y/62$24$>/64#&"B$4>$>/<6!(&"B$!"#$&<$2'/>/C4>/$!9<4(12/(?$645@!>!9(/$942'$6>4<<H641"2>?$!"#$4;/>$2&5/=$

Opposite, attainment is virtually impossible to be analysed, since the coding changed several times both across

countries and over time, making the harmonisation very challenging, since it would require comparing answers

from almost 400 different datasets. Since ISSP is not coordinated by European Union agencies, the survey also

includes non­European countries, such as Japan, Australia, Canada.

A careful analysis of the relevant variable (Years of education) across surveys let emerge many issues that have

been faced in order to generate a reliable and consistent set of indicators. First of all, it frequently happened that

two next surveys were submitted together to the same sample of individuals. In this case, in order not to replicate

observations, it was necessary to drop one of the two surveys. For instance, the 2005 survey “Work Orientation III”

was submitted in Bulgaria together with the 2004 survey “Citizenship” to the same sample of 1,121 individuals. In

this case, therefore, one of the two samples were excluded, in particular the one relative to 2004, since data have

16 http://www.issp.org/index.php

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Elena!Meschi!and!Francesco!Scervini

been collected in July 2005 (by the way, since we focus on the cohort dimension, the exact time of data collection

is not relevant). This procedure led to the exclusion of 31 datasets.17

Second, datasets were compared across waves, in order to detect possible data inconsistencies. Over 357 data­

sets remaining, we detected evident data inconsistency only for France 1999, in which the average level of years

of education was 6 years higher than both previous and subsequent waves, due to some kind of measurement error

(years of education takes an unrealistic minimum value of 10). Moreover, we also exclude datasets for Bulgaria,

Canada, Germany and Spain in 1999, since data on years of education were missing for all individuals. In addition,

we also exclude all observations for United Kingdom and United States because data were unfortunately bottom­

and top­coded (8­20 years for UK, 0­20 years for US), making the comparison to other countries impossible.

Aggregation of all surveys generates a single dataset including more than 300,000 observations, that are in

turn partitioned over countries and cohorts, according to the general criteria. The only cells with less than 50 indi­

;&#1!(<$!>/$0&"(!"#$&"$2'/$:><2$64'4>28$A2!(?$&"$2'/$2E4$?41"B/<2$64'4>2<$!"#$U!"!#!$!"#$W1"B!>?$&"$2'/$(!<2$4"/=

4.5.! Summary

Table 2 summarises the measures of education chosen from the four surveys. While the variable on years of

education is available and reliable in ESS, IALS and ISSP, 2'/$&"C4>5!2&4"$4"$2'/$'&B'/<2$J1!(&:6!2&4"$!6'&/;/#$

is only available in ESS, IALS and EU­SILC. Finally, competences are only included in IALS, which was specially

designed to test adults’ skills.

Table!2:!Summary!of!measures!of!education!in!the!four!surveysESS EU-SILC IALS ISSP

Years!of!education x x x.

Qualifications x x x

Competences x

Overall, we have information on 31 countries, most of which are European and covered in all the surveys, as

reported in Table 3. Extra­European countries are available in IALS (e.g United States) or ISSP (e.g. Japan and Aus­

tralia only).18 However, the number of countries covered in each survey is not constant across cohorts. For some

641"2>&/<8$2'/$"159/>$4C$49</>;!2&4"<$C4>$<45/$64'4>2<$)2?@&6!((?$2'/$:><2$4>$2'/$(!<2$4"/<-$&<$(4E/>$2'!"$G+$!"#$E/$

decided to exclude these cells in order to avoid to calculate imprecise statistics based on small sample sizes. Table

4 reports the number of countries covered in each survey for the different cohorts.

17 Australia 1993; Austria 1987, 2001, 2003, 2007, Bulgaria 1996, 1998, 2004; Canada 1998, 2000, 2003, 2005, Ireland 1990, 1995, 2004, 2005, 2007; Italy 1990; Latvia 2002, 2006; Netherland 2003; Poland 2003; Portugal 2003, 2005; Slovak Republic 2007; Slovenia 1993, 1999, 2001; Switzerland 1996, 2004; United States 2005.

18 Given the relative homogeneity of education data across surveys, we could have potentially merged observations from different surveys and calculated a unique summary statistics. However, since the four surveys contain different individual weights, it would have been #&C:61(2$24$645@12/$!BB>/B!2/$5/!<1>/<$>/@>/</"2!2&;/$4C$2'/$>/!($@4@1(!2&4"$!"#$E/$2'/>/C4>/$#/6&#/#$24$P//@$2'/$#!2!</2<$</@!>!2/=

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A!New!Dataset!of!Educational!Inequality

Table!3:!Countries!covered!by!different!surveysCountries ESS EU-SILC IALS ISSP Countries ESS EU-SILC IALS ISSP

Australia x Japan xAustria x x x Latvia x x xBelgium x x Lithuania xBelgium!(Flanders) x x Luxembourg x xBulgaria x x x Netherlands x x x xCanada x Norway x x x xCzech!Republic x x x x Poland x x x xDenmark x x x x Portugal x x xEstonia x x Romania x xFinland x x x x Slovak!Republic x x xFrance x x x Slovenia x x x xGermany x x x x Spain x x xGreece x x Sweden x x x xHungary x x x x Switzerland x x xIreland x x x x United!Kingdom x x xItaly x x x x United!States x

Table!4:!Number!of!countries!covered!by!surveys!and!cohortsCohorts ESS EU-SILC IALS ISSP

20-24 22 2 2325-29 26 2 2430-34 26 25 16 2435-39 26 25 16 2440-44 26 25 16 2445-49 26 25 16 2450-54 26 25 16 2455-59 26 25 16 2460-64 26 25 16 2465-69 26 25 16 2470-74 26 25 10 2475-79 26 25 2380-84 24 25 21

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Page!!!22

Elena!Meschi!and!Francesco!Scervini

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A!New!Dataset!of!Educational!Inequality

5.! Variables

For each country and cohort, we calculate different measures of educational level and several indices of dis­

persion. All the statistics presented in the dataset have been computed using survey weights, which allow to make

inference on the whole population. Educational levels are measured using the following statistics:

8 Weighted average:

Aggregation of all surveys generates a single dataset including more than 300,000observations, that are in turn partitioned over countries and cohorts, according to thegeneral criteria. The only cells with less than 50 individuals are Finland in the firstcohort, Italy in the two youngest cohorts and Canada and Hungary in the last one.

4.5 SummaryTable 2 summarises the measures of education chosen from the four surveys. Whilethe variable on years of education is available and reliable in ess, ials and issp, theinformation on the highest qualification achieved is only available in ess, ials and eu-silc. Finally, competences are only included in ials, which was specially designed totest adults’ skills.

ess eu-silc ials isspYears of education x x xQualifications x x xCompetences x

Table 2: Summary of measures of education in the four surveys

Overall, we have information on 31 countries, most of which are European and coveredin all the surveys, as reported in Table 3. Extra-European countries are available in ials(e.g United States) or issp (e.g. Japan and Australia only).18

However, the number of countries covered in each survey is not constant across co-horts. For some countries, the number of observations for some cohorts (typically thefirst or the last ones) is lower than 50 and we decided to exclude these cells in order toavoid to calculate imprecise statistics based on small sample sizes. Table 4 reports thenumber of countries covered in each survey for the di!erent cohorts.

5 VariablesFor each country and cohort, we calculate di!erent measures of educational level and sev-eral indices of dispersion. All the statistics presented in the dataset have been computedusing survey weights, which allow to make inference on the whole population.

Educational levels are measured using the following statistics:

• Weighted average: µx =!

i xifi!i fi

, where x is either years of education or skills, f isthe weight and i denotes the N individuals in the population;

18Given the relative homogeneity of education data across surveys, we could have potentially mergedobservations from di!erent surveys and calculated a unique summary statistics. However, since the foursurveys contain di!erent individual weights, it would have been di"cult to compute aggregate measuresrepresentative of the real population and we therefore decided to keep the datasets separate.

10

, where

Aggregation of all surveys generates a single dataset including more than 300,000observations, that are in turn partitioned over countries and cohorts, according to thegeneral criteria. The only cells with less than 50 individuals are Finland in the firstcohort, Italy in the two youngest cohorts and Canada and Hungary in the last one.

4.5 SummaryTable 2 summarises the measures of education chosen from the four surveys. Whilethe variable on years of education is available and reliable in ess, ials and issp, theinformation on the highest qualification achieved is only available in ess, ials and eu-silc. Finally, competences are only included in ials, which was specially designed totest adults’ skills.

ess eu-silc ials isspYears of education x x xQualifications x x xCompetences x

Table 2: Summary of measures of education in the four surveys

Overall, we have information on 31 countries, most of which are European and coveredin all the surveys, as reported in Table 3. Extra-European countries are available in ials(e.g United States) or issp (e.g. Japan and Australia only).18

However, the number of countries covered in each survey is not constant across co-horts. For some countries, the number of observations for some cohorts (typically thefirst or the last ones) is lower than 50 and we decided to exclude these cells in order toavoid to calculate imprecise statistics based on small sample sizes. Table 4 reports thenumber of countries covered in each survey for the di!erent cohorts.

5 VariablesFor each country and cohort, we calculate di!erent measures of educational level and sev-eral indices of dispersion. All the statistics presented in the dataset have been computedusing survey weights, which allow to make inference on the whole population.

Educational levels are measured using the following statistics:

• Weighted average: µx =!

i xifi!i fi

, where x is either years of education or skills, f isthe weight and i denotes the N individuals in the population;

18Given the relative homogeneity of education data across surveys, we could have potentially mergedobservations from di!erent surveys and calculated a unique summary statistics. However, since the foursurveys contain di!erent individual weights, it would have been di"cult to compute aggregate measuresrepresentative of the real population and we therefore decided to keep the datasets separate.

10

is either years of education or skills,

Aggregation of all surveys generates a single dataset including more than 300,000observations, that are in turn partitioned over countries and cohorts, according to thegeneral criteria. The only cells with less than 50 individuals are Finland in the firstcohort, Italy in the two youngest cohorts and Canada and Hungary in the last one.

4.5 SummaryTable 2 summarises the measures of education chosen from the four surveys. Whilethe variable on years of education is available and reliable in ess, ials and issp, theinformation on the highest qualification achieved is only available in ess, ials and eu-silc. Finally, competences are only included in ials, which was specially designed totest adults’ skills.

ess eu-silc ials isspYears of education x x xQualifications x x xCompetences x

Table 2: Summary of measures of education in the four surveys

Overall, we have information on 31 countries, most of which are European and coveredin all the surveys, as reported in Table 3. Extra-European countries are available in ials(e.g United States) or issp (e.g. Japan and Australia only).18

However, the number of countries covered in each survey is not constant across co-horts. For some countries, the number of observations for some cohorts (typically thefirst or the last ones) is lower than 50 and we decided to exclude these cells in order toavoid to calculate imprecise statistics based on small sample sizes. Table 4 reports thenumber of countries covered in each survey for the di!erent cohorts.

5 VariablesFor each country and cohort, we calculate di!erent measures of educational level and sev-eral indices of dispersion. All the statistics presented in the dataset have been computedusing survey weights, which allow to make inference on the whole population.

Educational levels are measured using the following statistics:

• Weighted average: µx =!

i xifi!i fi

, where x is either years of education or skills, f isthe weight and i denotes the N individuals in the population;

18Given the relative homogeneity of education data across surveys, we could have potentially mergedobservations from di!erent surveys and calculated a unique summary statistics. However, since the foursurveys contain di!erent individual weights, it would have been di"cult to compute aggregate measuresrepresentative of the real population and we therefore decided to keep the datasets separate.

