d of e i - core · acknowledgement we thank gabriele ballarino, michela braga, massimiliano bratti,...
TRANSCRIPT
Distributional Consequences of Labor Demand Adjustm
ents to a DownturnOlivier Bargain, Herwig Im
mervoll, Andreas Peichl, Sebastian Siegloch
GIN
I DP 1
GINI Discussion PaPer 3December 2010
A New DAtAset of eDucAtIoNAl INequAlIty
ElenaMeschiandFrancescoScervini
GrOwInG InequALItIeS’ ImPACtS
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.
A new Dataset on educational Inequality
15 December 2010 DP 3
elena meschi
university of milan – DeAS
Department of quantitative Social Science – Institute of education
Francesco Scervini
university of milan – DeAS
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Elena Meschi and Francesco Scervini
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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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Elena Meschi and Francesco Scervini
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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, eij-silc, 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.
JEL Classification: I21, D30, Y1
Keywords: new dataset, educational attainment, educational inequality, dispersion indices, cross-country,
cohorts
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Elena Meschi and Francesco Scervini
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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
and Lindahl (2001); de la Fuente and Domenech (2006)), and beneficial to individuals and societies. From the
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,
well-being, 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 spill-over effects, generating
not only private, but also public benefits. Increasing education seems in fact to influence positively also social
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 cross-country measures of educational attainment and in-
equality, drawing on four international representative surveys (ess1, eu-silc2, 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
several birth cohorts. In particular we observe 13 cohorts, aggregated at 5-years intervals, where the first cohort is
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.
Beside the standard variable on years of education completed, it also contains information on highest qualification
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
and Lee (2010)). Finally, our dataset is the first one to provide information not only on educational level, but also
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
Measuring education in an international context is not straightforward, due to the difficult comparability of
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
effective attainment of any certificate/diploma/degree and it misleadingly accounts for year repetitions. Therefore,
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
qualification achieved, but it is suitable to compute inequality indices. Indeed, even if it is not properly continuous,
it spans from 0 to 25-30 years, depending on highest level educational institutions, with an acceptable level of
variability.
The second measure is based on attainment levels, capturing the highest qualification achieved. The advantage
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-
rization that disregards by definition intermediate cases, such as dropouts or partial attendance and furthermore,
being a categorical variable, it performs poorly in generating aggregation statistics measuring inequality. Another
important drawback of this measure in cross-country analyses is that levels, types and duration of specific edu-
cational programmes depend on the institutional structure of educational systems and, given the high degree of
differentiation of educational systems across countries and over time, it is difficult to construct a classification of
educational qualification that is valid and comparable internationally.
A common internationally harmonised measure of educational attainment is the International Standard Clas-
sification of Education (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
6 United Nations Educational, Scientific and Cultural Organisation7 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
particular, the classification distinguishes seven levels of education ranging from pre-primary education to second
stage of tertiary education. Mapping tables to link national educational programmes to these categories are avail-
able in oecd (1999), and eurostat (2005). However, although the international standards are clearly defined, it is
sometimes problematic for countries to integrate these standards with the national systems. In fact, as pointed out
by Eurydice studies8, not all school systems find univocal correspondence to 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 level 2. In other countries it is difficult
to differentiate between isced 3 and 4 because the certificates awarded are the same, and just the pathway taken
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
individuals when collecting data surveys, but they are derived from the answers they provide to country-specific
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
definitions established by unesco and oecd and different classification decisions were taken with respect to similar
educational programmes in different countries
In order to reduce measurement error due do imprecise coding, we create a simplified version of 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
Table 1: Official isced classification and correspondence to authors’ simplified version
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A New Dataset of Educational Inequality
Table 1: Official isced classification and correspondence to authors’ simplified version
IsceD 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
advantage of this measure is that these tests were specifically designed for cross-country comparison and are
therefore readily comparable across country. Moreover, test scores are continuous-like 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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Elena Meschi and Francesco Scervini
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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-
creasing the sample size for each country. In particular we create 13 cohorts at 5-years intervals. The first cohort
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
suitable observations, by trimming the sample over two dimensions in all the considered datasets: first of all, we
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
the level of education and increases its dispersion. We choose 25 years as a threshold as this is the standard defini-
tion of the beginning of adult age according to most of the literature in the field (see for instance Barro and Lee
(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 5-6 years of age and ending with a Ph.D., and
that additional years of education do not reflect real improvements but either misreported data or repeated years.
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,
the share of such individuals is so little that it is not expected to significantly affect the results.
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Elena Meschi and Francesco Scervini
Finally, we keep only the cells (country-cohort) 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 It consists of repeated cross-sections initiated in 2002/2003 (first round) and then carried
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-
tional attainment: actual (full-time equivalent) years of education, highest level of education in a country-specific
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 provides educational attainment country-specific format, we were able to correct for misclassifications of
country-specific variables into 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 (eu-silc)11 is a collection of timely and com-
parable multidimensional microdata covering EU countries plus Iceland and Norway.
