Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality?

Abstract

Lack of comparable cross-country data on access to and participation into higher education (HE) among disadvantaged and marginalised communities prevents a comprehensive examination of the role of education in shaping social mobility and how this has changed following educational expansions. We use data from the OECD Survey of Adult Skills, the Programme for International Student Assessment as well as administrative and census data from several countries to provide a comprehensive cross-country overview of the relationship between, on the one hand, socio-economic background, migrant background, and place of residence, and on the other hand, HE expectations, participation and completion. We find that when a higher share of the population has access to higher education, inequalities in access and completion are lower, but inequalities in skill levels remain unchanged. This could be due to the varying degree of inequality observed at different levels of higher education; as well as to the differences in the aspirations secondary school students express of enrolling and completing HE. We discuss implications for research and policy.

Full text

Open Education Studies, 2020; 2: 312343
Research Article
Francesca Borgonovi, Gabriele Marconi*
Inequality in Higher Education: Why Did Expanding
Access Not Reduce Skill Inequality?
https://doi.org/10.1515/edu-2020-0110
received May 2, 2020; accepted November 30, 2020.
Abstract: Lack of comparable cross-country data on
access to and participation into higher education (HE)
among disadvantaged and marginalised communities
prevents a comprehensive examination of the role of
education in shaping social mobility and how this has
changed following educational expansions. We use data
from the OECD Survey of Adult Skills, the Programme
for International Student Assessment as well as
administrative and census data from several countries to
provide a comprehensive cross-country overview of the
relationship between, on the one hand, socio-economic
background, migrant background, and place of residence,
and on the other hand, HE expectations, participation
and completion. We find that when a higher share of the
population has access to higher education, inequalities in
access and completion are lower, but inequalities in skill
levels remain unchanged. This could be due to the varying
degree of inequality observed at different levels of higher
education; as well as to the differences in the aspirations
secondary school students express of enrolling and
completing HE. We discuss implications for research and
policy.
Keywords: higher education; inequality; access; socio-
economic background; international data; geography;
migration; skills .
1 Introduction
Many countries have experienced increasing income
and wealth inequality in the past decades. For example,
available data show that wealth is unevenly distributed
and income inequality within countries is at its highest
level in over 30 years (OECD, 2015; Saez & Zucman,
2016). Increases in inequality harm economic growth and
undermine social cohesion (Alesina and La Ferrara, 2002;
Barro, 2000; Kawachi etal., 1997). Inequality also limits
social mobility, meaning that individuals from low-income
families have few chances of moving up the social ladder
while those from privileged families keep their wealth and
privilege (Corak, 2013).
Large increases in income and wealth inequality
occurred at a time when in most OECD countries
educational opportunities were greatly expanded. The
expansion in educational opportunities in the past
decades in OECD countries was driven by the recognition
that higher levels of skills increase productivity and
therefore educational investments can promote economic
growth (Hanushek & Wössmann, 2010). However, such
expansions were also driven by the belief that increases in
educational opportunities would reduce socio-economic
differentials in labour market outcomes and life chances,
thus promoting social mobility (Marks & McMillan, 2004).
The contribution of this paper is twofold. It maps
inequalities on access and completion of higher education
across countries by parental education, immigrant status
and place of residence (rural vs. urban), both using new
data and exploring existing data in new ways. In addition,
it puts these inequalities in relation to the expansion of
participation in higher education that occurred in the past
in many countries.
The expansion of educational opportunities that
occurred in recent decades does not appear to have
benefited socio-economically disadvantaged individuals
and, in fact, may have exacerbated disparities. Explaining
the coexistence of growing inequality and expansions in
educational participation has been the focus of theoretical
and applied research. Such research has examined
*Corresponding author: Gabriele Marconi, Organisation for
Economic Co-operation and Development (OECD), E-mails:
gabrimarconi@gmail.com; gabriele.marconi@oecd.org
Francesca Borgonovi, University College London and Organisation
for Economic Co-operation and Development (OECD)
The opinions expressed and arguments employed herein are solely
those of the authors and do not necessarily reflect the official views
of the British Academy, the OECD or its member countries. Francesca
Borgonovi acknowledges the support from the British Academy
through its Global Professorship scheme 4JS.
Open Access. © 2020 Francesca Borgonovi, Gabriele Marconi, published by De Gruyter. This work is licensed under the Creative
Commons Attribution 4.0 Public License.

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 313
differences in the incentives different socio-economic
groups have when making educational decisions and
the extent to which such decisions consider foreseen
decisions on the part of others or not.
Life course studies consider cumulative advantage
mechanisms to explain inequalities in educational
attainment and labour market performance, tracing large
differences in educational attainment to the accumulation
of disadvantage following differences in the early years
(DiPrete & Eirich, 2006). In particular, compensatory
advantage theory maintains that socio-economic
differentials can determine different developmental
trajectories by shaping the resources and stimulation
parents invest in their offspring. More specifically,
compensatory advantage theory predicts that the life
course trajectories of socio-economically disadvantaged
individuals will be more dependent on prior negative
outcomes than those of socio-economically advantaged
individuals (Bernardi, 2014). This could occur because
socio-economically advantaged parents are prepared to
invest resources as a way to reduce their children’s early
failure, as a way to avoid downward social mobility, as
suggested in risk aversion theory (Breen & Goldthorpe,
1997). While compensatory advantage theory and relative
risk aversion theory provide a theoretical foundation to
make predictions on the incentives and behaviours of
individual households, they do not formally consider
behaviours under different external constraints, for
example expansions of educational opportunities.
The maximally maintained inequality thesis
maintains that expansions in educational opportunities
reduce socio-economic differentials in educational
attainment if and only if participation among the most
socio-economically advantaged was already at its highest
prior to educational expansions (Raftery & Hout 1993). By
contrast, the effectively maintained inequality thesis states
that even if expansions of educational opportunities were
to primarily benefit socio-economically disadvantaged
individuals, they would not necessarily be effective in
reducing inequalities in life outcomes (Lucas, 2001).
Qualitative differences in the opportunities enjoyed by
different socio-economic groups would in fact most likely
emerge as a way for the socio-economically privileged to
maintain their advantage (Altbach, Reisberg & Rumbley,
2009).
Despite these concerns and evidence that educational
expansions may not necessarily promote social mobility,
many governments continue to invest in educational
expansions as a way to reduce inequality by providing
individuals from disadvantaged backgrounds with
skills to succeed in the labour market and life in general
(OECD, 2018a). With this belief, many governments have
put in place strategies to widen participation in higher
education (HE) among the groups of individuals who are
traditionally underrepresented in HE, such as individuals
whose parents did not participate in HE, those living
in poverty, those living in rural areas or those with an
immigrant or minority background.
Given the policy relevance of understanding
inequalities in HE and the relative paucity of comparable
data, the aim of this paper is to propose a set of key
indicators that can be used to map inequalities in HE
across OECD countries and monitor its evolution over time.
The goal is to examine the impact educational expansions
can have on social mobility and identify to what extent
predictions based on the theoretical frameworks detailed
are supported by the data.
The paper answers the following research questions:
1. Was the expansion of HE that occurred in the past
decades in many countries associated with a reduction
of inequality in HE participation?
2. Was the expansion of HE that occurred in the past
decades in many countries associated with a reduction
of skills inequality?
3. Are individuals from a disadvantaged socio-economic
background less likely to access HE and to graduate
with more advanced higher education degrees?
4. Are socio-economically disadvantaged secondary
school students less likely to expect to enrol and
complete HE, even when their academic achievement
is on a par with their more advantaged peers?
Answering the first two questions allows us to test
predictions based on the maximally maintained inequality
thesis and the effectively maintained inequality thesis.
If the maximally maintained inequality thesis were
correct, educational expansions would be associated
with reductions in inequality only in countries with
historically high levels of participation. If the effectively
maintained inequality thesis were correct, educational
expansions would not be associated with reductions in
skill disparities.
The third and fourth research questions imply
testing predictions based on compensatory advantage
theory and relative risk aversion theory. According to
these theories, we would expect lower participation in
HE among socio-economically disadvantaged groups,
disparities in expectations to participate in HE and a
stronger association between educational expectations
and academic achievement among socio-economically
disadvantaged youngsters. Students who hold ambitious
expectations about their educational prospects are