10

is the weight and

Aggregation of all surveys generates a single dataset including more than 300,000observations, that are in turn partitioned over countries and cohorts, according to thegeneral criteria. The only cells with less than 50 individuals are Finland in the firstcohort, Italy in the two youngest cohorts and Canada and Hungary in the last one.

4.5 SummaryTable 2 summarises the measures of education chosen from the four surveys. Whilethe variable on years of education is available and reliable in ess, ials and issp, theinformation on the highest qualification achieved is only available in ess, ials and eu-silc. Finally, competences are only included in ials, which was specially designed totest adults’ skills.

ess eu-silc ials isspYears of education x x xQualifications x x xCompetences x

Table 2: Summary of measures of education in the four surveys

Overall, we have information on 31 countries, most of which are European and coveredin all the surveys, as reported in Table 3. Extra-European countries are available in ials(e.g United States) or issp (e.g. Japan and Australia only).18

However, the number of countries covered in each survey is not constant across co-horts. For some countries, the number of observations for some cohorts (typically thefirst or the last ones) is lower than 50 and we decided to exclude these cells in order toavoid to calculate imprecise statistics based on small sample sizes. Table 4 reports thenumber of countries covered in each survey for the di!erent cohorts.

5 VariablesFor each country and cohort, we calculate di!erent measures of educational level and sev-eral indices of dispersion. All the statistics presented in the dataset have been computedusing survey weights, which allow to make inference on the whole population.

Educational levels are measured using the following statistics:

• Weighted average: µx =!

i xifi!i fi

, where x is either years of education or skills, f isthe weight and i denotes the N individuals in the population;

18Given the relative homogeneity of education data across surveys, we could have potentially mergedobservations from di!erent surveys and calculated a unique summary statistics. However, since the foursurveys contain di!erent individual weights, it would have been di"cult to compute aggregate measuresrepresentative of the real population and we therefore decided to keep the datasets separate.

10

denotes the

Aggregation of all surveys generates a single dataset including more than 300,000observations, that are in turn partitioned over countries and cohorts, according to thegeneral criteria. The only cells with less than 50 individuals are Finland in the firstcohort, Italy in the two youngest cohorts and Canada and Hungary in the last one.

4.5 SummaryTable 2 summarises the measures of education chosen from the four surveys. Whilethe variable on years of education is available and reliable in ess, ials and issp, theinformation on the highest qualification achieved is only available in ess, ials and eu-silc. Finally, competences are only included in ials, which was specially designed totest adults’ skills.

ess eu-silc ials isspYears of education x x xQualifications x x xCompetences x

Table 2: Summary of measures of education in the four surveys

Overall, we have information on 31 countries, most of which are European and coveredin all the surveys, as reported in Table 3. Extra-European countries are available in ials(e.g United States) or issp (e.g. Japan and Australia only).18

However, the number of countries covered in each survey is not constant across co-horts. For some countries, the number of observations for some cohorts (typically thefirst or the last ones) is lower than 50 and we decided to exclude these cells in order toavoid to calculate imprecise statistics based on small sample sizes. Table 4 reports thenumber of countries covered in each survey for the di!erent cohorts.

5 VariablesFor each country and cohort, we calculate di!erent measures of educational level and sev-eral indices of dispersion. All the statistics presented in the dataset have been computedusing survey weights, which allow to make inference on the whole population.

Educational levels are measured using the following statistics:

• Weighted average: µx =!

i xifi!i fi

, where x is either years of education or skills, f isthe weight and i denotes the N individuals in the population;

18Given the relative homogeneity of education data across surveys, we could have potentially mergedobservations from di!erent surveys and calculated a unique summary statistics. However, since the foursurveys contain di!erent individual weights, it would have been di"cult to compute aggregate measuresrepresentative of the real population and we therefore decided to keep the datasets separate.

10

individuals in the population;

8 Percentages P of individuals who completed at least each ISCED level: • Percentages of individuals who completed at least each isced level: %ISCEDk =!ni=1(1|ISCEDi!k)

n , k = 1, 2, 3, 4.

Educational inequality is measured computing the following set of dispersion indicesbased on years of education and competences. These measures have been frequently usedfor income and consumption, but very few studies have calculated them on education(see for example Checchi (2004) and Thomas et al. (2001)). One point that is worthmentioning is that the cardinality of the two measures (years of education and compe-tences) is not theoretically doubtless (is a child obtaining a score 400 twice as competentas a child obtaining 200? Does a year of university give the same education as a yearof primary school?). However, we disregard this issue here, since there are no generallyagreed means to overcome this problem.

In particular, we derived the following set of inequality measures comparable acrosscountries and over time (see Cowell (2009) for an exhaustive treatment of inequalityindices and their properties):

• Standard deviation !x =!!

i(xifi"µx)2

N"1 , a “standardised” measure of the varianceof a variable;

• Coe!cient of variation: cvx = !xµx

, the standard deviation normalised by the mean;

• Gini index: Gx = 12µ

"i

"j|xi"xj |

N2 , !i, j " N , the most used inequality index in theliterature;

• Generalized entropy family: GE (") = 1"2""

#1N

"i

$xiµ

%"

# 1&, a set of indices

separable and bounded between zero and one. Opposite to the more widely usedGini index, these indices allow to perfectly decompose total inequality in “within”and “between” group inequality. Among all the possible values of ", we choose themore frequently used:

– Theil index, with " = 1: Tx = 1N

"i

$xiµ ln xi

µ

%, introduced by Theil (1967)

and extensively described among others by Conceicao and Ferreira (2000),

– Mean logarithmic deviation (MLD), with " = 0: Tx = 1N

"i

$ln µ

xi

%, analo-

gous to the Theil index, but characterized by a lower sensitivity index;

• Atkinson indices: Ax = 1# 1µ

'1N

"i x

1"#i

( 11!! (# = 0.5, 1, 2), a measure that allows

di"erent sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:

Ax = 1# 1

µ

)1

N

*

i

x1"#i

+ 11!!

=

,-.

-/

1# 1µ

'1N

"i

$xi

(2 if # = 0.5

1#0

ixiµ if # = 1

1# µh

µ if # = 2

where µh is the harmonic mean.

12

= • Percentages of individuals who completed at least each isced level: %ISCEDk =!n

i=1(1|ISCEDi!k)n , k = 1, 2, 3, 4.

Educational inequality is measured computing the following set of dispersion indicesbased on years of education and competences. These measures have been frequently usedfor income and consumption, but very few studies have calculated them on education(see for example Checchi (2004) and Thomas et al. (2001)). One point that is worthmentioning is that the cardinality of the two measures (years of education and compe-tences) is not theoretically doubtless (is a child obtaining a score 400 twice as competentas a child obtaining 200? Does a year of university give the same education as a yearof primary school?). However, we disregard this issue here, since there are no generallyagreed means to overcome this problem.

In particular, we derived the following set of inequality measures comparable acrosscountries and over time (see Cowell (2009) for an exhaustive treatment of inequalityindices and their properties):

• Standard deviation !x =!!

i(xifi"µx)2

N"1 , a “standardised” measure of the varianceof a variable;

• Coe!cient of variation: cvx = !xµx

, the standard deviation normalised by the mean;

• Gini index: Gx = 12µ

"i

"j|xi"xj |

N2 , !i, j " N , the most used inequality index in theliterature;

• Generalized entropy family: GE (") = 1"2""

#1N

"i

$xiµ

%"

# 1&, a set of indices

separable and bounded between zero and one. Opposite to the more widely usedGini index, these indices allow to perfectly decompose total inequality in “within”and “between” group inequality. Among all the possible values of ", we choose themore frequently used:

– Theil index, with " = 1: Tx = 1N

"i

$xiµ ln xi

µ

%, introduced by Theil (1967)

and extensively described among others by Conceicao and Ferreira (2000),

– Mean logarithmic deviation (MLD), with " = 0: Tx = 1N

"i

$ln µ

xi

%, analo-

gous to the Theil index, but characterized by a lower sensitivity index;

• Atkinson indices: Ax = 1# 1µ

'1N

"i x

1"#i

( 11!! (# = 0.5, 1, 2), a measure that allows

di"erent sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:

Ax = 1# 1

µ

)1

N

*

i

x1"#i

+ 11!!

=

,-.

-/

1# 1µ

'1N

"i

$xi

(2 if # = 0.5

1#0

ixiµ if # = 1

1# µh

µ if # = 2

where µh is the harmonic mean.

12

Educational inequality is measured computing the following set of dispersion indices based on years of edu­

cation and competences. These measures have been frequently used for income and consumption, but very few

studies have calculated them on education (see for example Checchi (2004) and Thomas et al. (2001)). One point

that is worth mentioning is that the cardinality of the two measures (years of education and competences) is not

theoretically doubtless (is a child obtaining a score 400 twice as competent as a child obtaining 200? Does a year

of university give the same education as a year of primary school?). However, we disregard this issue here, since

there are no generally agreed means to overcome this problem.

In particular, we derived the following set of inequality measures comparable across countries and over time

(see Cowell (2009) for an exhaustive treatment of inequality indices and their properties):

8 Standard deviation

• Percentages of individuals who completed at least each isced level: %ISCEDk =!ni=1(1|ISCEDi!k)

n , k = 1, 2, 3, 4.

Educational inequality is measured computing the following set of dispersion indicesbased on years of education and competences. These measures have been frequently usedfor income and consumption, but very few studies have calculated them on education(see for example Checchi (2004) and Thomas et al. (2001)). One point that is worthmentioning is that the cardinality of the two measures (years of education and compe-tences) is not theoretically doubtless (is a child obtaining a score 400 twice as competentas a child obtaining 200? Does a year of university give the same education as a yearof primary school?). However, we disregard this issue here, since there are no generallyagreed means to overcome this problem.

In particular, we derived the following set of inequality measures comparable acrosscountries and over time (see Cowell (2009) for an exhaustive treatment of inequalityindices and their properties):

• Standard deviation !x =!!

i(xifi"µx)2

N"1 , a “standardised” measure of the varianceof a variable;

• Coe!cient of variation: cvx = !xµx

, the standard deviation normalised by the mean;

• Gini index: Gx = 12µ

"i

"j|xi"xj |

N2 , !i, j " N , the most used inequality index in theliterature;

• Generalized entropy family: GE (") = 1"2""

#1N

"i

$xiµ

%"

# 1&, a set of indices

separable and bounded between zero and one. Opposite to the more widely usedGini index, these indices allow to perfectly decompose total inequality in “within”and “between” group inequality. Among all the possible values of ", we choose themore frequently used:

– Theil index, with " = 1: Tx = 1N

"i

$xiµ ln xi

µ

%, introduced by Theil (1967)

and extensively described among others by Conceicao and Ferreira (2000),

– Mean logarithmic deviation (MLD), with " = 0: Tx = 1N

"i

$ln µ

xi

%, analo-

gous to the Theil index, but characterized by a lower sensitivity index;

• Atkinson indices: Ax = 1# 1µ

'1N

"i x

1"#i

( 11!! (# = 0.5, 1, 2), a measure that allows

di"erent sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:

Ax = 1# 1

µ

)1

N

*

i

x1"#i

+ 11!!

=

,-.

-/

1# 1µ

'1N

"i

$xi

(2 if # = 0.5

1#0

ixiµ if # = 1

1# µh

µ if # = 2

where µh is the harmonic mean.