eu-silc is a project developed by eurostat, run yearly since 2004 and including both cross-section
and longitudinal surveys. Since we use birth cohorts as time dimension and since education is time-invar-
iant, we are interested only in the cross-sectional element. Unfortunately, opposite to other surveys, for
eu-silc we cannot pull all the waves together, because even cross-sectional data contain a longitudinal di-
mension, and it is not possible to detect repeated observations in different cross-section files. This means
that merging different waves would result in including some individuals up to four times. Therefore we
had to select only one wave and, among the five available cross-sections, we chose the 2008 wave, be-
10 ess is a project started in 2001, directed by a consortium of seven academic institutions and funded by The European Commission, The 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 eu-silc consist in the wide country coverage (all eu-27 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 eu-silc, no cohorts are excluded either because of the low number of observations
or because of consistency issues. However, there is also a major shortcoming in the data: there is no information
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 French-speaking 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 Italian-speaking
region of Switzerland) participated in the third round of data collection in 1998. Our final 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 1970-1974 cohort, available for ten
countries only. Moreover, because of the small sample size for most countries, the first two cohorts (1920-1924
and 1925-1929) 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 socio-demographic 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 eu-silc 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 dataset, and are available only through the Australian Bureau of Statistics, for confi-
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
is defined as the knowledge and skills needed to understand and use information from texts including editorials,
news stories, poems, and fiction. Document literacy measures the knowledge and skills required to locate and
use information contained in various formats, including job applications, payroll forms, transportation schedules,
maps, tables, and graphics. Quantitative literacy is defined as the knowledge and skills required to apply arithmetic
operations (see ials User guide for further details). The results of the tests are scaled in the range of 0-500 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 cross-national 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 does not focus specifically on education and the main topic changes every year, its characteristics
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
it is not subject to recoding or rescaling and is therefore absolutely comparable both cross-country and over time.
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 non-European 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 top-coded (8-20 years for UK, 0-20 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-
viduals are Finland in the first cohort, Italy in the two youngest cohorts and Canada and Hungary in the last one.
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, the information 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 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. Extra-European 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
countries, the number of observations for some cohorts (typically the first or the last ones) is lower than 50 and we
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 difficult to compute aggregate measures representative of the real population and we therefore decided to keep the datasets separate.
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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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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:
● 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 Summary
Table 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 different cohorts.
5 VariablesFor each country and cohort, we calculate different 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 different surveys and calculated a unique summary statistics. However, since the foursurveys contain different individual weights, it would have been difficult 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 Summary
Table 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 different cohorts.
5 VariablesFor each country and cohort, we calculate different 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 different surveys and calculated a unique summary statistics. However, since the foursurveys contain different individual weights, it would have been difficult 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 Summary
Table 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 different cohorts.
5 VariablesFor each country and cohort, we calculate different 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 different surveys and calculated a unique summary statistics. However, since the foursurveys contain different individual weights, it would have been difficult 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 Summary
Table 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 different cohorts.
5 VariablesFor each country and cohort, we calculate different 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 different surveys and calculated a unique summary statistics. However, since the foursurveys contain different individual weights, it would have been difficult 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 Summary
Table 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 different cohorts.
5 VariablesFor each country and cohort, we calculate different 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 different surveys and calculated a unique summary statistics. However, since the foursurveys contain different individual weights, it would have been difficult to compute aggregate measuresrepresentative of the real population and we therefore decided to keep the datasets separate.
10
individuals in the population;
● 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 σx =
√∑i(xifi−µx)
2
N−1, a “standardised” measure of the variance
of a variable;
• Coefficient of variation: cvx = σx
µx, 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 (α) = 1α2−α
[1N
∑i
(xi
µ
)α
− 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 α, we choose themore frequently used:
– Theil index, with α = 1: Tx = 1N
∑i
(xi
µln xi
µ
), introduced by Theil (1967)
and extensively described among others by Conceicao and Ferreira (2000),
– Mean logarithmic deviation (MLD), with α = 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−εi
) 11−ε (ε = 0.5, 1, 2), a measure that allows
different sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:
Ax = 1− 1
µ
(1
N
∑i
x1−εi
) 11−ε
=
1− 1µ
(1N
∑i
√xi
)2 if ε = 0.5
1−∏
ixi
µif ε = 1
1− µh
µif ε = 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 σx =
√∑i(xifi−µx)
2
N−1, a “standardised” measure of the variance
of a variable;
• Coefficient of variation: cvx = σx
µx, 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 (α) = 1α2−α
[1N
∑i
(xi
µ
)α
− 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 α, we choose themore frequently used:
– Theil index, with α = 1: Tx = 1N
∑i
(xi
µln xi
µ
), introduced by Theil (1967)
and extensively described among others by Conceicao and Ferreira (2000),
– Mean logarithmic deviation (MLD), with α = 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−εi
) 11−ε (ε = 0.5, 1, 2), a measure that allows
different sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:
Ax = 1− 1
µ
(1
N
∑i
x1−εi
) 11−ε
=
1− 1µ
(1N
∑i
√xi
)2 if ε = 0.5
1−∏
ixi
µif ε = 1
1− µh
µif ε = 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):
● 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 σx =
√∑i(xifi−µx)
2
N−1, a “standardised” measure of the variance
of a variable;
• Coefficient of variation: cvx = σx
µx, 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 (α) = 1α2−α
[1N
∑i
(xi
µ
)α
− 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 α, we choose themore frequently used:
– Theil index, with α = 1: Tx = 1N
∑i
(xi
µln xi
µ
), introduced by Theil (1967)
and extensively described among others by Conceicao and Ferreira (2000),
– Mean logarithmic deviation (MLD), with α = 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−εi
) 11−ε (ε = 0.5, 1, 2), a measure that allows
different sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:
Ax = 1− 1
µ
(1
N
∑i
x1−εi
) 11−ε
=
1− 1µ
(1N
∑i
√xi
)2 if ε = 0.5
1−∏
ixi
µif ε = 1
1− µh
µif ε = 2
where µh is the harmonic mean.