314 Francesca Borgonovi, Gabriele Marconi
more likely to put effort into their learning and take an
advantage of the education opportunities available to
them to achieve their goals (Beal and Crockett, 2010;
OECD, 2012; OECD, 2017a; Perna, 2000). Crucially, this
can be an explanation for our findings related to the first
and second research questions. If people from socio-
economically disadvantaged groups are less able to take
advantage of the opportunities offered by differentiated
higher education systems, expanding access to higher
education may not reduce learning outcome inequality
(Marginson, 2016a, 2016b; Crawford et al., 2016, Shavit et
al., 2007).
2 Data and Methods
2.1 Data
In this paper, we exploit a variety of datasets steered by the
OECD in order to answer the research questions outlined
in Section 1 as comprehensively as possible.
2.1.1 The OECD Survey of Adult Skills (also called
Programme for the International Assessment of Adult
Competencies – PIAAC)
PIAAC is a low-stakes assessment that was administered
in 2012 (although additional administration rounds were
organised in 2015 and 2017). The PIAAC target population
includes all non-institutionalised adults between the ages
of 16 and 65 (inclusive) whose usual place of residence
is in the country at the time of data collection. Key
assessment domains in PIAAC are literacy, numeracy and
problem solving in technology rich environments. PIAAC
is a household-based study. The PIAAC instruments were
designed to be comparable with the International Adult
Literacy Survey (IALS) and the Adult Literacy and Lifeskills
Survey (ALL). Trained interviewers first administered
the background questionnaire which was conducted
using Computer Assisted Personal Interviewing (CAPI).
Respondents were then encouraged to start the direct skill
assessment. The questionnaire took around 40 minutes
to complete on average and the assessment took slightly
less than an hour. The questionnaire was designed to
identify detailed information on respondents’ educational
attainment, employment and personal characteristics,
such as the educational attainment of the parents of
respondents. Detailed information on PIAAC can be found
in OECD (2016a).
2.1.2 The OECD Indicators of Education System Network
(INES) Pilot Survey on Equity in Tertiary Education
The INES Pilot Survey on Equity in Tertiary Education
is a data collection initiated by the OECD Indicators of
Education Systems (INES) and carried out by national
statistical offices in OECD member countries. National
statistical offices have provided ex-post harmonised data
on the number of new entrants in HE, HE graduates and
on the overall number of individuals in the population
by age, gender, HE education level, parental education,
immigrant status, rural origin. These data are assembled
using information collected through national censuses,
administrative sources or representative surveys and
they enable researchers to calculate rates of access to and
graduation from HE by socio-economic background. All
data comply with the same definitions of socio-economic
background indicators agreed upon by the OECD by and
member countries for the purpose of this data collection.
More details on the methodology and data sources can
be found in OECD (2017b) and in the Appendix (Tables
B1, B2 and B3). The data presented in this paper on HE
inequalities by parental education and immigrant status
have been published in OECD (2017c), OECD (2018b) and
OECD (2019); the data on rural origin presented in this
paper are hitherto unpublished in country-by-country
form (aggregates of countries had been published in
OECD, 2019).
2.1.3 The Programme for International Student
Assessment (PISA)
PISA is a triennial large-scale low-stakes standardised
assessment conducted since 2000. The number of
participating countries has increased from 32 in 2000
to over 70 in 2015. Each PISA cycle assesses three core
domains (reading, mathematics and science), one of which
constitutes the major domain in a given cycle (reading in
2000 and 2009; mathematics in 2003 and 2012 and science
in 2006 and 2015). The assessment is complemented by a
background questionnaire designed to gather information
on students’ background, attitudes towards learning and
behaviours. Since the target sample of the study is 15-year-
olds who were in school at the time of the assessment,
individuals who had already left formal education by that
age are not represented in the study. Because school drop-
out is associated with socio-economic and demographic
status, results represent a lower bound of potential
disparities in educational expectations.

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 315
2.2 Analytical methods
2.2.1 Estimating the association between educational
expansions and inequalities in HE participation and skills
In order to answer the first and second research questions
laid out in Section 1 we examine if increased participation
in HE is accompanied by an increase in equity in learning
outcomes among individuals from different socio-
economic groups by assessing if, other things being equal,
skill disparities are wider or smaller among age cohorts
and countries experiencing different rates of participation
in HE. We do so using data from the OECD Survey of Adult
Skills.
We estimate the relationship between the higher
education access rate and several measures of skills
and participation inequality across age cohorts and
countries. In brief, these estimates allow us to determine
if, other things being equal, skill disparities are wider or
smaller among age cohorts experiencing different rates
of participation in HE. Our main explanatory variable
is the proportion of individuals in different cohorts who
report to have entered HE (access rate). To calculate this
proportion, we considered as having accessed HE all
individuals who reported having ever enrolled in a HE
programme, independently of completion.
We estimate the association between this variable
and five different measures of skill and participation
inequality in the population:
1. Mean levels of literacy proficiency in the overall
population in the country. The relationship between
this variable and the access rate shows whether HE
participation is associated with higher skills at all in
the sample – a precondition for its potential to reduce
skill inequality.
2. The relative access rate, operationalised as the ratio
between the proportion of people accessing HE
among those without higher educated parents and
among those with at least one parent with HE. This
is a measure of equity of access to HE: the higher the
ratio, the closer is the probability to access HE between
people with different levels of parental education.
3. The percentage-point difference between the
proportion of people accessing HE among those
without higher educated parents and among those
with at least one parent with HE (percentage-point
gap). This is an alternative measure of equity of access
to HE: the higher the percentage-point gap, the closer
the probability to access HE between people with
different levels of parental education.
4. The standard deviation of literacy proficiency. The
standard deviation in literacy proficiency is an
indicator of skill inequality in the population. The
higher the standard deviation, the larger is skill
inequality – the differences in literacy proficiency
observed across individuals.
5. The difference between the mean levels of literacy
proficiency among those without higher educated
parents and among those with at least one parent with
HE (literacy proficiency gap). This is an alternative
measure of skill inequality: the higher (i.e., closer to
0) the literacy proficiency gap, the closer the levels of
skills across people with different levels of parental
education.
For a number of different inequality indicators, we
calculated the difference across successive age cohorts
within countries participating to the survey. The following
equation was estimated by OLS:
∆ = 1 ∙ ∆ + 2 ∙ ∆ + 3 ∙ ∆ + 
 + ∆
∆ = 1 ∙ ∆ + 2 ∙ ∆ + 3 ∙ ∆ + 
 + ∆
Where β1, β2 and β3 are the coefficients to be estimated;
the subscript i denotes a country or economy for which
data are available, c denotes a cohort, the operator ∆
represents the difference for a certain variable between
the age cohort c and the younger age cohort in the same
country or economy; fc denotes cohort fixed effects; and εic
1 The percentage-point gap lends itself to a more demanding test of
reduction of inequality, as compared to the relative access rate. When
the percentage-point gap narrows, the relative access rate always
improves; the contrary is not true.
2 Compared to the standard deviation of literacy proficiency, the
literacy proficiency gap provides a more direct test of the hypothesis
that HE is able to reduce the skill gap across socio-economic groups.
Supposing that participation in the disadvantaged socio-economic
group increases faster than in the advantaged group (as it does in our
sample) and individuals from both socio-economic groups benefit
equally from HE, then increased access to higher education should
always improve the literacy proficiency gap in the population. In
contrast, even under the two conditions outlined above, it could
be that expanding access to HE increases the standard deviation of
literacy proficiency across the population (i.e. measured). Take the
illustrative, fictitious example of a society with perfect skill equality
and 0% enrolment to HE. In that case, expanding access would
lead some individuals to acquire more skills than others, therefore
increasing the standard deviation of literacy proficiency.