12

, a “standardised” measure of the variance of a variable;

8 %-9:*+)9.,$-: $;'<)',)-./$

• Percentages of individuals who completed at least each isced level: %ISCEDk =!ni=1(1|ISCEDi!k)

n , k = 1, 2, 3, 4.

Educational inequality is measured computing the following set of dispersion indicesbased on years of education and competences. These measures have been frequently usedfor income and consumption, but very few studies have calculated them on education(see for example Checchi (2004) and Thomas et al. (2001)). One point that is worthmentioning is that the cardinality of the two measures (years of education and compe-tences) is not theoretically doubtless (is a child obtaining a score 400 twice as competentas a child obtaining 200? Does a year of university give the same education as a yearof primary school?). However, we disregard this issue here, since there are no generallyagreed means to overcome this problem.

In particular, we derived the following set of inequality measures comparable acrosscountries and over time (see Cowell (2009) for an exhaustive treatment of inequalityindices and their properties):

• Standard deviation !x =!!

i(xifi"µx)2

N"1 , a “standardised” measure of the varianceof a variable;

• Coe!cient of variation: cvx = !xµx

, the standard deviation normalised by the mean;

• Gini index: Gx = 12µ

"i

"j|xi"xj |

N2 , !i, j " N , the most used inequality index in theliterature;

• Generalized entropy family: GE (") = 1"2""

#1N

"i

$xiµ

%"

# 1&, a set of indices

separable and bounded between zero and one. Opposite to the more widely usedGini index, these indices allow to perfectly decompose total inequality in “within”and “between” group inequality. Among all the possible values of ", we choose themore frequently used:

– Theil index, with " = 1: Tx = 1N

"i

$xiµ ln xi

µ

%, introduced by Theil (1967)

and extensively described among others by Conceicao and Ferreira (2000),

– Mean logarithmic deviation (MLD), with " = 0: Tx = 1N

"i

$ln µ

xi

%, analo-

gous to the Theil index, but characterized by a lower sensitivity index;

• Atkinson indices: Ax = 1# 1µ

'1N

"i x

1"#i

( 11!! (# = 0.5, 1, 2), a measure that allows

di"erent sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:

Ax = 1# 1

µ

)1

N

*

i

x1"#i

+ 11!!

=

,-.

-/

1# 1µ

'1N

"i

$xi

(2 if # = 0.5

1#0

ixiµ if # = 1

1# µh

µ if # = 2

where µh is the harmonic mean.

12

, the standard deviation normalised by the mean;

8 Gini index:

• Percentages of individuals who completed at least each isced level: %ISCEDk =!ni=1(1|ISCEDi!k)

n , k = 1, 2, 3, 4.

Educational inequality is measured computing the following set of dispersion indicesbased on years of education and competences. These measures have been frequently usedfor income and consumption, but very few studies have calculated them on education(see for example Checchi (2004) and Thomas et al. (2001)). One point that is worthmentioning is that the cardinality of the two measures (years of education and compe-tences) is not theoretically doubtless (is a child obtaining a score 400 twice as competentas a child obtaining 200? Does a year of university give the same education as a yearof primary school?). However, we disregard this issue here, since there are no generallyagreed means to overcome this problem.

In particular, we derived the following set of inequality measures comparable acrosscountries and over time (see Cowell (2009) for an exhaustive treatment of inequalityindices and their properties):

• Standard deviation !x =!!

i(xifi"µx)2

N"1 , a “standardised” measure of the varianceof a variable;

• Coe!cient of variation: cvx = !xµx

, the standard deviation normalised by the mean;

• Gini index: Gx = 12µ

"i

"j|xi"xj |

N2 , !i, j " N , the most used inequality index in theliterature;

• Generalized entropy family: GE (") = 1"2""

#1N

"i

$xiµ

%"

# 1&, a set of indices

separable and bounded between zero and one. Opposite to the more widely usedGini index, these indices allow to perfectly decompose total inequality in “within”and “between” group inequality. Among all the possible values of ", we choose themore frequently used:

– Theil index, with " = 1: Tx = 1N

"i

$xiµ ln xi

µ

%, introduced by Theil (1967)

and extensively described among others by Conceicao and Ferreira (2000),

– Mean logarithmic deviation (MLD), with " = 0: Tx = 1N

"i

$ln µ

xi

%, analo-

gous to the Theil index, but characterized by a lower sensitivity index;

• Atkinson indices: Ax = 1# 1µ

'1N

"i x

1"#i

( 11!! (# = 0.5, 1, 2), a measure that allows

di"erent sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:

Ax = 1# 1

µ

)1

N

*

i

x1"#i

+ 11!!

=

,-.

-/

1# 1µ

'1N

"i

$xi

(2 if # = 0.5

1#0

ixiµ if # = 1

1# µh

µ if # = 2

where µh is the harmonic mean.

12

, the most used inequality index in the literature

8 Generalized entropy family:

• Percentages of individuals who completed at least each isced level: %ISCEDk =!ni=1(1|ISCEDi!k)

n , k = 1, 2, 3, 4.

Educational inequality is measured computing the following set of dispersion indicesbased on years of education and competences. These measures have been frequently usedfor income and consumption, but very few studies have calculated them on education(see for example Checchi (2004) and Thomas et al. (2001)). One point that is worthmentioning is that the cardinality of the two measures (years of education and compe-tences) is not theoretically doubtless (is a child obtaining a score 400 twice as competentas a child obtaining 200? Does a year of university give the same education as a yearof primary school?). However, we disregard this issue here, since there are no generallyagreed means to overcome this problem.

In particular, we derived the following set of inequality measures comparable acrosscountries and over time (see Cowell (2009) for an exhaustive treatment of inequalityindices and their properties):

• Standard deviation !x =!!

i(xifi"µx)2

N"1 , a “standardised” measure of the varianceof a variable;

• Coe!cient of variation: cvx = !xµx

, the standard deviation normalised by the mean;

• Gini index: Gx = 12µ

"i

"j|xi"xj |

N2 , !i, j " N , the most used inequality index in theliterature;

• Generalized entropy family: GE (") = 1"2""

#1N

"i

$xiµ

%"

# 1&, a set of indices

separable and bounded between zero and one. Opposite to the more widely usedGini index, these indices allow to perfectly decompose total inequality in “within”and “between” group inequality. Among all the possible values of ", we choose themore frequently used:

– Theil index, with " = 1: Tx = 1N

"i

$xiµ ln xi

µ

%, introduced by Theil (1967)

and extensively described among others by Conceicao and Ferreira (2000),

– Mean logarithmic deviation (MLD), with " = 0: Tx = 1N

"i

$ln µ

xi

%, analo-

gous to the Theil index, but characterized by a lower sensitivity index;

• Atkinson indices: Ax = 1# 1µ

'1N

"i x

1"#i

( 11!! (# = 0.5, 1, 2), a measure that allows

di"erent sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:

Ax = 1# 1

µ

)1

N

*

i

x1"#i

+ 11!!

=

,-.

-/

1# 1µ

'1N

"i

$xi

(2 if # = 0.5

1#0

ixiµ if # = 1

1# µh

µ if # = 2

where µh is the harmonic mean.

12

, a set of indices separable and

bounded between zero and one. Opposite to the more widely used Gini index, these indices allow to

perfectly decompose total inequality in “within” and “between” group inequality. Among all the

possible values of, we choose the more frequently used:

=$ Theil index, with

• Percentages of individuals who completed at least each isced level: %ISCEDk =!ni=1(1|ISCEDi!k)

n , k = 1, 2, 3, 4.

Educational inequality is measured computing the following set of dispersion indicesbased on years of education and competences. These measures have been frequently usedfor income and consumption, but very few studies have calculated them on education(see for example Checchi (2004) and Thomas et al. (2001)). One point that is worthmentioning is that the cardinality of the two measures (years of education and compe-tences) is not theoretically doubtless (is a child obtaining a score 400 twice as competentas a child obtaining 200? Does a year of university give the same education as a yearof primary school?). However, we disregard this issue here, since there are no generallyagreed means to overcome this problem.

In particular, we derived the following set of inequality measures comparable acrosscountries and over time (see Cowell (2009) for an exhaustive treatment of inequalityindices and their properties):

• Standard deviation !x =!!

i(xifi"µx)2

N"1 , a “standardised” measure of the varianceof a variable;

• Coe!cient of variation: cvx = !xµx

, the standard deviation normalised by the mean;

• Gini index: Gx = 12µ

"i

"j|xi"xj |

N2 , !i, j " N , the most used inequality index in theliterature;

• Generalized entropy family: GE (") = 1"2""

#1N

"i

$xiµ

%"

# 1&, a set of indices

separable and bounded between zero and one. Opposite to the more widely usedGini index, these indices allow to perfectly decompose total inequality in “within”and “between” group inequality. Among all the possible values of ", we choose themore frequently used:

– Theil index, with " = 1: Tx = 1N

"i

$xiµ ln xi

µ

%, introduced by Theil (1967)

and extensively described among others by Conceicao and Ferreira (2000),

– Mean logarithmic deviation (MLD), with " = 0: Tx = 1N

"i

$ln µ

xi

%, analo-

gous to the Theil index, but characterized by a lower sensitivity index;

• Atkinson indices: Ax = 1# 1µ

'1N

"i x

1"#i

( 11!! (# = 0.5, 1, 2), a measure that allows

di"erent sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:

Ax = 1# 1

µ

)1

N

*

i

x1"#i

+ 11!!

=

,-.

-/

1# 1µ

'1N

"i

$xi

(2 if # = 0.5

1#0

ixiµ if # = 1

1# µh

µ if # = 2

where µh is the harmonic mean.

12

, introduced by Theil (1967) and extensively de-

scribed among others by Conceicao and Ferreira (2000),

=$ Mean logarithmic deviation (MLD), with

• Percentages of individuals who completed at least each isced level: %ISCEDk =!ni=1(1|ISCEDi!k)

n , k = 1, 2, 3, 4.

Educational inequality is measured computing the following set of dispersion indicesbased on years of education and competences. These measures have been frequently usedfor income and consumption, but very few studies have calculated them on education(see for example Checchi (2004) and Thomas et al. (2001)). One point that is worthmentioning is that the cardinality of the two measures (years of education and compe-tences) is not theoretically doubtless (is a child obtaining a score 400 twice as competentas a child obtaining 200? Does a year of university give the same education as a yearof primary school?). However, we disregard this issue here, since there are no generallyagreed means to overcome this problem.

In particular, we derived the following set of inequality measures comparable acrosscountries and over time (see Cowell (2009) for an exhaustive treatment of inequalityindices and their properties):

• Standard deviation !x =!!

i(xifi"µx)2

N"1 , a “standardised” measure of the varianceof a variable;

• Coe!cient of variation: cvx = !xµx

, the standard deviation normalised by the mean;

• Gini index: Gx = 12µ

"i

"j|xi"xj |

N2 , !i, j " N , the most used inequality index in theliterature;

• Generalized entropy family: GE (") = 1"2""

#1N

"i

$xiµ

%"

# 1&, a set of indices

separable and bounded between zero and one. Opposite to the more widely usedGini index, these indices allow to perfectly decompose total inequality in “within”and “between” group inequality. Among all the possible values of ", we choose themore frequently used:

– Theil index, with " = 1: Tx = 1N

"i

$xiµ ln xi

µ

%, introduced by Theil (1967)

and extensively described among others by Conceicao and Ferreira (2000),

– Mean logarithmic deviation (MLD), with " = 0: Tx = 1N

"i

$ln µ

xi

%, analo-

gous to the Theil index, but characterized by a lower sensitivity index;

• Atkinson indices: Ax = 1# 1µ

'1N

"i x

1"#i

( 11!! (# = 0.5, 1, 2), a measure that allows

di"erent sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:

Ax = 1# 1

µ

)1

N

*

i

x1"#i

+ 11!!