12
, a “standardised” measure of the variance of a variable;
● Coefficient of variation:
• 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 σx =
√∑i(xifi−µx)
2
N−1, a “standardised” measure of the variance
of a variable;
• Coefficient of variation: cvx = σx
µx, 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 (α) = 1α2−α
[1N
∑i
(xi
µ
)α
− 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 α, we choose themore frequently used:
– Theil index, with α = 1: Tx = 1N
∑i
(xi
µln xi
µ
), introduced by Theil (1967)
and extensively described among others by Conceicao and Ferreira (2000),
– Mean logarithmic deviation (MLD), with α = 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−εi
) 11−ε (ε = 0.5, 1, 2), a measure that allows
different sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:
Ax = 1− 1
µ
(1
N
∑i
x1−εi
) 11−ε
=
1− 1µ
(1N
∑i
√xi
)2 if ε = 0.5
1−∏
ixi
µif ε = 1
1− µh
µif ε = 2
where µh is the harmonic mean.
12
, the standard deviation normalised by the mean;
● 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 σx =
√∑i(xifi−µx)
2
N−1, a “standardised” measure of the variance
of a variable;
• Coefficient of variation: cvx = σx
µx, 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 (α) = 1α2−α
[1N
∑i
(xi
µ
)α
− 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 α, we choose themore frequently used:
– Theil index, with α = 1: Tx = 1N
∑i
(xi
µln xi
µ
), introduced by Theil (1967)
and extensively described among others by Conceicao and Ferreira (2000),
– Mean logarithmic deviation (MLD), with α = 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−εi
) 11−ε (ε = 0.5, 1, 2), a measure that allows
different sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:
Ax = 1− 1
µ
(1
N
∑i
x1−εi
) 11−ε
=
1− 1µ
(1N
∑i
√xi
)2 if ε = 0.5
1−∏
ixi
µif ε = 1
1− µh
µif ε = 2
where µh is the harmonic mean.
12
, the most used inequality index in the literature
● 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 σx =
√∑i(xifi−µx)
2
N−1, a “standardised” measure of the variance
of a variable;
• Coefficient of variation: cvx = σx
µx, 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 (α) = 1α2−α
[1N
∑i
(xi
µ
)α
− 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 α, we choose themore frequently used:
– Theil index, with α = 1: Tx = 1N
∑i
(xi
µln xi
µ
), introduced by Theil (1967)
and extensively described among others by Conceicao and Ferreira (2000),
– Mean logarithmic deviation (MLD), with α = 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−εi
) 11−ε (ε = 0.5, 1, 2), a measure that allows
different sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:
Ax = 1− 1
µ
(1
N
∑i
x1−εi
) 11−ε
=
1− 1µ
(1N
∑i
√xi
)2 if ε = 0.5
1−∏
ixi
µif ε = 1
1− µh
µif ε = 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 σx =
√∑i(xifi−µx)
2
N−1, a “standardised” measure of the variance
of a variable;
• Coefficient of variation: cvx = σx
µx, 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 (α) = 1α2−α
[1N
∑i
(xi
µ
)α
− 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 α, we choose themore frequently used:
– Theil index, with α = 1: Tx = 1N
∑i
(xi
µln xi
µ
), introduced by Theil (1967)
and extensively described among others by Conceicao and Ferreira (2000),
– Mean logarithmic deviation (MLD), with α = 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−εi
) 11−ε (ε = 0.5, 1, 2), a measure that allows
different sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:
Ax = 1− 1
µ
(1
N
∑i
x1−εi
) 11−ε
=
1− 1µ
(1N
∑i
√xi
)2 if ε = 0.5
1−∏
ixi
µif ε = 1
1− µh
µif ε = 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 σx =
√∑i(xifi−µx)
2
N−1, a “standardised” measure of the variance
of a variable;
• Coefficient of variation: cvx = σx
µx, 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 (α) = 1α2−α
[1N
∑i
(xi
µ
)α
− 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 α, we choose themore frequently used:
– Theil index, with α = 1: Tx = 1N
∑i
(xi
µln xi
µ
), introduced by Theil (1967)
and extensively described among others by Conceicao and Ferreira (2000),
– Mean logarithmic deviation (MLD), with α = 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−εi
) 11−ε (ε = 0.5, 1, 2), a measure that allows
different sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:
Ax = 1− 1
µ
(1
N
∑i
x1−εi
) 11−ε
=
1− 1µ
(1N
∑i
√xi
)2 if ε = 0.5
1−∏
ixi
µif ε = 1
1− µh
µif ε = 2
where µh is the harmonic mean.
12
, analogous to the Theil
index, but characterized by a lower sensitivity index;
Page • 24
Elena Meschi and Francesco Scervini
● 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 σx =
√∑i(xifi−µx)
2
N−1, a “standardised” measure of the variance
of a variable;
• Coefficient of variation: cvx = σx
µx, 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 (α) = 1α2−α
[1N
∑i
(xi
µ
)α
− 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 α, we choose themore frequently used:
– Theil index, with α = 1: Tx = 1N
∑i
(xi
µln xi
µ
), introduced by Theil (1967)
and extensively described among others by Conceicao and Ferreira (2000),
– Mean logarithmic deviation (MLD), with α = 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−εi
) 11−ε (ε = 0.5, 1, 2), a measure that allows
different sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:
Ax = 1− 1
µ
(1
N
∑i
x1−εi
) 11−ε
=
1− 1µ
(1N
∑i
√xi
)2 if ε = 0.5
1−∏
ixi
µif ε = 1
1− µh
µif ε = 2
where µh is the harmonic mean.