316 Francesca Borgonovi, Gabriele Marconi
represents a stochastic error term. In the model estimation,
country fixed effects are accounted for by the differencing,
which ensures that only within-country variation is used
in the model. The estimates of the standard errors for the
coefficients are robust to intra-country correlation of the
error term (clustered standard errors).
The model includes two control variables. The first
control variable (secondary) represents upper secondary
education attainment, i.e. the proportion of individuals in
each country and cohort who completed upper secondary
education. This variable is needed to control for the fact
that an expansion in access to HE is usually correlated
with a general expansion of education attainment, and
particularly of upper secondary education attainment
(which in many countries offer the credentials needed
to access HE). The general expansion in education
attainment is, in other words, a factor potentially
affecting both access to HE and the dependent variables
(i.e., potentially introducing endogeneity into the model).
Therefore, it is necessary to control for upper secondary
attainment when estimating the association of changes in
participation to HE with other variables.
The second control variable (basicskills) represents the
mean literacy proficiency score across individuals who did
not access HE in a given country and cohort. This variable
has been included to take into account generic differences
in the level of skills across countries and cohorts, which
could affect the relationship between HE access and the
skills variables in a number of ways. For example, one
could speculate that it is more difficult for education to
increase the level of skills when the population already
reached a certain skill level. In addition, in the model
with the population skill level as the dependent variable,
there could be reverse causality from the skill level to HE
access. Including the level of skills among those that did
not access HE takes this potential problem into account.
The age cohorts are 15-24, 25-34, 35-44, 45-54 and 55-64,
and data are available for 33 countries and economies
participating in the Survey of Adult Skills in 2012 or 2015.
This leads to 132 country-cohort observations. The target
sample size of the Survey of Adult Skills is 5000 adults
aged 15-64 (OECD, 2016b) (with some variation in the final
sample size across countries), so the typical number of
individuals for each cohort within a country is around
1 000 individuals. All estimates consider the stratified
nature of PIAAC data and the fact that achievement is
represented in the data through ten plausible values for
each achievement domain and were obtained following
PIAAC data analysis procedures and recommendations
(OECD, 2016a).
2.2.2 Disparities in access to and completion of different
HE degrees
In order to examine potential reasons for the persistence
of skills disparities in the face of expanded access, data
from the INES Pilot Survey on Equity in Tertiary Education
(OECD, 2017b) are used to identify inequality in access and
completion at two different levels of HE among different
socio-economic and demographic groups. Our analysis
compares the share of individuals from these groups
among new entrants and first-time graduates to their
representation in the population.
The INES Survey covers two entry-level educational
programmes in HE: 1) short-cycle tertiary education
programmes; and 2) bachelor’s and long first degrees
or equivalent programmes. Short-cycle programmes are
relatively short (up to two years) and are typically more
occupationally-specific and practice-based than other HE
programmes (for example, tertiary vocational education,
or fagskoleutdanning, in Norway). However, they can also
have a more general character and prepare students for
access into HE programmes at other levels (for example,
some associate degree programmes in the United States).
Usually, short-cycle programmes are not offered by
universities, but by other HE institutions. The bachelor’s
level is in the large majority of countries the most common
level through which individuals access HE. Bachelor’s
programmes tend to last three or four years. They can
be practically-based programmes oriented towards
the labour market as well as theoretically-oriented,
research-based programmes preparing for more advanced
qualifications. Long first degrees are classified at the
master’s or equivalent level. However, they are generally
accessible with an upper secondary or post-secondary,
non-tertiary qualification. In this respect, they are more
similar to bachelor’s and short-cycle programmes than to
other master’s programmes. A typical example on long
first degrees are medical programmes (UNESCO Institute
for Statistics, 2012; OECD/Eurostat/UNESCO Institute for
Statistics, 2015).
3New entrants are individuals who enter a HE programme for the
first time in their life.
4First-time graduates are individuals who graduate from a HE
programme for the first time in their life.
5Short-cycle programmes are less theoretically-oriented than
bachelor’s programmes and are considered as a lower ISCED level
(UNESCO Institute for Statistics, 2012; OECD/Eurostat/UNESCO
Institute for Statistics, 2015). Across OECD countries, earnings of
individuals with this level of educational attainment tend to be
higher than among individuals with upper secondary education, but
lower than for individuals with higher levels of HE (OECD, 2018).

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 317
We complement analyses of socio-economic
disparities with disparities across individuals living in
urban vs. rural communities. Geographic factors can also
play a role in HE participation decisions. For example,
existing studies have shown that distance of the family
home from HE institutions plays an important role in
participation decisions in large countries such as Canada
(Frenette, 2006) and the United States (Hillman and
Weichman, 2016). However, its role seems less important
in smaller countries such as Denmark (Sørensen and
Høst, 2015) and Ireland (Cullinan etal., 2013). In addition,
at least in certain countries, rural communities are
characterised by different values, traditions and customs
compared to urban communities (Smalley and Warren,
2013), which could affect education enrolment decisions.
Internationally comparable data on entrants to HE
from rural communities are difficult to gather. The INES
Survey allows us to compare the share of entrants with
the share of the overall population living in regions (at
Territorial Level 3) that are considered predominantly rural
or intermediate (or equivalently, not urban) according
to the OECD (2011) Regional Typology. According to the
OECD’s Regional Typology definition regions are classified
as predominantly rural or intermediate, rather than urban,
if at least one of the following three conditions holds:
1. Their population density is below 150 inhabitants
per square kilometre (500 inhabitants for Japan and
Korea)
2. The share of population living in rural local units
(local units with a population density below 150
inhabitants per square kilometre) is above 15%
3. They lack the presence of an urban centre of more than
500000 inhabitants (1000000 for Japan and Korea)
representing at least 25% of the regional population
This definition has the advantage of being standardised
and agreed upon internationally, but it has the important
limitation of being based on the characteristics of
residency at the meso-level (region) rather than micro-
level residency (community). Therefore, people living in
small towns located in rural or intermediate regions are
not considered “urban” according to this definition. In a
similar way, people living in rural communities in a region
that includes a large metropolitan area are classified as
living in an urban region. The distinction between urban
and rural/intermediate in the OECD (2011) definition
reflects, in many cases, the distinction between the
6 Local units coincide with municipalities in most European
countries and Japan, statistical local areas in Australia, and counties
in Korea and the US.
regions surrounding large cities and capitals from other
regions. For example, in Austria only Vienna and its
surroundings (Wiener Umland/Nordteil, Wiener Umland/
Südteil, and Vienna), and the regions of Innsbruck and
Rheintal-Bodenseegebiet are considered urban, whereas
the rest of the country is rural or intermediate. In some
other countries, the distinction reflects other geographical
factors: for example, Portugal has a number of urban
territorial units, mostly located on the coastal area, and
a larger number of rural or intermediate units mostly
located in the inner part of the country.
2.2.3 Estimating inequalities in educational
expectations
In order to estimate inequalities in educational
expectations a dichotomous dependent variable was
created. In 2015 and 2003, the PISA survey waves used
in this paper, participating students were asked to report
the highest level of education they expected to complete.
Students who indicated that they expected to complete
a degree at the 5A, 5B or 6 level according to the 1997
International Standard Classification of Education were
considered as expecting to complete a higher education
degree (UNESCO, 2006). The analysis examines the degree
to which the expectation that students will complete a
HE degree differs depending on key indicators of socio-
economic condition and if they changed between 2003
and 2015.
The first indicator we use to identify disadvantage is
a composite socio-economic status (SES) index, which
aggregates information on parental education, occupation
and availability of cultural resources (Pokropek,
Borgonovi and Jakubowski, 2015). The indicator is
standardised to have a mean of 0 and a SD of 1 across
OECD countries. In our analysis, “high SES” students are
those that were assigned a value on the PISA SES indicator
in the top quarter of the national distribution of SES; and
“low SES” students are those with values on the indicator
in the bottom quarter. Students were considered to have
an immigrant background if they reported having at least
one foreign-born parent or reported being foreign born
themselves. Finally to identify disparities depending on
place of residence, we compare students depending on
whether they live in a city (more than 100000 people),
those living in a town (between 3000 and 100000 people)
and those living in a village or rural area (less than 3000
people).
We first develop a series of country specific descriptive
statistics to compare average levels of HE expectations

318 Francesca Borgonovi, Gabriele Marconi
among different groups of students, and how these evolved
between 2003 and 2015. These results are presented in
Tables C1, C2 and C3 in the Appendix. Next, we fitted a
series of country specific logistic regression models to
estimate the odds that students will report expecting to
complete HE depending on personal characteristics. All
results control for students’ academic performance in the
PISA standardised test (through an indicator of whether
the student achieved at least the PISA proficiency level
3 in reading, mathematics or science). We include all
countries with available data in our analyses, implying
that the country coverage extends well beyond OECD
countries.
3 Results
3.1 Are expansions in access to HE
associated with lower socio-economic
disparities in participation?
In order to answer the first research question we use data
from the OECD Survey of Adult Skills. The access rate
in HE of 25-44 year-olds whose parents did not obtain a
HE degree was more strongly correlated with the overall
access rate in that age group than the access rate in HE of
25-44 year-olds with at least one parent who obtained a HE
degree (r=0.86 among the former group and r=0.50 among
the latter group – see Figure 1). These results are in line
with predictions: as participation in HE rises in a country,
increases will be especially concentrated among those who
do not have parents with HE degrees. A one-percentage
point increase in the overall rate of participation to HE
in a cohort is associated with a reduction of one fifth of a
percentage point in the participation gap between people
having no parent with HE and individuals with higher
educated parents.
3.2 Are expansions in access to HE
associated with lower disparities in skills?
Increased participation in HE could fail to provide
greater social inclusiveness if it is accompanied by social
stratification within the HE sector. If the children of higher
educated parents and those of non-higher educated
parents attend institutions of different quality and
prestige, the expansion of HE opportunities could lead
to wider inequalities in labour market opportunities and
wages (Marginson, 2016a, 2016b; Crawford et al., 2016,
Shavit et al., 2007). Persisting inequality in skill outcomes
and expanding access to HE may also reflect other factors,
such as differences in a study experience, graduation
Figure 1: Intergenerational educational mobility (2012 and 2015). Proportion of 25-44 year-olds who have entered HE at least once in their
life (independent of completion) by parental education attainment.
Note: Countries are ranked in descending order of the share of the HE access rate of 25-44 year-olds without parents with HE attainment.
Source: Authors’ calculations based on the Survey of Adult Skills (PIAAC) (2012, 2015).