=

,-.

-/

1# 1µ

'1N

"i

$xi

(2 if # = 0.5

1#0

ixiµ if # = 1

1# µh

µ if # = 2

where µh is the harmonic mean.

12

, analogous to the Theil

index, but characterized by a lower sensitivity index;

Page 24: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!24

Elena!Meschi!and!Francesco!Scervini

8 Atkinson indices:

• Percentages of individuals who completed at least each isced level: %ISCEDk =!ni=1(1|ISCEDi!k)

n , k = 1, 2, 3, 4.

Educational inequality is measured computing the following set of dispersion indicesbased on years of education and competences. These measures have been frequently usedfor income and consumption, but very few studies have calculated them on education(see for example Checchi (2004) and Thomas et al. (2001)). One point that is worthmentioning is that the cardinality of the two measures (years of education and compe-tences) is not theoretically doubtless (is a child obtaining a score 400 twice as competentas a child obtaining 200? Does a year of university give the same education as a yearof primary school?). However, we disregard this issue here, since there are no generallyagreed means to overcome this problem.

In particular, we derived the following set of inequality measures comparable acrosscountries and over time (see Cowell (2009) for an exhaustive treatment of inequalityindices and their properties):

• Standard deviation !x =!!

i(xifi"µx)2

N"1 , a “standardised” measure of the varianceof a variable;

• Coe!cient of variation: cvx = !xµx

, the standard deviation normalised by the mean;

• Gini index: Gx = 12µ

"i

"j|xi"xj |

N2 , !i, j " N , the most used inequality index in theliterature;

• Generalized entropy family: GE (") = 1"2""

#1N

"i

$xiµ

%"

# 1&, a set of indices

separable and bounded between zero and one. Opposite to the more widely usedGini index, these indices allow to perfectly decompose total inequality in “within”and “between” group inequality. Among all the possible values of ", we choose themore frequently used:

– Theil index, with " = 1: Tx = 1N

"i

$xiµ ln xi

µ

%, introduced by Theil (1967)

and extensively described among others by Conceicao and Ferreira (2000),

– Mean logarithmic deviation (MLD), with " = 0: Tx = 1N

"i

$ln µ

xi

%, analo-

gous to the Theil index, but characterized by a lower sensitivity index;

• Atkinson indices: Ax = 1# 1µ

'1N

"i x

1"#i

( 11!! (# = 0.5, 1, 2), a measure that allows

di"erent sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:

Ax = 1# 1

µ

)1

N

*

i

x1"#i

+ 11!!

=

,-.

-/

1# 1µ

'1N

"i

$xi

(2 if # = 0.5

1#0

ixiµ if # = 1

1# µh

µ if # = 2

where µh is the harmonic mean.

12

, a measure that allows different sensi-

,);),)9($,-$,<'.(:9<($,-$,>9$&-?$,')&$-: $,>9$@)(,<)AB,)-.C$D>9$:-<EB&'$'A-;9$()EF&)*9($'($:-&&-?(/

• Percentages of individuals who completed at least each isced level: %ISCEDk =!ni=1(1|ISCEDi!k)

n , k = 1, 2, 3, 4.

Educational inequality is measured computing the following set of dispersion indicesbased on years of education and competences. These measures have been frequently usedfor income and consumption, but very few studies have calculated them on education(see for example Checchi (2004) and Thomas et al. (2001)). One point that is worthmentioning is that the cardinality of the two measures (years of education and compe-tences) is not theoretically doubtless (is a child obtaining a score 400 twice as competentas a child obtaining 200? Does a year of university give the same education as a yearof primary school?). However, we disregard this issue here, since there are no generallyagreed means to overcome this problem.

In particular, we derived the following set of inequality measures comparable acrosscountries and over time (see Cowell (2009) for an exhaustive treatment of inequalityindices and their properties):

• Standard deviation !x =!!

i(xifi"µx)2

N"1 , a “standardised” measure of the varianceof a variable;

• Coe!cient of variation: cvx = !xµx

, the standard deviation normalised by the mean;

• Gini index: Gx = 12µ

"i

"j|xi"xj |

N2 , !i, j " N , the most used inequality index in theliterature;

• Generalized entropy family: GE (") = 1"2""

#1N

"i

$xiµ

%"

# 1&, a set of indices

separable and bounded between zero and one. Opposite to the more widely usedGini index, these indices allow to perfectly decompose total inequality in “within”and “between” group inequality. Among all the possible values of ", we choose themore frequently used:

– Theil index, with " = 1: Tx = 1N

"i

$xiµ ln xi

µ

%, introduced by Theil (1967)

and extensively described among others by Conceicao and Ferreira (2000),

– Mean logarithmic deviation (MLD), with " = 0: Tx = 1N

"i

$ln µ

xi

%, analo-

gous to the Theil index, but characterized by a lower sensitivity index;

• Atkinson indices: Ax = 1# 1µ

'1N

"i x

1"#i

( 11!! (# = 0.5, 1, 2), a measure that allows

di"erent sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:

Ax = 1# 1

µ

)1

N

*

i

x1"#i

+ 11!!

=

,-.

-/

1# 1µ

'1N

"i

$xi

(2 if # = 0.5

1#0

ixiµ if # = 1

1# µh

µ if # = 2

where µh is the harmonic mean.

12

where

• Percentages of individuals who completed at least each isced level: %ISCEDk =!ni=1(1|ISCEDi!k)

n , k = 1, 2, 3, 4.

Educational inequality is measured computing the following set of dispersion indicesbased on years of education and competences. These measures have been frequently usedfor income and consumption, but very few studies have calculated them on education(see for example Checchi (2004) and Thomas et al. (2001)). One point that is worthmentioning is that the cardinality of the two measures (years of education and compe-tences) is not theoretically doubtless (is a child obtaining a score 400 twice as competentas a child obtaining 200? Does a year of university give the same education as a yearof primary school?). However, we disregard this issue here, since there are no generallyagreed means to overcome this problem.

In particular, we derived the following set of inequality measures comparable acrosscountries and over time (see Cowell (2009) for an exhaustive treatment of inequalityindices and their properties):

• Standard deviation !x =!!

i(xifi"µx)2

N"1 , a “standardised” measure of the varianceof a variable;

• Coe!cient of variation: cvx = !xµx

, the standard deviation normalised by the mean;

• Gini index: Gx = 12µ

"i

"j|xi"xj |

N2 , !i, j " N , the most used inequality index in theliterature;

• Generalized entropy family: GE (") = 1"2""

#1N

"i

$xiµ

%"

# 1&, a set of indices

separable and bounded between zero and one. Opposite to the more widely usedGini index, these indices allow to perfectly decompose total inequality in “within”and “between” group inequality. Among all the possible values of ", we choose themore frequently used:

– Theil index, with " = 1: Tx = 1N

"i

$xiµ ln xi

µ

%, introduced by Theil (1967)

and extensively described among others by Conceicao and Ferreira (2000),

– Mean logarithmic deviation (MLD), with " = 0: Tx = 1N

"i

$ln µ

xi

%, analo-

gous to the Theil index, but characterized by a lower sensitivity index;

• Atkinson indices: Ax = 1# 1µ

'1N

"i x

1"#i

( 11!! (# = 0.5, 1, 2), a measure that allows

di"erent sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:

Ax = 1# 1

µ

)1

N

*

i

x1"#i

+ 11!!

=

,-.

-/

1# 1µ

'1N

"i

$xi

(2 if # = 0.5

1#0

ixiµ if # = 1

1# µh

µ if # = 2

where µh is the harmonic mean.

12

is the harmonic mean.

8 Deciles (pc10, pc20 to pc90) and quartiles (pc25, pc50, pc75), in order to give an intuition of the shape

of underlying distribution and to allow the readers to build interdecile ratios.

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Page!!!25

A!New!Dataset!of!Educational!Inequality

6.! Consistency!of!measures!across!surveys

Finally, we check the consistency of the aggregate measures of educational attainment and educational in­

equality across the surveys.

Graphs in Appendices plot for each variable the measure obtained using the different data sources. Each point

&"$2'/$B>!@'$64>>/<@4"#<$24$!$<@/6&:6$6/(($)641"2>?$!"#$64'4>2-8$6!(61(!2/#$1<&"B$2'/$<1>;/?$&"#&6!2/#$&"$2'/$!L/<$

and the 45 degree line represents the perfect equality of the computed measures.

AC$E/$(44P$!2$:B1>/$,$2'!2$!"!(?</<$2'/$64"<&<2/"6?$4C$2'/$!;/>!B/$?/!><$4C$<6'44(&"B8$E/$"42&6/$2'!2$E/$5!"­

aged to achieve a good consistency across surveys, as all the points are substantially aligned around the 45 degree

(&"/=$N'/$<!5/$64"<&#/>!2&4"$!@@(&/<$24$!(($2'/$<2!2&<2&6<$9!</#$4"$?/!><$4C$<6'44(&"B$)<//$:B1>/<$*$24$*+-$E'&6'$

seem to be consistent across different data sources. An exception is the standard deviation of years of education

which turns out to be fairly different in the three surveys, but this is not surprising since this variable is not nor­

malised by the mean and it is likely to be affected by the different sample size. Therefore we can conclude that the

measures of dispersion calculated on years of education are consistent and robust to the use of different sources of

microdata, which proves their good reliability.

As far as attainment levels are concerned, the aggregate measures seem to be more dependent to the surveys

used to generate the statistics. Figures 21 to 23 in fact show that the proportion of individuals that completed re­

spectively lower secondary, upper secondary and tertiary education somehow differ when computed in ESS, EU­SILC

or IALS.

N'&<$/;&#/"6/$64":>5<$2'/$#&C:61(2$6>4<<H641"2>?$645@!>!9&(&2?$4C$/#16!2&4"!($!22!&"5/"28$E'4</$6!2/B4>&<!­

2&4"$#/@/"#<$4"$2'/$<@/6&:6$&"<2&212&4"!($C/!21>/<$4C$"!2&4"!($<6'44($<?<2/5<=$Q;/"$&C$!"$&"2/>"!2&4"!((?$'!>54"&</#$

5/!<1>/$'!<$9//"$1</#8$2'/$6(!<<&:6!2&4"$4C$/#16!2&4"!($(/;/(<$5!?$<2&(($9/$<19Y/62$24$<45/$5/!<1>/5/"2$/>>4>=$V"$

the contrary, years of schooling can be easily computed and compared internationally and indeed the indicators

of educational levels and inequality based on this simple measure appear to be fairly consistent across surveys.