12
, a measure that allows different sensi-
tivities to transfers to the low tail of the distribution. The formula above simplifies as follows:
• 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 σx =
√∑i(xifi−µx)
2
N−1, a “standardised” measure of the variance
of a variable;
• Coefficient of variation: cvx = σx
µx, 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 (α) = 1α2−α
[1N
∑i
(xi
µ
)α
− 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 α, we choose themore frequently used:
– Theil index, with α = 1: Tx = 1N
∑i
(xi
µln xi
µ
), introduced by Theil (1967)
and extensively described among others by Conceicao and Ferreira (2000),
– Mean logarithmic deviation (MLD), with α = 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−εi
) 11−ε (ε = 0.5, 1, 2), a measure that allows
different sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:
Ax = 1− 1
µ
(1
N
∑i
x1−εi
) 11−ε
=
1− 1µ
(1N
∑i
√xi
)2 if ε = 0.5
1−∏
ixi
µif ε = 1
1− µh
µif ε = 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 σx =
√∑i(xifi−µx)
2
N−1, a “standardised” measure of the variance
of a variable;
• Coefficient of variation: cvx = σx
µx, 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 (α) = 1α2−α
[1N
∑i
(xi
µ
)α
− 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 α, we choose themore frequently used:
– Theil index, with α = 1: Tx = 1N
∑i
(xi
µln xi
µ
), introduced by Theil (1967)
and extensively described among others by Conceicao and Ferreira (2000),
– Mean logarithmic deviation (MLD), with α = 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−εi
) 11−ε (ε = 0.5, 1, 2), a measure that allows
different sensitivities to transfers to the low tail of the distribution. The formulaabove simplifies as follows:
Ax = 1− 1
µ
(1
N
∑i
x1−εi
) 11−ε
=
1− 1µ
(1N
∑i
√xi
)2 if ε = 0.5
1−∏
ixi
µif ε = 1
1− µh
µif ε = 2
where µh is the harmonic mean.
12
is the harmonic mean.
● 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.
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
in the graph corresponds to a specific cell (country and cohort), calculated using the survey indicated in the axes
and the 45 degree line represents the perfect equality of the computed measures.
If we look at figure 1 that analyses the consistency of the average years of schooling, we notice that we man-
aged to achieve a good consistency across surveys, as all the points are substantially aligned around the 45 degree
line. The same consideration applies to all the statistics based on years of schooling (see figures 2 to 20) which
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, eu-silc
or ials.
This evidence confirms the difficult cross-country comparability of educational attainment, whose categorisa-
tion depends on the specific institutional features of national school systems. Even if an internationally harmonised
measure has been used, the classification of educational levels may still be subject to some measurement error. On
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.
Page • 26
Elena Meschi and Francesco Scervini
Page • 27
A New Dataset of Educational Inequality
references
Barro, Robert J, and Jong-Wha Lee (1996) ‘International measures of schooling years and schooling quality.’ American Economic Review 86(2), 218-23
Barro, Robert J., and Jong-Wha Lee (2001) ‘International data on educational attainment. updates and implica-tions.’ Oxford Economic Papers 3, 541-563
_ (2010) ‘A new data set of educational attainment in the world, 1950-2010.’ NBER Working Papers 15902, Na-tional 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 Eco-nomic Growth 12(1), 51-76
Conceicao, Pedro, and Pedro M. Ferreira (2000) ‘The young person’s guide to the theil index: Suggesting in-tuitive 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 Jong-Wha Lee (2002) ‘Education and income inequality: New evidence from cross-coun-try data.’ Review of Income and Wealth 48(3), 395-416
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), 1-36
eurostat (2005) UOE-UNESCO-OECD-EUrOStat data collection on education: Mapping of national education programmes to isced-97 UOE-unesco-oecd-eurostat
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, labor-force quality, and the growth of nations.’ American Economic Review 90(5), 1184-1208
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), 115-156
Krueger, Alan B., and Mikael Lindahl (2001) ‘Education for growth: Why and for whom?’ Journal ofEconomic Literature 39(4), 1101-1136
Lochner, Lance, and Enrico Moretti (2004) ‘The effect of education on crime: Evidence from prison inmates, ar-rests, and self-reports.’ American Economic Review 94(1), 155189
Lopez, Ramon, Vinod Thomas, and Yan Wang (1998) ‘Addressing the education puzzle : the distribution of educa-tion 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(9-10), 1667-1695
Page • 28
Elena Meschi and Francesco Scervini
oecd (1999) Classifying educational programmes. Manual for iSCED-97 implementation in OECD countries oecd (Paris)
Park, Kang H. (1996) ‘Educational expansion and educational inequality on income distribution.’ Economics of Education Review 15(1), 51-58
Schneider, Silke L. (2007) ‘Measuring educational attainment in cross-national surveys: The case of the European Social Survey.’ Paper presented at the educ research group workshop of the equalsoc network
_ (2009) ‘Confusing credentials: The cross-nationally comparable measurement of educational attainment.’ PhD dissertation, Nuffield College, Oxford: University of Oxford
_ (2010) ‘Nominal comparability is not enough: (in-)equivalence of construct validity of cross-national measures of educational attainment in the european social survey.’ Research in Social Stratification and Mobility 28(3), 343 - 357
Theil, Henri (1967) Economics and Information Theory (Amsterdam: North Holland)
Thomas, Vinod, Yan Wang, and Xibo Fan (2001) ‘Measuring education inequality -Gini coefficients of education.’ Policy Research Working Paper Series 2525, The World Bank, January
unesco (2006) Global education digest 2006. Comparing education statistics across the world Institute for Sta-tistics (Montreal)
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)
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Years of education - Mean
16
Figure 2Figure 2
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Years of education - St. Dev.