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 319
probability and grades, and career opportunities after
graduation for people from different socio-economic
backgrounds (Crawford etal., 2016).
Our results show that, across different cohorts, a
wider access to HE is related to higher overall literacy
proficiency and to improved odds of participation in HE
among individuals with no parent with a HE qualification.
However, wider access is not related to a reduction in skill
inequality across the population (Table 1).
A one-percentage point increase in the overall
rate of participation to HE in a cohort of individuals is
associated with a significant improvement in literacy
proficiency by 0.47 score points (Model 1). To put this
result in perspective, an increase of 20 percentage points
in the share of individuals entering HE within a cohort (as
observed between the 25-34 and the 35-44 age cohorts in
Korea, Lithuania and Poland - at the upper edge of the
distribution of the variation in access rates) is associated
with an increase of about 10 points in the literacy
proficiency score (which correspond to around one fifth
of a standard deviation in skills). This result accounts
for country and cohort fixed effects, the change in upper
secondary attainment and the change in “basic skills”
(the mean literacy proficiency of those without HE).
A one-percentage-point increase in HE access is also
significantly associated with an improvement by 0.56%
in the relative probability to access HE for people without
higher educated parents (Model 2); and by 0.22 percentage
points in the access rate gap between people with and
without at least one parent with HE (Model 3).
However, expanding access in HE is not associated
with a reduction in skill inequality. Neither the standard
deviation of the literacy proficiency score, nor the literacy
proficiency gap between the two socio-economic groups
are associated with wider access to HE (the coefficients
are close to 0 and not significant – Model 4 and Model 5).
In summary, these results indicate that, across
different cohorts, a wider access to HE is related to higher
overall literacy proficiency and to improved odds of
participation in HE among individuals with no parent with
a HE qualification. However, wider access is not related
to a reduction in skill inequality across the population.
In other words, expanding access to HE reduces the
HE access gap between people from higher and lower
socio-economic background, but not the skill gap. These
results are consistent with the widely held view that the
expansion of access of HE has led to better skills across
different cohorts, and better opportunities for people from
lower socio-economic backgrounds to access HE (Ritzen,
2010; Altbach, Reisberg, & Rumbley, 2009). However, they
also suggest that the expansion of HE does not necessarily
reduce skills inequality in the population, possibly
because of social stratification in HE opportunities.
Table 1: Relationship between the variation in literacy proficiency and HE participation
 Model ()
 mean literacy
proficiency
score
Model ()
 relative access
rate for people
without higher
educated parents
Model ()
 percentage-point gap
in the access rate for
people without higher
educated parents
Model ()
 standard
deviation of
the literacy
proficiency score
Model ()
 literacy proficiency
score gap for people
without higher
educated parents
 access to HE .** (.) .** (.) .* (.) -. (.) . (.)
 upper secondary
education attainment
-. (.) . (.) -. (.) . (.) . (.)
 average literacy
proficiency score among
individuals who did not
access HE
.** (.) . (.) .** (.) -.* (.) -.** (.)
Cohort fixed effects Yes Yes Yes Yes Yes
Observations     
Coefficients from the first-difference regressions of the column variables on the row variables, conditional on cohort fixed effects (cluster-
robust standard errors in brackets).  represents change in one variable across two different cohorts within the same country or economy.
**Significant at the 1% level; *Significant at the 5% level
Note: The changes refer to the difference in the variable values between two subsequent 10-year cohorts of individuals. The cohorts are
15-24, 25-34, 35-44, 45-54 and 55-64.
Source: OECD Survey of Adult Skills (PIAAC).

320 Francesca Borgonovi, Gabriele Marconi
3.3 Cross-country evidence on disparities in
HE access and completion
Disparities in the skills associated with participation
in HE across individuals from different socio-economic
and demographic groups could be due, among other
things, to differences in participation to programmes
of different quality. Prior research has highlighted, for
example, how individuals from disadvantaged groups are
especially less likely to attend Russell group universities
in the United Kingdom, which comprises the most well
regarded universities for academic achievement (Belfield
et al., 2018). Although international comparable data on
academic quality are not available, it is possible to identify
the extent to which entrants in short-cycle programmes,
Bachelor programmes, long first degree programmes from
different socio-economic and demographic backgrounds
are over or under-represented compared to the share of
such group in the general population. Furthermore, by
comparing differences between access and completion
to different programmes, it is possible to identify the
barriers socio-economic disadvantage poses not at entry,
but within the HE sector.
Across countries with available data, the share of
individuals whose parents did not complete HE is between
15 and 35 percentage points lower among new entrants to
bachelor’s and long first degree programmes, than in the
total population (Figure 2). Furthermore, the proportion
of students whose parents do not have a HE qualification
is consistently higher among new entrants in short-cycle
tertiary education programmes than among new entrants
to bachelor’s and long first degree programmes. In some
countries, such as Chile and Slovenia, the proportion of
students whose parents do not have a HE qualification
is also slightly higher among new entrants in short-
cycle tertiary education programmes than in the overall
population.
The share of young people without higher educated
parents tends to be similar among new entrants and among
first-time graduates (between 40% and 50%, on average
across countries with available data). This could suggest
that there is no systematic relationship between socio-
economic backgrounds and HE completion. However,
data from the OECD Survey of Adult Skills suggests that
non-completion across OECD countries is related to socio-
economic backgrounds. Therefore, caution is needed
when drawing conclusions on the completion rate of
individuals from different demographic groups.
Large disparities by parental educational attainment
are confirmed by PIAAC data (Table 2). Across OECD
countries around 27% of individuals in the 20-40 year-
old population have parents without upper secondary
education. However, the figure is 16% among those who
started HE but did not complete (dropouts), and is even
lower among current HE students (11%), graduates (14%),
and graduates from doctoral programmes (7%). Similarly,
while in the population, the proportion of 20-40 year-olds
with at least one parent with a HE qualification is one
third, this figure increases to 43% among HE dropouts,
Figure 2.
Percentage of 18-24 year-olds with parents without HE (2015) in the total
population and among HE entrants and graduates
Note: Short -cycle tertiary education corresponds to ISCED 5, while bachelors or long first degree correspond
to ISCED 6 and some ISCED 7 programmes with direct access from upper secondary education. See Table
A1 and Table B1 in the online Supplementary Annex for the underlying data and metadata.
Source: OECD Pilot Survey on Equity in Tertiary Education.
0
10
20
30
40
50
60
70
80
90
ITA CHL GRC LTU NLD AUS SVN CHE FRA Av erage SWE NOR DEU ISR USA EST FIN PR T
Population Entrants , short-cyc le Entrants , bachel or's and long first degrees Graduates, short-cycle Graduates, bachelor' s and long first degrees
Figure 2: Percentage of 18-24 year-olds with parents without HE (2015) in the total population and among HE entrants and graduates.
Note: Short-cycle tertiary education corresponds to ISCED 5, while bachelors or long first degree correspond to ISCED 6 and some ISCED
7 programmes with direct access from upper secondary education. See Table A1 and Table B1 in the online Supplementary Annex for the
underlying data and metadata.
Source: OECD Pilot Survey on Equity in Tertiary Education.