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Page!!!26

Elena!Meschi!and!Francesco!Scervini

Page 27: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!27

A!New!Dataset!of!Educational!Inequality

References

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Barro, Robert J., and Jong­Wha Lee (2001) ‘International data on educational attainment. updates and implica­tions.’ Oxford Economic Papers 3, 541­563

_ (2010) ‘A new data set of educational attainment in the world, 1950­2010.’ NBER Working Papers 15902, Na­tional Bureau of Economic Research, Inc. http://www.!nber.org/papers/w15902.pdf

Checchi, Daniele (2004) ‘Does educational achievement help to explain income inequality?’ In Inequality, Growth and Poverty in an Era of Liberalization and Globalization, ed. Andrea Cornia (Oxford University Press) chapter 4

Cohen, Daniel, and Marcelo Soto (2007) ‘Growth and human capital: good data, good results.’ Journal of Eco­nomic Growth 12(1), 51­76

Conceicao, Pedro, and Pedro M. Ferreira (2000) ‘The young person’s guide to the theil index: Suggesting in­tuitive interpretations and exploring analytical applications.’ UTIP Working Paper Series. http://ssrn.com/paper=228703

Cowell, Frank (2009) ‘Measuring inequality.’ http://darp.lse.ac.uk/MI3

De Gregorio, Jose, and Jong­Wha Lee (2002) ‘Education and income inequality: New evidence from cross­coun­try data.’ Review of Income and Wealth 48(3), 395­416

de la Fuente, Angel, and Rafael Domenech (2006) ‘Human capital in growth regressions: How much difference does data quality make?’ Journal of the European Economic Association 4(1), 1­36

EUROSTAT (2005) UOE­UNESCO­OECD­EUROSTAT data collection on education: Mapping of national education programmes to ISCED­97 UOE­UNESCO­OECD­EUROSTAT

Grossman, Michael (2006) ‘Education and nonmarket outcomes.’ In Handbook ofthe Economics of Education, ed. Erik Hanushek and F. Welch (Elsevier) chapter 10, pp. 577633

Hanushek, Eric A., and Dennis D. Kimko (2000) ‘Schooling, labor­force quality, and the growth of nations.’ American Economic Review 90(5), 1184­1208

Hanushek, Eric A., and Ludger Woessmann (2010) ‘The economics of international differences in educational achievement.’ Technical Report

Harmon, Colm, Hessel Oosterbeek, and Ian Walker (2003) ‘The returns to education: Microeconomics.’ Journal of Economic Surveys 17(2), 115­156

Krueger, Alan B., and Mikael Lindahl (2001) ‘Education for growth: Why and for whom?’ Journal ofEconomic Literature 39(4), 1101­1136

Lochner, Lance, and Enrico Moretti (2004) ‘The effect of education on crime: Evidence from prison inmates, ar­rests, and self­reports.’ American Economic Review 94(1), 155189

Lopez, Ramon, Vinod Thomas, and Yan Wang (1998) ‘Addressing the education puzzle : the distribution of educa­tion and economic reform.’ Policy Research Working Paper Series 2031, The World Bank, December. http://ideas.repec.org/p/wbk/wbrwps/!2031.html

Milligan, Kevin, Enrico Moretti, and Philip Oreopoulos (2004) ‘Does education improve citizenship? Evidence from the United States and the United Kingdom.’ Journal of Public Economics 88(9­10), 1667­1695

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Page!!!28

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OECD (1999) Classifying educational programmes. Manual for ISCED­97 implementation in OECD countries OECD (Paris)

Park, Kang H. (1996) ‘Educational expansion and educational inequality on income distribution.’ Economics of Education Review 15(1), 51­58

Schneider, Silke L. (2007) ‘Measuring educational attainment in cross­national surveys: The case of the European Social Survey.’ Paper presented at the educ research group workshop of the equalsoc network

_ (2009) ‘Confusing credentials: The cross­nationally comparable measurement of educational attainment.’ PhD #&<</>2!2&4"8$S1C:/(#$U4((/B/8$VLC4>#Z$R"&;/><&2?$4C$VLC4>#

_ (2010) ‘Nominal comparability is not enough: (in­)equivalence of construct validity of cross­national measures of educational attainment in the european social survey.’ !"#"$%&'()*(+,&)$-(+.%$.)/&$.),*($*0(1,2)-).3 28(3), 343 ­ 357

Theil, Henri (1967) Economics and Information Theory (Amsterdam: North Holland)

N'45!<8$a&"4#8$b!"$\!"B8$!"#$c&94$0!"$)*++,-$dK/!<1>&"B$/#16!2&4"$&"/J1!(&2?$He&"&$64/C:6&/"2<$4C$/#16!2&4"=[$Policy Research Working Paper Series 2525, The World Bank, January

UNESCO (2006) Global education digest 2006. Comparing education statistics across the world Institute for Sta­tistics (Montreal)

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Page!!!29

A!New!Dataset!of!Educational!Inequality

A!Consistency!graphics!–!Inequality!indices

Figure!1

A Consistency graphics – Inequality indices

Figure 1

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)BE(FL)

CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDKDK

FIFI

FIFIFIFIFIFIFI

DEDEDEDEDEDEDEDE

HUHUHU

HUHUHUHUHUHUIEIEIE

IEIEIEIEIE

ITITIT

ITIT

ITITITIT

NLNLNLNLNLNL

NLNLNLNL

NONONONONONONONONO

PLPLPLPLPLPLPLPL

SISI

SISISISISISISI

SESESESESE

SESESESESE

CHCHCHCHCHCHCHCHCH

04

812

16IS

SP

0481216IALS

ATATATATATATATATATATATATAT

BGBGBG BG

BGBGBGBGBGBGBGBGCZCZCZCZCZ

CZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDKDKDKDKDKDK

FI FIFI

FI FI FIFI FIFIFIFIFI

FRFRFRFRFRFRFR FRFRFRFRFRFR

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IEIEIEIEIEIE IEIEIEIE

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NLNLNLNLNLNLNL

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PLPLPL PLPL PLPL PLPLPLPL PLPL

PTPTPTPTPT

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PT PTPT PT

PT

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SKSKSKSKSKSKSKSKSK

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ESESESESES

ESES

ESES

ESES ESES

SESESESE SE

SE SESESESESESESE

CHCHCHCHCHCHCHCHCHCHCHCHCH

04

812

16IS

SP

0 4 8 12 16ESS

CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDKDK

FIFI

FIFI FI

FI FIFIFI

DEDEDEDEDEDEDEDE

HUHU

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IT

NLNLNLNLNLNLNL

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NO NONONONONONONONO

PL PLPL PLPL PLPLPL

SISI

SISISISI SISI SI

SESESE

SE SE SE SESESESECHCHCHCHCHCHCHCHCH

UKUKUKUKUKUKUKUKUK

04

812

16IA

LS

0 4 8 12 16ESS

Years of education - Mean

16

Figure!2Figure 2

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)BE(FL) BE(FL)CZ CZCZCZCZCZ CZCZCZ

DKDKDKDKDK

DK DKDK DK

FIFIFIFIFI FIFIFI FI

DE

DE

DE DEDEDEDEDEHUHUHU HUHU HU

HUHUHU

IEIE IEIEIE IE IEIE

IT ITITIT

ITITIT

ITITNLNLNLNLNLNLNL NL

NLNL NONONONONONONONONO

PL PLPLPL PLPLPL PL

SISISISI

SI SI SISISI

SESE

SESESE SE

SESESE SECH

CHCH CHCHCH CH CH

CH

22.

533.

544.

555.

5IS

SP

22.533.544.555.5IALS

AT ATATAT ATATATATAT

AT AT

AT

AT

BGBG BGBGBGBGBGBGBGBG BG

BG

CZCZ CZCZCZCZCZ CZCZCZCZCZCZ

DKDKDKDK

DKDKDKDKDK

DKDK DKDK

FIFIFI FIFIFIFIFIFIFIFI

FI FRFRFRFRFR FRFRFRFRFRFR

FRFR

DEDE

DE

DE

DE DEDEDE DEDEDEDEDE

HUHUHUHU HUHUHUHUHUHUHU

HUIE IE IEIEIEIE IE

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LVLV

NLNL NL NLNLNL NLNLNLNLNLNL NLNONONO NONONONONONONONONONO

PLPL

PLPLPLPLPLPLPLPL

PLPLPL

PTPTPTPTPT

PTPTPTPTPTPTPTPT

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SKSKSKSKSKSK SKSK

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22.

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SP2 2.5 3 3.5 4 4.5 5 5.5

ESS

CZCZCZCZCZ CZ

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DKDK DKDKDKDKDKDK

DK

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FIFIFIFI

DEDE

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DEDEDE DEDE HUHU

HU

HUHUHU

HUHUHU IEIEIEIE IE

IEIEIE

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ITIT ITNLNL NL NL

NLNLNL

NLNLNL

NO NONONONONONONONO

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SISI

SISI SISI

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SE

SE

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CH CHCHCH

CHCHCH

CHCH

UKUK UKUKUKUKUKUKUK

22.

53

3.5

44.

55

5.5

IALS

2 2.5 3 3.5 4 4.5 5 5.5ESS

Years of education - St. Dev.

Figure 3

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDKDK

FI FIFIFIFI FIFIFIFIDEDE

DE DEDEDEDEDEHUHUHU HUHUHUHUHUHU

IEIEIEIEIE IEIEIE

IT ITITITITIT ITITITNLNLNLNLNLNLNL NLNLNLNONONONONONONONONO

PLPLPLPL PLPLPLPL

SISISISISISI SISISI

SE SESESESE SE

SESESESECHCHCHCHCHCHCHCHCH

0.1

.2.3

.4.5

.6IS

SP

0.1.2.3.4.5.6IALS

ATATATATATATATATAT

ATATATAT

BGBG BGBGBGBGBGBGBGBGBGBGCZCZCZCZCZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDKDKDKDKDKDK

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NLNLNLNLNLNLNLNLNLNLNLNLNLNONONONONONONONONONONONONO

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ESES ESESES

ESESESESESESESES

SESESESESESE

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0.1

.2.3

.4.5

.6IS

SP

0 .1 .2 .3 .4 .5 .6ESS

CZCZCZCZCZCZCZCZCZ

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ITITITNLNLNLNLNLNLNL

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0.1

.2.3

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.6IA

LS

0 .1 .2 .3 .4 .5 .6ESS

Years of education - Gini

17

Page 30: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!30

Elena!Meschi!and!Francesco!Scervini

Figure!3

Figure 2

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)BE(FL) BE(FL)CZ CZCZCZCZCZ CZCZCZ

DKDKDKDKDK

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FIFIFIFIFI FIFIFI FI

DE

DE

DE DEDEDEDEDEHUHUHU HUHU HU

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IEIE IEIEIE IE IEIE

IT ITITIT

ITITIT

ITITNLNLNLNLNLNLNL NL

NLNL NONONONONONONONONO

PL PLPLPL PLPLPL PL

SISISISI

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CH

22.

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555.

5IS

SP

22.533.544.555.5IALS

AT ATATAT ATATATATAT

AT AT

AT

AT

BGBG BGBGBGBGBGBGBGBG BG

BG

CZCZ CZCZCZCZCZ CZCZCZCZCZCZ

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DKDKDKDKDK

DKDK DKDK

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22.

533.

544.

555.

5IS

SP

2 2.5 3 3.5 4 4.5 5 5.5ESS

CZCZCZCZCZ CZ

CZCZCZ

DKDK DKDKDKDKDKDK

DK

FIFIFIFIFI

FIFIFIFI

DEDE

DE

DEDEDE DEDE HUHU

HU

HUHUHU

HUHUHU IEIEIEIE IE

IEIEIE

IT

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ITIT ITNLNL NL NL

NLNLNL

NLNLNL

NO NONONONONONONONO

PLPLPLPL

PLPLPLPL

SISI

SISI SISI

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UKUK UKUKUKUKUKUKUK

22.

53

3.5

44.

55

5.5

IALS

2 2.5 3 3.5 4 4.5 5 5.5ESS

Years of education - St. Dev.