Figure 3
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17
Page • 30
Elena Meschi and Francesco Scervini
Figure 3
Figure 2
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4.55
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2 2.5 3 3.5 4 4.5 5 5.5ESS
CZCZCZCZCZ CZ
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53
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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
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SP
0 .1 .2 .3 .4 .5 .6ESS
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LS
0 .1 .2 .3 .4 .5 .6ESS
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
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SKSKSKSKSKSKSKSKSKSKSKSKSK
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SESESESESESESE CHCHCHCHCHCHCHCHCHCHCHCHCH
0.0
5.1
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5IS
SP
0 .05 .1 .15 .2 .25ESS
CZCZCZCZCZCZCZCZCZ
DKDKDKDKDKDKDKDKDK
FIFIFIFIFI
FIFIFIFIDEDE
DEDEDEDEDEDE
HUHUHUHUHUHU
HUHUHU
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IT
ITITIT
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SESE
SESESESESE
CHCHCHCHCHCHCHCHCHUKUKUKUKUKUKUKUKUK
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
DKDKDKDKDKDKDKDKDK
FIFIFI FI
FI FIFIFIFIDEDE
DE DEDEDEDEDE
HUHUHU HUHUHUHUHUHUIEIE IEIEIE IEIEIE
ITIT IT
ITITIT
ITITITNLNL NL NLNLNLNL
NLNLNLNONONONONONONONONO
PLPLPLPL PLPLPLPL
SISI
SI SISI SI SISISI
SE SESE SESE SE
SESESE SECHCHCH CHCHCH CHCHCH
0.0
5.1
.15
.2IS
SP
0.05.1.15.2IALS
ATATATATATATATATATATATAT
AT
BGBG
BGBG
BGBGBGBGBGBGBGBG
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FIFIFIFIFI
FIFIFIFIFIFI
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DEDEDE
DE
DEDEDEDEDEDEDEDEDE
HUHUHUHUHUHUHUHUHUHUHUHU
IE IEIEIEIEIEIEIEIEIE IEIEIE
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NONONONONONONONONONONONONO
PL PLPL
PLPLPLPLPLPLPLPLPLPL
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PTPT
PTPT PTPTPT
PTPT
PTPT
SKSKSKSKSK
SKSKSKSKSKSKSKSK
SISI
SISI
SISISISISISISISI SI
ESES
ESES
ESESESES
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SESESESESESE
SESESESESESESECHCHCHCHCHCHCHCHCHCHCHCHCH
0.0
5.1
.15
.2IS
SP
0 .05 .1 .15 .2ESS
CZCZCZCZCZCZCZCZCZ
DKDKDKDKDKDKDKDKDK
FIFIFIFIFI
FIFIFIFIDEDE
DE
DEDEDEDEDE
HUHUHU
HUHUHUHUHUHUIEIE
IEIEIEIEIEIE
IT
ITIT
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SISI
SISISISISISISI
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SESESE
SESESESESE
CHCHCHCHCHCH
CHCHCHUKUKUKUKUKUKUKUKUK
0.0
5.1
.15
.2IA
LS
0 .05 .1 .15 .2ESS
Years of education - Theil
18
Page • 31
A New Dataset of Educational Inequality
Figure 5
Figure 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
ITIT IT
ITIT
IT ITITITNLNLNL NLNLNLNL NLNLNLNONONONONONONONONO
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SI SISI SI SISISI
SE SESE SESE SE
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0.0
5.1
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5IS
SP
0.05.1.15.2.25IALS
ATATATATATATATATATATATATAT
BGBG
BGBG
BGBGBGBGBGBGBG
BG
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FIFIFIFIFIFIFIFIFIFI
FI
FI FRFRFRFRFRFRFRFRFRFRFRFRFRDEDE
DEDE
DEDEDEDEDEDEDEDEDE
HUHUHUHUHUHUHUHUHUHUHUHUIE IEIEIEIEIEIEIEIEIE IEIEIE
ITITITITIT
ITIT
ITITIT IT
LVLV
LVLVLVLVLVLVLVLVLVLV