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 321
50% among HE students and graduates and is around
70% among doctoral graduates.
Individuals with an immigrant background are also
under-represented among new entrants and first-time
graduates, relative to their share in the population. This
is the case for all countries with available data, and both
for the short-cycle and the bachelor’s and long first degree
programmes (with the exception of first-time graduates
at the bachelor’s and long first degree programmes in the
Netherlands). On average across countries with available
data, the proportion of young people with immigrant
background is 10 percentage points lower among first-time
graduates in bachelor’s and long first degree programmes
than in the population.
An immigrant background is considered among the
most relevant equity dimensions for HE among OECD
governments (OECD, 2016c; Marconi, 2017). The inclusion
of immigrants in HE depends on a variety of factors,
including their command of the language of instruction,
their wider socio-economic backgrounds (parental
education, income, etc.), and their legal status (Camilleri
et al., 2012). These can vary widely across countries
(Camilleri et al., 2012), leading to variability across
countries in the representation of immigrants among
entrants and graduates as illustrated (Figure 3).
The association of immigrant backgrounds with
various forms of participation in HE is less evident
(Table 2). Across OECD countries, between 10% and
12% of the population was foreign-born. This figure was
similar among HE students, graduates and drop-outs.
Foreign-born individuals were over-represented among
doctoral graduates, presumably due the high level of
internationalisation of higher education, particularly at
the doctoral level (OECD, 2019). Individuals with foreign-
born parents represent a relatively small fraction of the
20-40 year-old population across OECD countries and
they account for a similar share of individuals among
HE graduates and dropouts. However, their proportion is
about twice as large among doctoral graduates.
The share of young people from rural and intermediate
regions tends to be lower among new entrants in
bachelor’s and long first degree programmes than in the
overall population across countries (though with some
exceptions). In Chile and Poland (two emerging economies
covering relatively wide areas), the share of young people
from rural and intermediate regions is around (or over) 10
percentage points lower among new entrants than among
the overall population (Figure 4). In other six countries,
this difference is more moderate (between 1 and 3
percentage points). However, considering the overall good
quality of these data, often of an administrative nature
and covering the whole population (OECD, 2017b), even
relatively small differences can be of policy relevance.
7 In Estonia, Greece and Ireland (three relatively small countries),
18-24 year-olds from rural and intermediate regions are over-
represented among new entrants at the bachelor’s and long first
degree level.
Table 2: Share of individuals by socio-economic backgrounds in selected categories relevant to HE (2012 or 2015), pooled data across OECD
countries.
Group  Doctoral
graduates
HE
graduates
HE
dropouts
HE
students
Overall
population
First-generation immigrant Percentage . .  . .
Standard error . . . . .
Sample size     
Second-generation
immigrant
Percentage . . . . .
Standard error . . . . .
Sample size     
Parents without upper
secondary education
Percentage . . . . .
Standard error . . . . .
Sample size     
At least one parent with HE Percentage .  .  .
Standard error  . . . .
Sample size     
Source: OECD Survey of Adult Skills (PIAAC).

322 Francesca Borgonovi, Gabriele Marconi
Finally, the share of people from rural or intermediate
regions is higher among new entrants in short-cycle
tertiary education than in bachelor’s or long first degree
programmes in the large majority of countries with
available data.
This evidence is consistent with what was observed
for other dimensions of socio-economic and demographic
characteristics reported in Figures 2 and 3 and suggests
that short-cycle tertiary programmes can play a role in
providing opportunities to access HE for people who may
otherwise find it difficult to do so. However, it is important
to note that short-cycle degrees do not generally confer
the same opportunities to progress at higher levels of HE
(OECD, 2019). Therefore, expansion of access to short-
cycle degrees among people from disadvantaged socio-
demographic groups could be one of the reasons why the
overall expansion of access to HE does not necessarily
result in a reduction in skill inequality.
3.4 Emerging disparities: the educational
aspirations of secondary school students
The Appendix illustrates socio-economic disparities in the
percentage of 15-year-old students who expected to earn a
HE degree in 2003 and 2015 across countries with available
data; as well as disparities related to place or residence
and immigrant background. Results reveal large gaps
between high and low SES students in the expectations
to attend HE: on average across OECD countries, students
with higher SES were around 40% more likely to expect
to complete HE than those with lower SES and such gaps
narrowed significantly between 2003 and 2015 in several
countries, in line with the general expansion of access to
higher education.
Brazil and Korea are the only countries where the
gap in HE expectations increased in the past decade. In
Brazil, this may partly be explained by the fact that the
percentages of students with HE expectations decreased
dramatically in the same period, particularly among
students with low SES (from 64% in 2003 to 39% in 2015).
In most countries, 15-year-olds who live in villages or
rural areas are significantly less likely to expect to attend
and graduate from HE institutions, and disparities between
urban and rural areas are a fairly stable phenomenon
across the countries examined and within countries
over the past decade. On average across countries with
available data, students in village/rural areas were 20%
less likely to have HE aspirations than their peers in cities.
Disparities are substantial in countries such as Hungary,
Italy, the Slovak Republic, Portugal and Turkey where in
2015 the difference in the percentage of students living
in villages or rural areas and those living in cities who
expected to complete HE was 35% points or larger. In
Hungary as little as 3% of students living in a village in
2015 expected to complete HE, while 50% of those living
in a city did so.
Despite the fact that students with an immigrant
background tend to have a disadvantaged socio-economic
condition and to have low levels of competencies when
Figure 3.
Percentage of 18-24 year-olds with immigrant background (2015) in the total
population and among HE entrants and graduates
Note: Individuals with an immigrant background are either foreign-born or have two foreign-born parents.
Short-cycle tertiary education corresponds to ISCED 5, while bachelor’s or long first degree correspond to
ISCED 6 and some ISCED 7 programmes with direct access from upper secondary education. See Table A2
and Table B2 in the online Supplementary Annex for the underlying data and metadata.
Source: OECD Pilot Survey on Equity in Tertiary Education.
0
5
10
15
20
25
30
35
40
USA CHE SWE ISR GRC DEU Average NOR EST NLD SVN FIN
Population Entrants, short-cycle Entrants, bachelor's and long first degrees
Graduates, short-cycle Graduates, bachelor's and long first degrees
Figure 3: Percentage of 18-24 year-olds with immigrant background (2015) in the total population and among HE entrants and graduates.
Note: Individuals with an immigrant background are either foreign-born or have two foreign-born parents.
Short-cycle tertiary education corresponds to ISCED 5, while bachelor’s or long first degree correspond to ISCED 6 and some ISCED 7
programmes with direct access from upper secondary education. See Table A2 and Table B2 in the online Supplementary Annex for the
underlying data and metadata.
Source: OECD Pilot Survey on Equity in Tertiary Education.

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 323
compared to students without an immigrant background,
they are more likely to expect to obtain a HE degree, than
their naive peers. In particular, the group of students who
holds the most ambitious educational expectations is
precisely the group of students who is most disadvantaged
in terms of SES status and levels of academic proficiency:
foreign-born students.
Results presented in Table 3 confirm the descriptive
results presented in the Appendix and indicate that, in
general, disparities are little affected by the inclusion of
controls for academic achievement: among students of
equal academic achievement at age 15, socio-economically
advantaged individuals are generally more likely to expect
to pursue HE than their socio-economically disadvantaged
peers. The exception are students with an immigrant
background, who appear to consistently express greater
educational expectations than their peers without an
immigrant background. In fact, differences between the
two groups are magnified when achievement is accounted
for in Table 3.
3.5 Limitations
Our study suffers from a number of limitations that
could be addressed by future research. First, while our
reliance on different sources of data lets us investigate
our research questions more comprehensively, it also
implies that our indicators of socio-economic condition
differ across the different analyses undertaken. Second,
the focus of our analyses is on disparities (inequalities)
across socio-demographic groups, rather than on equity.
Equity depends on differences in cultural and political
institutions, making it more difficult to capture this
concept through a quantitative analysis like the one we
undertake. For example, a society may consider a certain
degree of inequality in the distribution of opportunities
equitable. Third, studying inequality requires high-quality
data that are often more available in richer countries.
Although we were able to assemble comparable data for
the vast majority of OECD countries, our analyses tend to
reflect the situation of a group of high and middle high
income countries. Finally, in the absence of longitudinal
evidence, some of the analyses we conducted rely on
retrospective data on participation in higher education
among different birth cohorts.
4 Conclusion
The evidence discussed in this paper showed that in OECD
countries, large disparities still exist in participation in
HE. Across cohorts, expanding access to HE is associated
with a reduction in the HE participation gap between
socio-demographic groups, but not with a reduction in
the skill gap between the same groups.
Expanding access to higher education is associated
with a general increase in the population skill level, and
with increasing odds of accessing higher education for
people from disadvantaged socio-demographic groups
(compared to advantaged groups). However, we find that
expanding access to higher education is not associated
Figure 4.
Percentage of 18-24 year-olds coming from rural areas (2015) 9 in the total
population and among HE entrants
Note: Rural regions are those classified as rural or intermediate in the OECD Regi onal Typology (OECD,
2011). Short-cycle tertiary education corresponds to ISCED 5, while bachelor or long first degree correspond
to ISCED 6 and some ISCED 7 programmes with di rect access from upper secondary education. See Table
A3 and Table B3 in the Appendix for the underlying data and metadata.
Source: OECD Pilot Survey on Equity in Tertiary Education.
0
10
20
30
40
50
60
70
80
90
100
SVK HUN SWE NOR IRL AUT CHL EST DEU GRC Average POL PRT AUS CHE USA ISR
Population Entrants, short-cycle Entrants, bachelor's and long first degrees
Figure 4: Percentage of 18-24 year-olds coming from rural areas (2015) in the total population and among HE entrants.
Note: Rural regions are those classified as rural or intermediate in the OECD Regional Typology (OECD, 2011). Short-cycle tertiary education
corresponds to ISCED 5, while bachelor or long first degree correspond to ISCED 6 and some ISCED 7 programmes with direct access from
upper secondary education. See Table A3 and Table B3 in the Appendix for the underlying data and metadata.
Source: OECD Pilot Survey on Equity in Tertiary Education.