Figure 3

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDKDK

FI FIFIFIFI FIFIFIFIDEDE

DE DEDEDEDEDEHUHUHU HUHUHUHUHUHU

IEIEIEIEIE IEIEIE

IT ITITITITIT ITITITNLNLNLNLNLNLNL NLNLNLNONONONONONONONONO

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0 .1 .2 .3 .4 .5 .6ESS

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Years of education - Gini

17

Figure!4Figure 4

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDKDK

FIFIFI FIFI FIFIFIFI

DEDE

DE DEDEDEDEDE

HUHUHU HUHUHUHUHUHUIEIE IEIEIE IEIEIE

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IT ITITITNLNLNL NLNLNLNL NLNLNLNONONONONONONONONO

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ATATATATATATATATATATATATAT

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SP

0 .05 .1 .15 .2 .25ESS

CZCZCZCZCZCZCZCZCZ

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PLPL

PLPLPLPLPLPL

SISISISISISISISISI

SESESE

SESE

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0.0

5.1

.15

.2.2

5IA

LS

0 .05 .1 .15 .2 .25ESS

Years of education - MLD

Figure 5

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)CZCZCZCZCZCZCZCZCZ

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Years of education - Theil

18

Page 31: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!31

A!New!Dataset!of!Educational!Inequality

Figure!5

Figure 4

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Years of education - MLD

Figure 5

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18

Figure!6Figure 6

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Years of education - CoV

Figure 7

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)CZCZCZCZCZCZCZCZCZ

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Years of education - Atkinson (0.5)

19

Page 32: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!32

Elena!Meschi!and!Francesco!Scervini

Figure!7

Figure 6

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Figure 7

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19

Figure!8Figure 8

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Figure 9

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)CZ CZCZCZCZCZCZCZCZ

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Years of education - Atkinson (2)

20

Page 33: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!33

A!New!Dataset!of!Educational!Inequality

Figure!9

Figure 8

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LS

0 .03 .06 .09 .12 .15 .18ESS

Years of education - Atkinson (1)

Figure 9

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)CZ CZCZCZCZCZCZCZCZ

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0.1

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0.1

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0 .1 .2 .3 .4ESS

Years of education - Atkinson (2)

20

Page 34: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!34

Elena!Meschi!and!Francesco!Scervini

Page 35: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!35

A!New!Dataset!of!Educational!Inequality

B!Consistency!graphics!–!Percentiles

Figure!10

B Consistency graphics – Percentiles

Figure 10

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)

CZCZCZCZCZCZ

CZCZCZ

DKDKDKDKDKDKDKDKDK

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FI DEDEDEDEDEDEDEDE

HUHUHUHUHUHUHUHUHU IEIEIEIEIE

IEIEIE

ITITITITITITITITIT

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NONONONONONONONO

NO

PL

PLPLPLPLPLPL PL

SISISISI

SISISISISISESESESESESE

SESESESECHCHCHCHCHCHCHCHCH

04

812

1620

ISSP

048121620IALS

ATATATATATATATATATATATATAT

BGBGBG BGBGBGBGBGBGBGBGBGCZCZCZCZCZ

CZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDKDKDKDKDKDK

FIFI FIFI FIFI FI

FI FIFI

FI

FI

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ES ESES

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04

812

1620

ISSP

0 4 8 12 16 20ESS

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FI FIFIFIFI FI

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HUHUHUHUHUHUHUHUHUIEIEIEIEIE IE

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CHUK

UKUKUKUKUKUKUKUK

04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 10

21

Figure!11Figure 11

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)BE(FL)

CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDKDK

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FIFIFI

DEDEDEDEDEDEDE

DEHU

HUHUHUHUHUHUHUHU

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IE

ITITITIT

ITITITITIT

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NLNL

NONONONONONONONONO

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SISISISISISI

SISISI

SESESESESESESESE

SESECHCHCHCHCHCHCHCHCH

04

812

1620

ISSP

048121620IALS

ATATATATATAT

ATAT

ATATATATAT

BGBGBGBGBG

BGBGBGBGBG

BGBG

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CZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDK

DKDKDKDKDK

FIFI FI FIFIFI FI

FI FIFIFIFI

FRFRFR FRFRFRFR FR

FRFRFRFRFR

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HUHUHUHUHUHUHUHU

HUHUHUHUIEIEIEIE IE

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IT ITITITIT

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LVLVLVLVLVLVLV

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04

812

1620

ISSP

0 4 8 12 16 20ESS

CZCZCZCZCZ

CZCZCZCZ

DKDKDK

DK DKDKDKDKDK

FI FIFIFI FI

FI FI FIFI

DEDEDEDEDEDEDEDE

HUHUHUHUHU

HUHUHUHU

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UKUKUKUKUKUKUKUKUK

04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 20

Figure 12

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)BE(FL)BE(FL)

CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDK

DK

FIFIFIFI

FIFIFIFIFI

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HUHUHUHUHUHU

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IEIE

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NLNLNL

NONONONONONONONONO

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SISISISISISI

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SESESECHCHCHCHCHCHCHCHCH

04

812

1620

ISSP

048121620IALS

ATATATAT

ATATATATAT

ATATATAT

BGBGBGBGBGBG

BGBGBGBGBGBG

CZCZCZ CZCZ

CZCZCZCZCZCZCZCZ

DKDKDKDKDKDK

DKDKDKDKDKDKDK

FIFIFIFI FI

FI FIFIFI FIFIFI

FRFRFR FRFRFRFR

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DEDEDE DEDEDEDEDEDEDEDEDE

DE

HUHU HUHUHUHUHUHUHUHUHUHU

IEIE IEIEIEIE IE IE

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SISISI SISISISISI

SISISI SISI

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ES

SESESESE SESE SE

SESESE SESESE

CHCHCHCHCHCHCHCHCHCHCHCHCH

04

812

1620

ISSP

0 4 8 12 16 20ESS

CZ CZCZCZ

CZCZCZCZCZ

DKDKDK

DKDKDKDKDKDK

FI FIFI FI FI

FI FIFIFI

DE DEDEDEDEDEDEDE

HUHUHU

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SESESESE

CHCHCHCHCHCHCHCHCH

UKUKUKUKUKUKUKUKUK

04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 25

22

Page 36: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!36

Elena!Meschi!and!Francesco!Scervini

Figure!12

Figure 11

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)BE(FL)

CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDKDK

FIFIFIFIFIFI

FIFIFI

DEDEDEDEDEDEDE

DEHU

HUHUHUHUHUHUHUHU

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IE

ITITITIT

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NONONONONONONONONO

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SISISISISISI

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04

812

1620

ISSP

048121620IALS

ATATATATATAT

ATAT

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BGBGBGBGBG

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FIFI FI FIFIFI FI

FI FIFIFIFI

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04

812

1620

ISSP

0 4 8 12 16 20ESS

CZCZCZCZCZ

CZCZCZCZ

DKDKDK

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FI FIFIFI FI

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DEDEDEDEDEDEDEDE

HUHUHUHUHU

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04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 20

Figure 12

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)BE(FL)BE(FL)

CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDK

DK

FIFIFIFI

FIFIFIFIFI

DEDEDEDEDEDEDEDE

HUHUHUHUHUHU

HUHUHU

IEIEIEIEIEIE

IEIE

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NLNLNLNLNLNLNL

NLNLNL

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04

812

1620

ISSP

048121620IALS

ATATATAT

ATATATATAT

ATATATAT

BGBGBGBGBGBG

BGBGBGBGBGBG

CZCZCZ CZCZ

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FIFIFIFI FI

FI FIFIFI FIFIFI

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DEDEDE DEDEDEDEDEDEDEDEDE

DE

HUHU HUHUHUHUHUHUHUHUHUHU

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04

812

1620

ISSP

0 4 8 12 16 20ESS

CZ CZCZCZ

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FI FIFI FI FI

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04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 25

22

Figure!13Figure 13

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)BE(FL)BE(FL)BE(FL)

CZCZCZCZCZCZCZCZCZ

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DEDEDEDEDEDEDEDE

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IEIE

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NLNLNLNLNLNLNL

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04

812

1620

ISSP

048121620IALS

ATATATAT

ATATATATATATATATAT

BGBGBGBGBG

BGBGBGBGBGBGBGCZCZCZCZCZ

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DEDEDEDEDEDEDEDEDEDEDE

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HU HUHUHUHU

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04

812

1620

ISSP

0 4 8 12 16 20ESS

CZCZCZCZCZCZCZCZCZ

DKDK DK

DKDKDKDKDKDK

FIFIFI FI FI

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DEDEDEDEDEDEDEDE

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UKUKUKUKUKUKUKUKUK

04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 30

Figure 14

BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDK

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FIFIFIFI

FIFIFIFIFI

DEDEDEDEDEDEDEDE

HUHUHU

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IEIEIEIE

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ITITITITIT IT

IT

NLNLNLNLNLNL

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PLPLPLPLPLPLPLPL

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04

812

1620

ISSP

048121620IALS

AT

AT

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04

812

1620

ISSP

0 4 8 12 16 20ESS

CZCZCZCZCZCZCZCZCZ

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04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 40

23

Page 37: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!37

A!New!Dataset!of!Educational!Inequality

Figure!14

Figure 13

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)BE(FL)BE(FL)BE(FL)