NLNL NLNLNLNLNLNLNLNLNLNLNLNONONONONONONONONONONONONO
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PTPTPT
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SISI
SISI
SISISISISISISISI SI
ESES ESES
ESESES
ESESESESESES
SESESESESESE
SESESESESESESE CHCHCHCHCHCHCHCHCHCHCHCHCH
0.0
5.1
.15
.2.2
5IS
SP
0 .05 .1 .15 .2 .25ESS
CZCZCZCZCZCZCZCZCZ
DKDKDKDKDKDKDKDKDK
FIFIFIFIFI
FIFIFIFIDEDE
DEDEDEDEDEDE
HUHUHUHUHUHU
HUHUHU
IEIEIEIEIEIEIEIE
IT
ITITIT
ITIT
ITIT ITNLNL NLNLNLNL
NLNLNLNL NONONONONONONONONO
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PLPLPLPLPLPL
SISISISISISISISISI
SESESE
SESE
SESESESESE
CHCHCHCHCHCHCHCHCHUKUKUKUKUKUKUKUKUK
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
DKDKDKDKDKDKDKDKDK
FIFIFI FI
FI FIFIFIFIDEDE
DE DEDEDEDEDE
HUHUHU HUHUHUHUHUHUIEIE IEIEIE IEIEIE
ITIT IT
ITITIT
ITITITNLNL NL NLNLNLNL
NLNLNLNONONONONONONONONO
PLPLPLPL PLPLPLPL
SISI
SI SISI SI SISISI
SE SESE SESE SE
SESESE SECHCHCH CHCHCH CHCHCH
0.0
5.1
.15
.2IS
SP
0.05.1.15.2IALS
ATATATATATATATATATATATAT
AT
BGBG
BGBG
BGBGBGBGBGBGBGBG
CZCZCZCZCZCZCZCZCZCZCZCZCZ
DKDKDKDKDKDKDKDKDKDKDKDKDK
FIFIFIFIFI
FIFIFIFIFIFI
FIFRFRFRFRFRFRFRFRFRFRFRFRFR
DEDEDE
DE
DEDEDEDEDEDEDEDEDE
HUHUHUHUHUHUHUHUHUHUHUHU
IE IEIEIEIEIEIEIEIEIE IEIEIE
ITITIT
ITITITITIT
ITIT IT
LVLV
LVLVLVLVLVLVLVLVLVLV
NLNL NLNLNLNLNLNLNLNLNLNLNL
NONONONONONONONONONONONONO
PL PLPL
PLPLPLPLPLPLPLPLPLPL
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PTPT
PTPT PTPTPT
PTPT
PTPT
SKSKSKSKSK
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SISI
SISI
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ESES
ESES
ESESESES
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SESESESESESE
SESESESESESESECHCHCHCHCHCHCHCHCHCHCHCHCH
0.0
5.1
.15
.2IS
SP
0 .05 .1 .15 .2ESS
CZCZCZCZCZCZCZCZCZ
DKDKDKDKDKDKDKDKDK
FIFIFIFIFI
FIFIFIFIDEDE
DE
DEDEDEDEDE
HUHUHU
HUHUHUHUHUHUIEIE
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IT
ITIT
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5.1
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0 .05 .1 .15 .2ESS
Years of education - Theil
18
Figure 6Figure 6
BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)CZCZCZCZCZCZCZCZCZ
DKDKDKDKDKDKDKDKDK
FIFIFIFIFI FIFIFIFIDEDE
DE DEDEDEDEDEHUHUHU HUHUHUHUHUHU
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IT IT ITITITIT ITITITNLNLNLNLNLNLNLNLNLNLNONONONONONONONONOPL PLPLPLPLPLPLPL
SISISISISISISISISI
SE SESESESE SESESESESECHCHCHCHCHCHCHCHCH
0.2
5.5
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11.
25IS
SP
0.25.5.7511.25IALS
ATATATATATATATATATATATATAT
BGBGBGBGBGBGBGBGBGBGBGBGCZCZCZCZCZCZCZCZCZCZCZCZCZ
DKDKDKDKDKDKDKDKDKDKDKDKDK
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DEDEDEDE
DEDEDEDEDEDEDEDEDEHUHUHUHUHUHUHUHUHUHUHUHU
IE IEIEIEIEIEIEIEIEIEIEIEIE
ITITITITITITITITITITITLVLV
LVLVLVLVLVLVLVLVLVLV
NLNLNLNLNLNLNLNLNLNLNLNLNLNONONONONONONONONONONONONO
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SISISISISISISISISISISISISI
ESESESESES
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SESESESESESESESESESESESESECHCHCHCHCHCHCHCHCHCHCHCHCH
0.2
5.5
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11.
25IS
SP
0 .25 .5 .75 1 1.25ESS
CZCZCZCZCZCZCZCZCZDKDKDKDKDKDKDKDKDK
FIFIFIFIFIFIFIFIFIDEDEDE
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IT
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0.2
5.5
.75
11.