324 Francesca Borgonovi, Gabriele Marconi
Table 3: Disparities in Educational Expectations.
Odds ratios of expecting to complete higher education*
Country High ESCS
compared to
low ESCS
Students with
an immigrant
background
compared to
native students
Foreign-born
students with
an immigrant
background
compared to
native students
Native-born
students with
an immigrant
background
compared to
native students
Students
in cities
compared
to villages
Students
in towns
compared
to villages
Students
in cities
compared
to towns
Australia . . . . . . .
Austria . . . . . . .
Belgium . . . . . . .
Canada . . . . . . .
Chile . . . . . . .
Czech Republic . . . . . . .
Denmark . . . . . . .
Estonia . . . . . . .
Finland . . . . . . .
France . . . . . . .
Germany . . . . . . .
Greece . . . . . . .
Hungary . . . . . . .
Iceland . . . . . . .
Ireland . . . . . . .
Israel . . . . . . .
Italy . . . . . . .
Japan . . . . c c .
Korea . . c . c c .
Latvia . . . . . . .
Luxembourg . . . . m m .
Mexico . . . . . . .
Netherlands . . . . c c .
New Zealand . . . . . . .
Norway . . . . . . .
Poland . . . . . . .
Portugal . . . . . . .
Slovak Republic . . .  . . .
Slovenia . . . . . . .
Spain . . . . . . .
Sweden . . . . . . .

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 325
Odds ratios of expecting to complete higher education*
Country High ESCS
compared to
low ESCS
Students with
an immigrant
background
compared to
native students
Foreign-born
students with
an immigrant
background
compared to
native students
Native-born
students with
an immigrant
background
compared to
native students
Students
in cities
compared
to villages
Students
in towns
compared
to villages
Students
in cities
compared
to towns
Switzerland . . . . . . .
Turkey . . . . . . .
United Kingdom . . . . . . .
United States . . . . . . .
OECD average . . . . . . .
Brazil . . . . . . .
B-S-J-G China . . . . . . .
Bulgaria . . . . . . .
Colombia . . . . . . .
Costa Rica . . . . . . .
Croatia . . . . c c .
Cyprus . . . . . . .
Dominican Republic . . . . . . .
Hong King (China) . . . . m m m
Lithuania . . . . . . .
Macao (China) . . . . c c c
Montenegro . . . . c c .
Peru . . . . . . .
Qatar . . . . . . .
Russia . . . . . . .
Singapore . . . . m m m
Chinese Taipei . . . . c c .
Thailand . c . . . .
Tunisia . . . . . . .
United Arab Emirates . . . . . . .
Uruguay . . . . . . .
Source: PISA 2015 data. Numbers denoted in bold imply that a difference is statistically significant at the 5% level. The letter c denotes that
there are not enough observations to identify meaningful estimates. Results are based on country specific logistic regressions that control
for students’ academic performance in the PISA standardised test (through an indicator of whether the student achieved at least the PISA
proficiency level 3 in reading, mathematics or science) and socio-economic status, operationalised through the PISA Index of Economic,
Social and Cultural Status.
ContinuedTable 3: Disparities in Educational Expectations.

326 Francesca Borgonovi, Gabriele Marconi
with a reduction in measures of skill inequality. Across
OECD countries, inequality tends to increase at higher
levels of education. This suggests that varying levels
of inequality within higher education pathways could
be an explanation for the previous result. Disparities in
HE participation and attainment do not depend only on
skills, but they are rooted in disparities in the ambitions
and expectations individuals hold long before they
have to make a decision to apply and enrol in HE. These
underlying differences in educational aspirations across
socio-demographic groups are worrying, because they
may play a role not only in the decision to access HE, but
also to access different programmes within the HE system.
The high level of inequality in access to and
completion of HE is problematic because participation in
HE is generally associated with better labour market and
well-being outcomes. Returns to participation in HE are
substantial, although the earnings advantage associated
with participation in HE is typically lower in countries
with high higher education attainment among the older
population (OECD, 2018b). Moreover, technological
progress and globalisation is likely to lead to even more
polarised employment patterns featuring high-skill/high-
paying jobs on the one hand and low-skill/low-paying
jobs on the other. When jobs are classified into different
skills categories, OECD countries have seen an average
increase of about 5 percentage points in jobs with high
skill requirements and an increase of about 2 percentage
points in jobs with low skill requirements. Employment
in medium skilled jobs decreased by 7 percentage points
between 1995 and 2015 (OECD, 2017d).
It appears that, in most countries, disparities in HE
participation and attainment are rooted in disparities in
the ambitions and expectations individuals hold long
before they have to make a decision to apply and enrol
in HE, and are not a result of their potential and ability
to succeed in HE. For example, at the age of 15, socio-
economically disadvantaged students are less likely to
expect to complete HE than their more advantaged peers
of equal ability. Similarly, students who live in rural
areas are less likely than students who live in large cities
of similar ability and socio-economic status to expect to
complete HE. Disparities by immigration backgrounds in
the ambitions harboured by 15-year-old students are more
complex: many foreign-born students and the children
of foreign-born have ambitious educational plans, but
education systems typically fail to equip them with the
skills that are needed to succeed in HE (OECD, 2018c).
The findings suggest that short-cycle HE programmes
may play a role in reducing inequality in HE access and
attainment. These programmes are shorter, and typically
more occupationally-specific and practically-based than
other HE programmes. More students at this level of
education study part-time than at the bachelor’s level,
on average across OECD countries (OECD, 2016d). This
may be appealing to students with different backgrounds,
and may help individuals who would be the first in their
families to attend HE or who have few role models of HE
participation among their acquaintances to integrate
into the HE learning environment. However, inequality
in HE is not only about access and attainment, but also
about what programmes people enrol in, and what
value these programmes bring to them. Some evidence
indicates that HE graduates from socio-economically
advantaged household typically display higher skills than
HE graduates from more disadvantaged households, an
indication of potentially increased polarisation in the
quality of institutions attended. Widening participation
by encouraging attendance in short-cycle HE programmes
may run the risk of further contributing to disparities in
outcomes among HE graduates.
The differences in the underlying educational
ambitions and in the type of HE programmes attended
by people in different socio-economic groups could
be an explanation for the findings we presented at
the beginning of Section 3. These findings indicate
that expanding HE can create more opportunities for
individuals from different backgrounds to develop skills
that will be required in the labour market and in society.
However, we find that HE expansions in and of themselves
may not be enough to reduce skill inequalities across the
economy and, in fact, may exacerbate these, as argued
by proponents of compensatory advantage theories,
and theories of maximally and effectively maintained
inequality. In order to improve effectively the equality
of opportunities among different individuals, it may be
necessary to broaden access not only to HE in general,
but also to all type of programmes [in particular, to
the most prestigious ones – e.g. Brennan and Naidoo
(2008)]. Furthermore, work needs to be done with socio-
economically disadvantaged youths and their families, to
ensure that they are equipped to benefit from HE, that they
have adequate information on the labour market returns
associated with participation and they are supported
to build adequate paths that lead to participation of
a comparable quality with that enjoyed by the socio-
economically advantaged. More evidence is needed to
understand how skill inequalities across individuals from
different socio-economic and demographic backgrounds
can be reduced, and the role HE can play. Our work
facilitates this by providing comparable indicators that
can be used in further empirical research.

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 327
Acknowledgements: We are grateful to Shizuka Kato
for her comments on an earlier version of the paper, to
Massimo Loi for early feedback on the statistical methods,
and to two anonymous reviewers for their valuable
comments.
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Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 329
Appendix
Table A1: Data for Figure 2 Percentage of 18-24 year-olds with parents without HE (2015) in the total population and among HE entrants and
graduates.
Country Entrants, short-
cycle
Entrants, bachelor’s and
long first degrees
Graduates,
short-cycle
Graduates, bachelor’s and
long first degrees
Population
ITA a .  . .
CHL . . m m .
GRC a . a m .
LTU a . a m .
NLD m . m . .
AUS m . m . .
SVN . . . . .
CHE a  a . .
FRA . . .  .
Average . . ~ . .
SWE . . . . .
NOR . . . . .
DEU m m m . .
ISR m . m . 
USA . x m . .
EST a . a m .
FIN a . a . .
PRT . . m m m
Notes: a: level of education is not applicable; m: missing; x: data are included in the other level of education; ~: insufficient number of
observations to calculate a meaningful average. See Table B1 for more information on definitions and data sources.