CZCZCZCZCZCZCZCZCZ

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DKDKDKDKDK

FIFIFIFIFI

FIFIFIFI

DEDEDEDEDEDEDEDE

HUHUHUHU

HUHUHUHUHU

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IEIE

ITITIT

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04

812

1620

ISSP

048121620IALS

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DKDKDKDKDKDK

DKDKDKDKDKDKDK

FI FI FIFIFI FI

FIFIFI FI FIFI

FRFRFRFRFRFR

FRFRFR FRFRFRFR

DEDEDEDEDEDEDEDEDEDEDE

DEDE

HU HUHUHUHU

HUHUHUHUHUHUHU

IEIE IEIE IEIE IE IE

IEIEIE IEIE

ITITITITIT

ITITITIT IT

IT

LVLVLVLV

LVLVLVLVLVLVLVLV

NLNLNLNLNLNLNL

NLNLNLNLNLNL

NONONONONONONONONONONO

NONO

PLPLPLPLPLPLPLPLPLPLPLPL PL

PTPT PTPTPT

PTPTPTPT PTPT PT

PT

SKSKSKSKSK

SKSKSKSKSKSKSKSK

SISISI SISISISI

SISISI SISISI

ESESES ES

ESES ESESESESES

ESES

SESESE SESESE

SESE SESESESESE

CHCHCHCHCHCHCHCHCHCHCHCHCH

04

812

1620

ISSP

0 4 8 12 16 20ESS

CZCZCZCZCZCZCZCZCZ

DKDK DK

DKDKDKDKDKDK

FIFIFI FI FI

FIFIFI FI

DEDEDEDEDEDEDEDE

HUHUHUHUHUHUHUHUHU

IEIE IEIE IE IEIE

IE

ITITIT ITITITIT IT

IT

NLNLNL

NLNLNLNLNLNLNL

NONO

NONONONONONONO

PLPLPLPL

PLPLPLPL

SI SISISISI

SISISI SI

SESESESESE

SESESE SESE

CHCHCHCHCHCHCHCHCH

UKUKUKUKUKUKUKUKUK

04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 30

Figure 14

BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDK

DKDKDK

FIFIFIFI

FIFIFIFIFI

DEDEDEDEDEDEDEDE

HUHUHU

HUHUHUHUHUHUIEIEIEIE

IEIEIEIE

ITIT

ITITITITIT IT

IT

NLNLNLNLNLNL

NLNLNLNL

NONONONONONONO

NONO

PLPLPLPLPLPLPLPL

SISISI

SISISISISISI

SESESESESESE

SESESESE

CHCHCHCHCHCHCHCHCH

04

812

1620

ISSP

048121620IALS

AT

AT

AT

ATATATATATATATATAT

AT

BGBGBGBG

BGBGBGBGBGBGBGBGCZCZCZCZCZCZCZCZCZCZCZCZCZ

DKDKDKDKDK

DKDKDKDKDKDKDKDK

FI FI FIFI FI

FI FI FIFIFI FIFI

FRFR FRFRFRFRFRFR FRFR

FRFRFR

DEDEDEDEDEDEDEDEDE

DEDEDEDE

HUHUHUHU HU

HUHUHUHUHUHUHU

IEIE IEIE IEIE

IEIE IEIEIE IEIE

ITITITIT

IT ITIT

IT IT ITIT

LVLV LVLVLVLVLVLVLVLVLVLV

NLNLNLNLNLNL

NLNLNLNLNLNLNL

NONONONONONONONONO

NONONONO

PLPLPLPL PLPLPLPLPLPLPL PLPL

PTPTPTPT

PTPTPTPTPTPT PT

PT

PT

SKSKSKSK

SKSKSKSKSKSKSKSKSK

SISISISISISISISISI SISISI

SI

ES ESESESES ESES

ESESES ESES

ES

SESE SESESESE

SESESESESESESE

CHCHCHCHCHCHCHCHCHCHCHCHCH

04

812

1620

ISSP

0 4 8 12 16 20ESS

CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDKDK

FIFI FI

FI FIFI FI FI

FI

DEDEDEDEDEDEDEDE

HUHUHUHUHUHUHUHUHU

IEIE IEIE IE

IE IEIE

ITITIT

ITIT ITIT

IT

IT

NLNLNLNLNLNL NL

NLNLNL

NO

NONONONONONONONO

PLPLPL

PLPLPLPLPL

SISI SI

SISISISI SISI

SESE SESESE

SESESESESE

CHCHCHCHCHCHCHCHCH

UKUKUKUKUKUKUK

UKUK

04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 40

23

Figure!15Figure 15

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)BE(FL)

CZCZCZCZCZCZCZCZCZDKDKDKDK

DKDKDKDKDK

FIFIFI

FIFIFIFIFIFI

DEDEDEDEDEDEDE

DE

HUHU

HUHUHUHUHUHUHUIEIE

IEIEIEIEIEIE

ITITIT

ITIT

ITITITIT

NLNLNLNLNLNLNL

NLNLNL

NONONONONONO

NONONO

PLPLPLPLPLPLPLPL

SISI

SISISISISISISI

SESESESESE

SESESESESECHCHCHCHCHCHCH

CHCH

04

812

1620

ISSP

048121620IALS

ATAT

AT

ATATATATATATATATATAT

BGBGBG

BGBGBGBGBGBGBGBGBGCZCZCZCZCZCZCZCZCZCZCZCZCZ

DKDKDK

DKDKDKDKDKDKDK DKDKDK

FI FIFI FI

FI FIFIFI FIFI FIFI

FRFRFRFRFRFRFR FRFR

FRFRFRFR

DEDEDEDEDEDEDEDEDE

DEDEDEDE

HUHUHUHU

HUHUHUHUHUHUHUHUIEIEIE IE

IE IEIE IEIEIE IEIE IE

ITIT

ITITIT

ITIT

ITIT

ITIT

LVLVLVLVLV LV

LVLVLVLVLV

LV

NLNLNLNLNLNLNL

NL NLNLNLNL

NL

NONONONONO

NONONONONONONO

NO

PLPLPL PLPLPLPL PLPLPL

PLPLPL

PTPTPTPTPTPTPT

PTPT PTPT

PTPT

SKSKSKSKSKSKSKSKSKSKSKSK

SK

SISISI SI

SISISISI SISISI SISI

ESESESES ESESESES

ESESESESES

SE SESESESE

SESESESESESESESE

CHCHCHCHCHCHCHCHCHCHCHCH

CH

04

812

1620

ISSP

0 4 8 12 16 20ESS

CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDK DK

FI FI FIFI FI

FIFI FIFI

DEDEDEDEDEDEDE

DE

HUHUHU

HUHUHUHUHUHU

IE IEIEIEIE IEIEIE

IT ITIT

IT ITITIT IT

IT

NLNLNLNL

NLNLNLNL NLNL

NONONO NONONONONONO

PLPL

PLPLPL PLPLPL

SI SI

SISISISI SISISI

SESESE

SESESESESESESE

CHCHCHCHCHCHCHCHCH

UKUKUKUKUKUKUKUKUK

04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 50

Figure 16

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

CZCZCZCZCZCZCZCZ

CZDKDKDKDKDKDKDKDK

DK

FIFIFIFI

FIFIFIFIFI

DEDEDEDEDE

DEDE

DE

HUHU

HUHUHUHUHUHUHUIEIEIEIE

IEIEIEIE

ITITIT

ITITITITITIT

NLNLNLNLNLNL

NLNLNLNL

NONONONONONONONONO

PLPLPLPLPLPLPLPL

SISISISI SISISISISI

SESESESE

SESESESESESE

CHCHCH

CHCHCHCHCHCH

04

812

1620

ISSP

048121620IALS

ATATATATATATATATATATATATAT

BGBG

BGBGBGBGBGBGBGBGBG

BG

CZCZCZCZCZCZCZCZCZCZCZCZ

CZ

DKDKDK DK

DKDKDKDKDKDKDKDKDK

FIFI FI

FI FIFI FIFIFI

FIFIFI

FRFRFR FRFRFRFRFRFRFR

FR FRFR

DEDEDEDEDEDEDE

DEDEDEDEDEDE

HUHUHUHU

HUHUHUHUHUHUHUHU

IE IEIEIEIE IE

IE IEIEIEIE

IEIE

IT ITIT ITIT

ITIT ITITITIT

LVLVLVLVLV LVLVLVLVLVLVLV

NLNLNLNLNLNL

NLNLNLNLNLNLNL

NONONONO

NO NONONONONONONONO

PLPLPL PLPL

PL PLPLPLPLPLPLPL

PTPTPTPTPTPTPT PT

PTPTPTPT

PT

SKSKSK

SKSKSKSKSKSKSKSKSK

SK

SISISI

SI SISISISISISI SISI

SI

ESESES ESESES ES

ESESESES ES

ES

SESESESE

SESESESESESESESESE

CHCHCHCHCH

CHCHCHCHCHCHCHCH

04

812

1620

ISSP

0 4 8 12 16 20ESS

CZCZCZCZCZCZCZCZCZDK DKDKDKDKDKDKDKDK

FI FIFI

FI FIFIFIFI

FI

DEDEDEDEDEDEDEDE

HU

HUHUHUHUHUHUHUHU

IE IEIE IEIE IEIEIE

IT ITIT IT

ITITITITIT

NLNLNLNLNLNLNLNLNL

NL

NONONONONONONONONO

PLPLPLPL

PLPLPLPL

SISI SI

SISISISISI SI

SESESE

SE SESESESESESECHCHCHCHCHCHCHCHCH

UKUKUKUKUKUKUKUKUK

04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 60

24

Page 38: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!38

Elena!Meschi!and!Francesco!Scervini

Figure!16

Figure 15

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)BE(FL)

CZCZCZCZCZCZCZCZCZDKDKDKDK

DKDKDKDKDK

FIFIFI

FIFIFIFIFIFI

DEDEDEDEDEDEDE

DE

HUHU

HUHUHUHUHUHUHUIEIE

IEIEIEIEIEIE

ITITIT

ITIT

ITITITIT

NLNLNLNLNLNLNL

NLNLNL

NONONONONONO

NONONO

PLPLPLPLPLPLPLPL

SISI

SISISISISISISI

SESESESESE

SESESESESECHCHCHCHCHCHCH

CHCH

04

812

1620

ISSP

048121620IALS

ATAT

AT

ATATATATATATATATATAT

BGBGBG

BGBGBGBGBGBGBGBGBGCZCZCZCZCZCZCZCZCZCZCZCZCZ

DKDKDK

DKDKDKDKDKDKDK DKDKDK

FI FIFI FI

FI FIFIFI FIFI FIFI

FRFRFRFRFRFRFR FRFR

FRFRFRFR

DEDEDEDEDEDEDEDEDE

DEDEDEDE

HUHUHUHU

HUHUHUHUHUHUHUHUIEIEIE IE

IE IEIE IEIEIE IEIE IE

ITIT

ITITIT

ITIT

ITIT

ITIT

LVLVLVLVLV LV

LVLVLVLVLV

LV

NLNLNLNLNLNLNL

NL NLNLNLNL

NL

NONONONONO

NONONONONONONO

NO

PLPLPL PLPLPLPL PLPLPL

PLPLPL

PTPTPTPTPTPTPT

PTPT PTPT

PTPT

SKSKSKSKSKSKSKSKSKSKSKSK

SK

SISISI SI

SISISISI SISISI SISI

ESESESES ESESESES

ESESESESES

SE SESESESE

SESESESESESESESE

CHCHCHCHCHCHCHCHCHCHCHCH

CH

04

812

1620

ISSP

0 4 8 12 16 20ESS

CZCZCZCZCZCZCZCZCZ

DKDKDKDKDKDKDKDK DK

FI FI FIFI FI

FIFI FIFI

DEDEDEDEDEDEDE

DE

HUHUHU

HUHUHUHUHUHU

IE IEIEIEIE IEIEIE

IT ITIT

IT ITITIT IT

IT

NLNLNLNL

NLNLNLNL NLNL

NONONO NONONONONONO

PLPL

PLPLPL PLPLPL

SI SI

SISISISI SISISI

SESESE

SESESESESESESE

CHCHCHCHCHCHCHCHCH

UKUKUKUKUKUKUKUKUK

04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 50

Figure 16

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

CZCZCZCZCZCZCZCZ

CZDKDKDKDKDKDKDKDK

DK

FIFIFIFI

FIFIFIFIFI

DEDEDEDEDE

DEDE

DE

HUHU

HUHUHUHUHUHUHUIEIEIEIE

IEIEIEIE

ITITIT

ITITITITITIT

NLNLNLNLNLNL

NLNLNLNL

NONONONONONONONONO

PLPLPLPLPLPLPLPL

SISISISI SISISISISI

SESESESE

SESESESESESE

CHCHCH

CHCHCHCHCHCH

04

812

1620

ISSP

048121620IALS

ATATATATATATATATATATATATAT

BGBG

BGBGBGBGBGBGBGBGBG

BG

CZCZCZCZCZCZCZCZCZCZCZCZ

CZ

DKDKDK DK

DKDKDKDKDKDKDKDKDK

FIFI FI

FI FIFI FIFIFI

FIFIFI

FRFRFR FRFRFRFRFRFRFR

FR FRFR

DEDEDEDEDEDEDE

DEDEDEDEDEDE

HUHUHUHU

HUHUHUHUHUHUHUHU

IE IEIEIEIE IE

IE IEIEIEIE

IEIE

IT ITIT ITIT

ITIT ITITITIT

LVLVLVLVLV LVLVLVLVLVLVLV

NLNLNLNLNLNL

NLNLNLNLNLNLNL

NONONONO

NO NONONONONONONONO

PLPLPL PLPL

PL PLPLPLPLPLPLPL

PTPTPTPTPTPTPT PT

PTPTPTPT

PT

SKSKSK

SKSKSKSKSKSKSKSKSK

SK

SISISI

SI SISISISISISI SISI

SI

ESESES ESESES ES

ESESESES ES

ES

SESESESE

SESESESESESESESESE

CHCHCHCHCH

CHCHCHCHCHCHCHCH

04

812

1620

ISSP

0 4 8 12 16 20ESS

CZCZCZCZCZCZCZCZCZDK DKDKDKDKDKDKDKDK

FI FIFI

FI FIFIFIFI

FI

DEDEDEDEDEDEDEDE

HU

HUHUHUHUHUHUHUHU

IE IEIE IEIE IEIEIE

IT ITIT IT

ITITITITIT

NLNLNLNLNLNLNLNLNL

NL

NONONONONONONONONO

PLPLPLPL

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SISI SI

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SE SESESESESESECHCHCHCHCHCHCHCHCH