25IA
LS
0 .25 .5 .75 1 1.25ESS
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
DKDKDKDKDKDKDKDKDK
FIFIFI FI
FI FIFIFIFI
DEDE
DE DEDEDEDEDE
HUHUHU HUHUHUHUHUHU
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ITIT IT
ITIT
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NLNL NL NLNLNLNLNLNLNLNONONONONONONONONO
PLPL PLPL PLPLPLPL
SI
SISI SISI SI SISISI
SE SESE SESE SE
SESESE SECHCHCH CHCHCH CHCHCH
0.0
2.0
4.0
6.0
8.1
ISSP
0.02.04.06.08.1IALS
ATATATATATATATATATATATAT
AT
BGBG
BGBG
BGBGBGBGBGBGBG
BG
CZCZCZCZCZCZCZCZCZCZCZCZCZ
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FIFIFIFIFI
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FI FRFRFRFRFRFRFRFRFRFRFRFRFR
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DE
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0.0
2.0
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ISSP
0 .02 .04 .06 .08 .1ESS
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0 .02 .04 .06 .08 .1ESS
Years of education - Atkinson (0.5)
19
Page • 32
Elena Meschi and Francesco Scervini
Figure 7
Figure 6
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 - 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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0.02.04.06.08.1IALS
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2.0
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0 .02 .04 .06 .08 .1ESS
Years of education - Atkinson (0.5)
19
Figure 8Figure 8
BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)CZ CZCZCZCZCZCZCZCZ
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0.03.06.09.12.15.18IALS
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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
.2.3
.4IA
LS
0 .1 .2 .3 .4ESS
Years of education - Atkinson (2)
20
Page • 33
A New Dataset of Educational Inequality
Figure 9
Figure 8
BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)BE(FL)CZ CZCZCZCZCZCZCZCZ
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0.0
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0.03.06.09.12.15.18IALS
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0.0
3.0
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SP
0 .03 .06 .09 .12 .15 .18ESS
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0.0
3.0
6.0
9.1
2.1
5.1
8IA
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
DKDKDKDKDKDKDK
DKDK
FIFIFI FIFI FIFI
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DEDEDE
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ITIT IT
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SISI SISI SI
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0.1
.2.3
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SP
0.1.2.3.4IALS
ATATATATATATATATATATATAT
AT
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BGBG
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BG
BG
CZCZCZCZCZCZCZCZCZCZCZCZCZ
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FIFIFIFI
FI
FI
FI
FRFRFRFRFRFRFRFRFRFRFRFRFR
DEDEDE
DE
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HU HUHUHUHUHUHUHUHUHUHUHUIE IE IE IEIEIE IE IEIEIE IEIEIE
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UKUK
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0.1
.2.3
.4IA
LS
0 .1 .2 .3 .4ESS
Years of education - Atkinson (2)
20
Page • 34
Elena Meschi and Francesco Scervini
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
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NONONONONONONONO
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PL
PLPLPLPLPLPL PL
SISISISI
SISISISISISESESESESESE
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04
812
1620
ISSP
048121620IALS
ATATATATATATATATATATATATAT
BGBGBG BGBGBGBGBGBGBGBGBGCZCZCZCZCZ
CZCZCZCZCZCZCZCZ
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FIFI FIFI FIFI FI
FI FIFI
FI
FI
FRFRFRFRFRFRFRFRFRFR
FRFRFR
DEDEDEDEDEDEDEDEDE
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LVLVLVLVLVLVLV
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SISISI SISISISISISISISI
SISI
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ES ESES
ESESESESES
SESESESESE SESESESESE
SE SESE
CHCHCHCHCHCHCHCHCHCHCHCHCH
04
812
1620
ISSP
0 4 8 12 16 20ESS
CZCZCZCZCZCZCZCZCZ
DKDKDKDKDKDK
DKDKDK
FI FIFIFIFI FI
FI FIFI
DEDEDEDEDEDEDEDE
HUHUHUHUHUHUHUHUHUIEIEIEIEIE IE
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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
FIFIFIFIFIFI
FIFIFI
DEDEDEDEDEDEDE
DEHU
HUHUHUHUHUHUHUHU
IEIEIEIEIEIEIE
IE
ITITITIT
ITITITITIT
NLNLNLNLNLNLNLNL
NLNL
NONONONONONONONONO
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SISISISISISI
SISISI
SESESESESESESESE
SESECHCHCHCHCHCHCHCHCH
04
812
1620
ISSP
048121620IALS
ATATATATATAT
ATAT
ATATATATAT
BGBGBGBGBG
BGBGBGBGBG
BGBG
CZCZCZCZCZ
CZCZCZCZCZCZCZCZ
DKDKDKDKDKDKDKDK
DKDKDKDKDK
FIFI FI FIFIFI FI
FI FIFIFIFI
FRFRFR FRFRFRFR FR
FRFRFRFRFR
DEDEDEDEDEDEDEDEDEDEDEDEDE
HUHUHUHUHUHUHUHU
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CHCHCHCHCHCHCHCHCHCHCHCHCH
04
812
1620
ISSP
0 4 8 12 16 20ESS
CZCZCZCZCZ
CZCZCZCZ
DKDKDK
DK DKDKDKDKDK
FI FIFIFI FI
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DEDEDEDEDEDEDEDE
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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
DEDEDEDEDEDEDEDE
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IEIEIEIEIEIE
IEIE
ITITITITITITIT
ITIT
NLNLNLNLNLNLNL
NLNLNL
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SISISISISISI
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04
812
1620
ISSP
048121620IALS
ATATATAT
ATATATATAT
ATATATAT
BGBGBGBGBGBG
BGBGBGBGBGBG
CZCZCZ CZCZ
CZCZCZCZCZCZCZCZ
DKDKDKDKDKDK
DKDKDKDKDKDKDK
FIFIFIFI FI
FI FIFIFI FIFIFI
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DEDEDE DEDEDEDEDEDEDEDEDE
DE
HUHU HUHUHUHUHUHUHUHUHUHU
IEIE IEIEIEIE IE IE
IEIE IEIEIE
ITITITITITIT ITITIT
ITIT
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NLNLNLNLNLNLNL
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NONONONONONONONONONONONONO
PLPL PLPLPL PLPLPLPLPLPLPL PL
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SISISI SISISISISI
SISISI SISI
ESESESES ES
ES ESESESESESES
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SESESE SESESE
CHCHCHCHCHCHCHCHCHCHCHCHCH
04
812
1620
ISSP
0 4 8 12 16 20ESS
CZ CZCZCZ
CZCZCZCZCZ
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DE DEDEDEDEDEDEDE