330 Francesca Borgonovi, Gabriele Marconi
Table A2: Data for Figure 3 Percentage of 18-24 year-olds with immigrant background (2015) in the total population and among HE entrants
and graduates.
Country Entrants,
short-cycle
Entrants, bachelor’s
and long first degrees
Graduates,
short-cycle
Graduates, bachelor’s
and long first degrees
Population
USA x . m . .
CHE a . a . .
SWE . .  . .
ISR m . m . .
GRC a . a m .
DEU m m m . .
Average ~ . ~ . .
NOR . . . . .
EST a . a m .
NLD m . m . .
SVN . . . . .
FIN a . a . .
Notes: a: level of education is not applicable; m: missing; x: data are included in the other level of education; ~: insufficient number of
observations to calculate a meaningful average. See Table B2 for more information on definitions and data sources.
Table A3: Data for Figure 4 Percentage of 18-24 year-olds coming from rural areas (2015) 9 in the total population and among HE entrants.
Country Entrants, short-cycle Entrants, bachelor’s and long first degrees Population
SVK . m .
HUN . . .
SWE . . .
NOR . . .
IRL .  .
AUT . . .
CHL . . .
EST a . .
DEU m . .
GRC a  .
Average . . .
POL m . .
PRT . . .
AUS m . .
CHE a . .
USA . x .
ISR m . .
Notes: a: level of education is not applicable; m: missing; x: data are included in the other level of education; ~: insufficient number of
observations to calculate a meaningful average. See Table B3 for more information on definitions and data sources.

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 331
Table B1: Metadata for Figure 2 Percentage of 18-24 year-olds with parents without HE (2015) in the total population and among HE entrants
and graduates.
Country Category Reference
year
Definition: operational and
conceptual differences
Coverage
differences
International
students inclusion
Data source
AUS Entrants/graduates,
ISCED 
. . . . Survey of institutions
(Higher Education
Student Data Collection)
Population  . . . Survey (ABS General
Social Survey)
CHE Entrants ISCED   . Includes all
entrants (not
only new
entrants)
. Survey on the social and
economic condition of
students’ life
Graduates ISCED  . . Includes all
graduates (not
only first-time
graduates)
. Graduate survey
Population  . . Includes
international
students
Swiss Adult Education
Survey 
DEU Graduates ISCED   . Only includes
academic
programmes
Includes
international
students (%)
Graduate Panel ,
German Centre for
Higher Education
Research and Science
Studies (DZHW)
Population  . . . OECD Survey of Adult
Skills
EST Entrants ISCED   . First-year
students
instead of new
entrants
. EUROSTUDENT
Population  . . . OECD Survey of Adult
Skills (PIAAC)
FIN Entrants ISCED   . Includes all
entrants (not
only new
entrants)
Includes
international
students (%)
Statistics Finland’s
student and degree data
registers.
Graduates ISCED  . . Includes all
graduates (not
only first-time
graduates)
Includes
international
students
Statistics Finland’s
student and degree data
registers
Population . . . Includes
international
students
Statistics Finland’s
population data.

332 Francesca Borgonovi, Gabriele Marconi
Country Category Reference
year
Definition: operational and
conceptual differences
Coverage
differences
International
students inclusion
Data source
FRA Entrants ISCED / . . . . Cohort of new entrants
into Tertiary Education
(“bacheliers” ).
Graduates ISCED
/
. . . . The cohort of new
entrants into Tertiary
Education (“bacheliers”
), followed within
the Tertiary Education
from September 
up to September .
Population  . . . OECD Survey of Adult
Skills (PIAAC)
GRC Population  . . . Population census
survey 
Entrants . . . . Annual entrant survey
ISR Entrants/graduates,
ISCED 
.For most students, parental
educational attainment has
been inferred on the basis
of their mothers’ number of
years in education.
. . Administrative files and
population registry and
educational attainments
registry (different
sources are used).
Population . . . . Population registry and
educational attainments
registry
LTU Entrants ISCED   . First-year
students
instead of new
entrants
. Survey (EUROSTUDENT)
Population  . . . Census (Population and
Housing Census )
NLD Entrants/graduates,
ISCED 
. . . . Administrative (register
data)
Population . . . Includes
international
students
Administrative
(municipal registration
data)
NOR Entrants/graduates,
ISCED /
. Parental education when
the student was  years
old
. . Administrative registers
Population . . . . Administrative registers
PRT Entrants ISCED / . . . Includes
international
students (%)
Annual Survey filled in
by all higher education
institutions
ContinuedTable B1: Metadata for Figure 2 Percentage of 18-24 year-olds with parents without HE (2015) in the total population and among HE
entrants and graduates.

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 333
Country Category Reference
year
Definition: operational and
conceptual differences
Coverage
differences
International
students inclusion
Data source
SVN Entrants and
graduates, ISCED
/
. . . . Central administrative
database of students
enrolled (the Ministry
of education), Central
population register,
Census
Population . . . Includes
international
students
Central population
register, Census
SWE Entrants/graduates,
ISCED /
. . . . Student registers
Population . . . . Population registers
USA Entrants, ISCED /  . Includes
ISCED 
. Beginning Post
Secondary Students
Graduates, ISCED   . . .  Baccalaurate and
Beyond Longitudinal
Study (B&B)
Population  . . . OECD Survey of Adult
Skills (PIAAC)
Table B2: Metadata for Figure 3 Percentage of 18-24 year-olds with immigrant background (2015) in the total population and among HE
entrants and graduates.
Country Category Reference
year
Definition
differences
Coverage differences International
students
inclusion
Data source
CHE Entrants ISCED   . Includes all entrants (not
only first time entrants)
. Survey on the social and
economic condition of students’
life
Graduates ISCED  . . Includes all graduates
(not necessarily first-time
graduates)
. Graduate survey
Population . . . Includes
international
students
Labour Force Survey
DEU Graduates ISCED   . Only includes academic
programmes
Includes
international
students (%)
Graduate Panel , German
Centre for Higher Education
Research and Science Studies
(DZHW)
Population  . . Includes
international
students
Federal Statistical Office:
Microcensus 
EST Entrants ISCED   . First-year students
instead of new entrants
. EUROSTUDENT
ContinuedTable B1: Metadata for Figure 2 Percentage of 18-24 year-olds with parents without HE (2015) in the total population and among HE
entrants and graduates.

334 Francesca Borgonovi, Gabriele Marconi
Country Category Reference
year
Definition
differences
Coverage differences International
students
inclusion
Data source
FIN Entrants ISCED   . Includes all entrants (not
only first time entrants)
Includes
international
students (%)
Statistics Finland’s student and
degree data registers.
Graduates ISCED  . . Includes all graduates
(not necessarily first-time
graduates)
Includes
international
students (%)
Statistics Finland’s student and
degree data registers
Population . . . Includes
international
students
Statistics Finland’s population
data.
GRC Population  . . . Population census survey 
Entrants . . . . Annual entrant survey
ISR Entrants ISCED  . . . . Administrative files
Graduates ISCED  . . . . Administrative files
Population . . . . Population registry
NLD Entrants/graduates
ISCED 
. . . . Administrative (register data)
Population . . . Includes
international
students
Administrative (municipal
registration data)
NOR Entrants,
graduates,
population
. . . . Administrative registers
SVN Entrants/graduates
ISCED /
. . . . Central administrative database
of students enrolled (the Ministry
of education)
Population . . . Includes
international
students
Central population register,
Census
SWE Entrants and
graduates ISCED
/
. . . . Student registers
Population . . . . Population registers
USA Entrants, ISCED
/
 . Includes ISCED  . Beginning Post Secondary
Students
Graduates, ISCED   . . .  Baccalaurate and Beyond
Longitudinal Study (B&B)
Population  . . Includes
international
students
Current Population Survey 
ContinuedTable B2: Metadata for Figure 3 Percentage of 18-24 year-olds with immigrant background (2015) in the total population and among HE
entrants and graduates.