UKUKUKUKUKUKUKUKUK

04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 60

24

Figure!17Figure 17

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)

CZCZCZCZCZCZCZCZ

CZDKDKDKDKDKDK

DKDKDK

FIFI

FIFIFIFIFIFIFI

DEDEDE

DEDEDEDE

DEHUHUHUHUHUHUHU HUHU IEIEIEIEIEIEIEIE

ITIT

ITITITITITIT

IT

NLNLNLNLNLNLNLNLNL

NL

NONONONONONONONONO

PLPLPLPLPLPLPLPL SISISISISISISISI

SI

SESESESE

SESESESESESECHCHCHCHCHCHCHCHCH

04

812

1620

ISSP

048121620IALS

ATATATATATAT

ATATATAT

ATATAT

BGBGBGBGBG

BGBGBGBGBGBGBG

CZCZCZCZCZCZCZCZCZCZCZCZCZDKDKDK

DKDKDKDKDKDKDKDKDKDK

FI FIFI

FI FI FIFIFI FIFIFIFI

FRFRFRFRFRFRFRFRFRFR

FRFRFR

DEDEDEDE

DEDE

DEDEDEDE

DEDEDE

HUHU

HUHUHUHUHUHUHUHUHUHUIE

IEIE IEIEIE IEIEIE

IEIE IEIE

IT ITIT IT

ITITITITITIT

IT

LVLVLVLVLV LVLVLVLVLVLVLV

NLNLNLNLNLNLNLNLNL

NLNLNLNL

NONONO NONONONONONONO

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PLPL PL

PLPL PLPLPLPLPLPLPL

PL

PTPTPTPTPTPT

PTPTPT

PTPTPT

PT

SKSKSKSKSKSKSKSKSKSKSKSK SK

SISISI SISISI SISISISI

SI SISI

ES ESESESESES

ESESESES

ESESES

SE SESESE

SESESESESESE SESESE

CHCHCHCHCHCHCHCHCHCHCH

CHCH

04

812

1620

ISSP

0 4 8 12 16 20ESS

CZCZCZCZCZCZCZCZCZDK DKDKDKDKDKDKDKDK

FIFI

FI FI FIFI FI FIFI

DEDEDEDEDEDEDEDE

HUHUHUHUHUHUHU

HUHU

IE IEIEIE IEIEIE IE

ITITIT

ITITITITIT IT

NLNLNLNLNLNL

NLNLNLNL

NONONO

NONONONONONO

PLPLPLPLPLPLPLPL

SI SISISI SISISISI SI

SE SESESE SESESESESESE

CHCHCHCHCHCHCHCHCH

UKUKUKUKUKUKUKUKUK

04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 70

Figure 18

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)

CZCZCZCZCZCZCZCZCZDKDKDKDK

DKDKDKDKDK

FIFIFIFIFI

FIFIFIFI

DEDEDEDEDEDEDE

DEHUHUHUHUHUHUHUHUHU IEIEIEIEIEIEIE

IE

ITIT

ITITITITITIT

IT

NLNLNLNLNLNLNL

NLNLNL

NONONONONONONO

NONO

PLPLPLPLPLPLPLPL SISISISISISISISI

SI

SESESESE

SESESESESESE

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04

812

1620

ISSP

048121620IALS

ATATATATATAT

ATATATATAT

ATATBGBGBGBGBGBGBGBGBGBGBG

BG

CZCZCZ

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DKDKDKDKDKDKDK

FIFI FI

FI FI FIFI FIFIFIFIFI

FRFRFR FRFRFRFRFRFRFRFRFRFR

DEDE

DEDEDEDEDE

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HUHUHUHU HUHUHUHUHUHUHU

HUIE IEIE

IEIEIEIE IEIEIEIE IEIE

IT ITITIT

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NLNLNLNLNLNLNL

NLNLNLNLNLNL

NONONONO

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PL PLPLPL PLPLPLPLPLPL

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PT

SK SKSKSKSKSKSKSKSKSKSKSK

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SISISI SI SISISISI

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04

812

1620

ISSP

0 4 8 12 16 20ESS

CZCZCZCZCZCZCZCZCZDKDKDKDKDKDKDKDKDK

FI FIFI FI FI

FI FIFIFI

DEDE DEDEDEDEDE

DE

HUHU

HUHUHUHUHUHUHU

IE IEIEIEIE IEIEIE

IT ITITITITITITIT

IT

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NL

NONONONONONONONONO

PLPLPLPLPLPLPLPL

SI SISISISISI SISI

SI

SESESE SESESE

SESESE SECHCHCHCHCHCH

CHCHCH

UKUKUKUKUKUKUKUKUK

04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 75

25

Page 39: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!39

A!New!Dataset!of!Educational!Inequality

Figure!18

Figure 17

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)

CZCZCZCZCZCZCZCZ

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DKDKDK

FIFI

FIFIFIFIFIFIFI

DEDEDE

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ITIT

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812

1620

ISSP

048121620IALS

ATATATATATAT

ATATATAT

ATATAT

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BGBGBGBGBGBGBG

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04

812

1620

ISSP

0 4 8 12 16 20ESS

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FIFI

FI FI FIFI FI FIFI

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ITITIT

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SE SESESE SESESESESESE

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UKUKUKUKUKUKUKUKUK

04

812

1620

IALS

0 4 8 12 16 20ESS

Years of education - Percentile 70

Figure 18

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

BE(FL)

CZCZCZCZCZCZCZCZCZDKDKDKDK

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04

812

1620

ISSP

048121620IALS

ATATATATATAT

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04

812

1620

ISSP

0 4 8 12 16 20ESS

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25

Figure!19Figure 19

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Years of education - Percentile 80

Figure 20

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Years of education - Percentile 90

26

Page 40: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!40

Elena!Meschi!and!Francesco!Scervini

Figure!20

Figure 19

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0 4 8 12 16 20ESS

Years of education - Percentile 80

Figure 20

BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)

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Years of education - Percentile 90

26

Page 41: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!41

A!New!Dataset!of!Educational!Inequality

C!Consistency!graphics!–!Attainmentlevels

Figure!21

C Consistency graphics – Attainment levels

Figure 21

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Figure!22

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Page 42: GROWING INEQUALITIES’ IMPACTS - core.ac.uk · Acknowledgement We thank Gabriele Ballarino, Michela Braga, Massimiliano Bratti, Daniele Checchi, Antonio Filippin, Carlo Fiorio, Marco

Page!!!42

Elena!Meschi!and!Francesco!Scervini

Figure!23

Figure 22

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Page!!!43

A!New!Dataset!of!Educational!Inequality

GINI!Discussion!Papers

Recent publications of GINI. They can be downloaded from the website www.gini-research.org under the subject Papers.

DP!5! Forthcoming:!Household!Joblessness!and!Its!Impact!on!Poverty!and!Deprivation!in!Europe

!! Marloes!de!Graaf-Zijl!! January!2011

DP!4! Forthcoming:!Inequality!Decompositions!-!A!Reconciliation

!! Frank!A.!Cowell!and!Carlo!V.!Fiorio!! December!2010

DP!3! A!New!Dataset!of!Educational!Inequality

!! Elena!Meschi!and!Francesco!Scervini!! December!2010

DP!2! Coverage!and!adequacy!of!Minimum!Income!schemes!in!the!European!Union

!! Francesco!Figari,!Tina!Haux,!Manos!Matsaganis!and!Holly!Sutherland!! November!2010

DP!1! Distributional!Consequences!of!Labor!Demand!Adjustments!to!a!Downturn.!A!Model-based!Approach!with!Application!to!!! Germany!2008-09

!! Olivier!Bargain,!Herwig!Immervoll,!Andreas!Peichl!and!Sebastian!Siegloch!! September!2010

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Elena!Meschi!and!Francesco!Scervini

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A!New!Dataset!of!Educational!Inequality

Information!on!the!GINI!project

AimsThe core objective of GINI is to deliver important new answers to questions of great interest to European societies: What are the social, cultural and political impacts that increasing inequalities in income, wealth and education may have? For the answers, GINI combines an interdisciplinary analysis that draws on economics, sociology, political science and health studies, with improved methodologies, uniform measurement, wide country coverage, a clear policy dimension and broad dissemination.

Methodologically, GINI aims to:

8 exploit differences between and within 29 countries in inequality levels and trends for understanding the impacts and teasing out implications for policy and institutions,

8 elaborate on the effects of both individual distributional positions and aggregate inequalities, and

8 allow for feedback from impacts to inequality in a two-way causality approach.

The project operates in a framework of policy-oriented debate and international comparisons across all EU countries (except Cyprus and Malta), the USA, Japan, Canada and Australia.

Inequality!Impacts!and!AnalysisSocial impacts of inequality include educational access and achievement, individual employment oppor-tunities and labour market behaviour, household joblessness, living standards and deprivation, family and household formation/breakdown, housing and intergenerational social mobility, individual health and life expectancy, and social cohesion versus polarisation. Underlying long-term trends, the economic cycle and ,>9$+B<<9.,$*.'.+)'&$'.@$9+-.-E)+$+<)()($?)&&$A9$).+-<F-<',9@C$G-&),)+-H+B&,B<'&$)EF'+,($).;9(,)I',9@$'<9/$4-$increasing income/educational inequalities widen cultural and political ‘distances’, alienating people from politics, globalisation and European integration? Do they affect individuals’ participation and general social trust? Is acceptance of inequality and policies of redistribution affected by inequality itself ? What effects @-$F-&),)+'&$(J(,9E($K+-'&),)-.(L?)..9<H,'M9(H'&&N$>';9O$P).'&&J3$),$:-+B(9($-.$+-(,($'.@$A9.9*,($-: $F-&)+)9($&)E),).I$ ).+-E9$).9QB'&),J$'.@$ ),($9:*$+)9.+J$:-<$E),)I',).I$-,>9<$ ).9QB'&),)9($K>9'&,>3$>-B().I3$9@B+',)-.$and opportunity), and addresses the question what contributions policy making itself may have made to the growth of inequalities.

Support!and!ActivitiesThe project receives EU research support to the amount of Euro 2.7 million. The work will result in four E').$<9F-<,($'.@$'$*.'&$<9F-<,3$(-E9$R6$@)(+B(()-.$F'F9<($'.@$1S$+-B.,<J$<9F-<,(C$D>9$(,'<,$-: $,>9$F<-T9+,$is 1 February 2010 for a three-year period. Detailed information can be found on the website.

www.gini-research.org

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Amsterdam!Institute!for!Advanced!labour!Studies!

University!of!Amsterdam!

!

Plantage!Muidergracht!12!! !!1018!TV!Amsterdam!! !!The!Netherlands!

Tel!+31!20!525!4199!! !!Fax!+31!20!525!4301!

[email protected]!! !!www.gini-research.org

f>4Y/62$C1"#/#$1"#/>$2'/$Socio­Economic sciencesand Humanities theme.