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UKUKUKUKUKUKUKUKUK
04
812
1620
IALS
0 4 8 12 16 20ESS
Years of education - Percentile 25
22
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)
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DEDEDEDEDEDEDE
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IE
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04
812
1620
ISSP
048121620IALS
ATATATATATAT
ATAT
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04
812
1620
ISSP
0 4 8 12 16 20ESS
CZCZCZCZCZ
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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
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FIFIFIFIFI
DEDEDEDEDEDEDEDE
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04
812
1620
ISSP
048121620IALS
ATATATAT
ATATATATAT
ATATATAT
BGBGBGBGBGBG
BGBGBGBGBGBG
CZCZCZ CZCZ
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FIFIFIFI FI
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DEDEDE DEDEDEDEDEDEDEDEDE
DE
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CHCHCHCHCHCHCHCHCHCHCHCHCH
04
812
1620
ISSP
0 4 8 12 16 20ESS
CZ CZCZCZ
CZCZCZCZCZ
DKDKDK
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FI FIFI FI FI
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DE DEDEDEDEDEDEDE
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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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DKDKDKDKDK
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FIFIFIFI
DEDEDEDEDEDEDEDE
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IEIE
ITITIT
ITITITITIT
IT
NLNLNLNLNLNLNL
NLNLNL
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SISISISISI
SISISISI
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04
812
1620
ISSP
048121620IALS
ATATATAT
ATATATATATATATATAT
BGBGBGBGBG
BGBGBGBGBGBGBGCZCZCZCZCZ
CZCZCZCZCZCZCZCZ
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DKDKDKDKDKDKDK
FI FI FIFIFI FI
FIFIFI FI FIFI
FRFRFRFRFRFR
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DEDEDEDEDEDEDEDEDEDEDE
DEDE
HU HUHUHUHU
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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
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
DKDKDKDK
DKDKDKDKDK
FIFIFIFIFI
FIFIFIFI
DEDEDEDEDEDEDEDE
HUHUHUHU
HUHUHUHUHU
IEIEIEIEIEIE
IEIE
ITITIT
ITITITITIT
IT
NLNLNLNLNLNLNL
NLNLNL
NONONONONONONONONO
PLPLPLPLPLPLPLPL
SISISISISI
SISISISI
SESESESESESE
SESESESECHCHCHCHCHCHCHCHCH
04
812
1620
ISSP
048121620IALS
ATATATAT
ATATATATATATATATAT
BGBGBGBGBG
BGBGBGBGBGBGBGCZCZCZCZCZ
CZCZCZCZCZCZCZCZ
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
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
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
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
NONONO
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
CHCHCHCHCHCHCHCHCH
04
812
1620
ISSP
048121620IALS
ATATATATATAT
ATATATATAT
ATATBGBGBGBGBGBGBGBGBGBGBG
BG
CZCZCZ
CZCZCZCZCZCZCZCZCZCZDKDKDKDKDKDK
DKDKDKDKDKDKDK
FIFI FI
FI FI FIFI FIFIFIFIFI
FRFRFR FRFRFRFRFRFRFRFRFRFR
DEDE
DEDEDEDEDE
DEDEDEDEDEDE
HUHUHUHU HUHUHUHUHUHUHU
HUIE IEIE
IEIEIEIE IEIEIEIE IEIE
IT ITITIT
ITITITITITIT
ITLVLVLVLVLVLVLVLVLVLVLVLV
NLNLNLNLNLNLNL
NLNLNLNLNLNL
NONONONO
NONONONONONONONONO
PL PLPLPL PLPLPLPLPLPL
PL PLPL
PTPTPTPTPTPT
PTPT
PTPTPT PT
PT
SK SKSKSKSKSKSKSKSKSKSKSK
SK
SISISI SI SISISISI
SISISI
SI SI
ESESESESES
ESESESES
ESESESES
SESESE SE
SESESESESE SESESESE
CHCHCHCHCHCHCHCHCHCHCHCHCH
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
NLNL NLNLNLNLNLNLNL
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
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
CZDKDKDKDKDKDK
DKDKDK
FIFI
FIFIFIFIFIFIFI
DEDEDE
DEDEDEDE
DEHUHUHUHUHUHUHU HUHU IEIEIEIEIEIEIEIE
ITIT
ITITITITITIT
IT
NLNLNLNLNLNLNLNLNL
NL
NONONONONONONONONO
PLPLPLPLPLPLPLPL SISISISISISISISI
SI
SESESESE
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04
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Years of education - Percentile 70
Figure 18
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Years of education - Percentile 75
25
Figure 19Figure 19
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Years of education - Percentile 80
Figure 20
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812
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0 4 8 12 16 20ESS
Years of education - Percentile 90
26
Page • 40
Elena Meschi and Francesco Scervini
Figure 20
Figure 19
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1620
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0 4 8 12 16 20ESS
Years of education - Percentile 80
Figure 20
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812
1620
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0 4 8 12 16 20ESS
Years of education - Percentile 90
26
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
Figure 22
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28
Page • 42
Elena Meschi and Francesco Scervini
Figure 23
Figure 22
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28
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
Page • 44
Elena Meschi and Francesco Scervini
Page • 45
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:
● exploit differences between and within 29 countries in inequality levels and trends for understanding the impacts and teasing out implications for policy and institutions,
● elaborate on the effects of both individual distributional positions and aggregate inequalities, and
● 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 the current financial and economic crisis will be incorporated. Politico-cultural impacts investigated are: Do 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 do political systems (coalitions/winner-takes-all) have? Finally, it focuses on costs and benefits of policies limiting income inequality and its effi ciency for mitigating other inequalities (health, housing, education 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 main reports and a final report, some 70 discussion papers and 29 country reports. The start of the project is 1 February 2010 for a three-year period. Detailed information can be found on the website.
www.gini-research.org
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
Project funded under the Socio-Economic sciencesand Humanities theme.