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 335
Table B3: Metadata for Figure 4 Percentage of 18-24 year-olds coming from rural areas (2015) 9 in the total population and among HE
entrants.
Country Category Reference
year
Definition differences Coverage differences International
students
inclusion
Data source
AUS Entrants/graduates,
ISCED 
. . . . Survey of institutions
(Higher Education
Student Data Collection)
Population . . . . Survey (ABS General
Social Survey)
AUT Entrants . . . . Statistics Austria,
university statistics
Population . . . Includes
international
students
Statistics Austria,
population statistics
CHE Population . Residence recorded at
the time of obtaining
the higher education
entrance qualification.
All entrants (not
necessarily new
entrants) in Bachelor
or old ‘Diploma’
programmes
. Swiss Higher Education
Information System
(SIUS) 
Entrants . National definition of
rural areas
. Includes
international
students
(%)
Population and
Household Statistics
(STATPOP)
DEU Entrants . . Data include only
academic programmes
(ISCED-Levels ,
, ) because data
on urban/rural areas
are only collected in
university statistics.
. Federal Statistical
Office (), University
statistics
Population . . . Includes
international
students
Federal Statistical Office
(), Population
statistics
EST Entrants  . First-year students
instead of new entrants
. EUROSTUDENT
Population  . . . Statistics Estonia
GRC Population  . . . Census 
Entrants . . . . Annual entrant survey
HUN Entrants . . . . FIR OSAP
Population . . . . KSH
IRL Entrants . . . . Student Record System
(Database operated by
the Higher Education
Authority)
Population . . . Includes
international
students
Estimates based on
 Census data from
the Central Statistics
Office
ISR Entrants . National definition of
rural areas
. . Administrative files
Population . National definition of
rural areas
. . Administrative files and
the population registry

336 Francesca Borgonovi, Gabriele Marconi
Country Category Reference
year
Definition differences Coverage differences International
students
inclusion
Data source
NOR Entrants . . . . Administrative registers
Population . . . . Administrative registers
POL Population . National definition of
rural areas
. . Estimated data
Entrants . National definition of
rural areas
. Includes
international
students
Estimates based on
 Census Results.
PRT Population . . . Includes
international
students
Annual Survey to all
higher education
institutions
Entrants . . . Includes
international
students
(%)
Annual Survey filled in
by all higher education
institutions
SVK Entrants . . . . Databases of processing
statistical reports
of schools in Slovak
Republic
Population . . . . Databases of the
Statistical Office of the
Slovak Republic
SWE Entrants . . . . Student registers
Population . . . . Population registers
USA Population  National definition of
rural areas
. . Beginning Post
Secondary Students
Entrants  National definition of
rural areas
. Includes
international
students
Current Population
Survey 
Table C1: Percentage of secondary school students expecting to complete HE - by country of residency, year and socio-economic status.
PISA  PISA  PISA  - PISA 
Low SES High
SES
Difference
(high-low)
Low SES High SES Difference
(high-low)
Low SES High
SES
Difference
(high-low)
Country % % % point diff. % % % point diff. % % % point diff.
Australia       - - 
Austria       - - 
Belgium         -
Canada       - - 
Czech Republic         -
Denmark       - -
Finland       - - 
France       - - 
ContinuedTable B3: Metadata for Figure 4 Percentage of 18-24 year-olds coming from rural areas (2015) 9 in the total population and among HE
entrants.

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 337
PISA  PISA  PISA  - PISA 
Low SES High
SES
Difference
(high-low)
Low SES High SES Difference
(high-low)
Low SES High
SES
Difference
(high-low)
Country % % % point diff. % % % point diff. % % % point diff.
Germany       - -
Greece       - - 
Hungary       - - -
Iceland        - -
Ireland       - -
Italy         -
Japan        -
Korea       - - 
Latvia       - - 
Luxembourg        - -
Mexico        -
Netherlands        - -
New Zealand       -  
Norway        - -
Poland         -
Portugal         
Slovak Republic         -
Spain         -
Sweden        - -
Switzerland         
Turkey       - - 
United Kingdom         -
United States        -
OECD average        - -
Brazil       - - 
Hong Kong (China)        -
Macao (China)       - - 
Russia       - - -
Thailand        -
Tunisia       - - 
Uruguay       - - 
Source: PISA 2003 and 2015 Database.
Notes: High SES students are students in the top quartile of the national distribution of a composite indicator of socio-economic status, the
PISA Index of Economic, Social and Cultural Status, which summarises information on the educational attainment and occupational status of
the parents of participating students as well as resources available in the household. Low SES students are students in the bottom quartile
of the national distribution.
Numbers denoted in bold imply that a difference is statistically significant at the 5% level.
* Students are considered to expect to complete HE if they reported that they expect to obtain a degree at level 5A, 5B or 6 according to the
International Standard of Educational Classifications (ISCED).
ContinuedTable C1: Percentage of secondary school students expecting to complete HE - by country of residency, year and socio-economic status.

338 Francesca Borgonovi, Gabriele Marconi
Table C2: Percentage of secondary school students expecting to complete HE - by country of residency, year and geographical residency.
PISA  PISA  PISA  - PISA 
Percentage of students expecting to
complete higher education*
Difference in the
percentage of
students expecting
to complete higher
education*
Percentage of students expecting
to complete higher education*
Difference in the
percentage of
students expecting
to complete higher
education*
Percentage of students
expecting to complete higher
education*
Difference in the
percentage of
students expecting
to complete higher
education*
Village/
rural
area
(<,)
Town
(between
, and
,)
City
(>,)
City -
village
Town -
village
City -
town
Village/
rural area
(<,)
Town
(between
,
and
,)
City
(>,)
City -
village
Town -
village
City -
town
Village/
rural
area
(<,)
Town
(between
,
and
,)
City
(>,)
City -
village
Town -
village
City -
town
 % % % %
point
diff.
%
point
diff.
%
point
diff.
% % % %
point
diff.
%
point
diff.
%
point
diff.
% % % %
point
diff.
%
point
diff.
%
point
diff.
Australia             - - - - - 
Austria             - - -  - 
Belgium    - - -         - - - -
Canada             - - -   -
Czech                  
Denmark     -         -  - - 
Finland             - - -  - 
Germany             - -  -
Greece             - - - - 
Hungary             - - - - - -
Iceland               - -  -
Ireland              - - - - 
Italy          - - -     -
Japan c   c c  m   m m  m   m m 
Korea c   c c  c   c c  m - - m m 
Latvia             - - -   
Luxembourg m   m m  m  m m m m m - m m m m
Mexico                -  -
Netherlands c   c c  c   c c  m   m m 
New Zealand              -  - - 

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 339
PISA  PISA  PISA  - PISA 
Percentage of students expecting to
complete higher education*
Difference in the
percentage of
students expecting
to complete higher
education*
Percentage of students expecting
to complete higher education*
Difference in the
percentage of
students expecting
to complete higher
education*
Percentage of students
expecting to complete higher
education*
Difference in the
percentage of
students expecting
to complete higher
education*
Village/
rural
area
(<,)
Town
(between
, and
,)
City
(>,)
City -
village
Town -
village
City -
town
Village/
rural area
(<,)
Town
(between
,
and
,)
City
(>,)
City -
village
Town -
village
City -
town
Village/
rural
area
(<,)
Town
(between
,
and
,)
City
(>,)
City -
village
Town -
village
City -
town
 % % % %
point
diff.
%
point
diff.
%
point
diff.
% % % %
point
diff.
%
point
diff.
%
point
diff.
% % % %
point
diff.
%
point
diff.
%
point
diff.
Norway                  
Poland                - - -
Portugal                  
Slovak
Republic
            -  - -  -
Spain     -          - - 
Sweden             -     
Switzerland               - - -
Turkey       c   c c m - - m m -
United
Kingdom
    -     -       
United States      -      -    - - 
OECD average                  -
Brazil             - - -   
Russia             - - - - - -
Thailand                -  -
Tunisia          - - -   
Uruguay            - - - - - -
Source: PISA 2003 and 2015 Database. Numbers denoted in bold imply that a difference is statistically significant the 5% level.
* Students are considered to expect to complete HE if they reported that they expect to obtain a degree at level 5A, 5B or 6 according to the International Standard of Educational Classifications
(ISCED).
ContinuedTable C2: Percentage of secondary school students expecting to complete HE - by country of residency, year and geographical residency.

340 Francesca Borgonovi, Gabriele Marconi
Table C3: Percentage of secondary school students expecting to complete HE - by country of residency, year and migration background.
Percentage of students expecting
to complete higher education*
Difference in the percentage of
students expecting to complete
higher education*
Percentage of students
expecting to complete higher
education*
Difference in the percentage of
students expecting to complete
higher education*
Percentage of students expecting
to complete higher education*
Difference in the percentage of
students expecting to complete
higher education*
Students with an
immigrant background
Students with an
immigrant background
Students with an
immigrant background
Native
stu-
dents
Total Native-
born
Foreign-
born
Between
students
with an
immig-
rant back-
ground
and
native
students
Between
native-
born
students
with an
immig-
rant back-
ground
and
native
students
Between
foreign-
born
students
with an
immig-
rant back-
ground
and
native
students
Native
stu-
dents
Total Native-
born
For-
eign-
born
Between
students
with an
immig-
rant back-
ground
and
native
students
Between
native-
born
students
with an
immig-
rant back-
ground
and
native
students
Between
foreign-
born
students
with an
immig-
rant back-
ground
and
native
students
Native
stu-
dents
Total Native-
born
Foreign-
born
Between
students
with an
immig-
rant back-
ground
and
native
students
Between
native-
born
students
with an
immig-
rant back-
ground
and
native
students
Between
for-
eign-born
students
with an
immi-
grant
back-
ground
and
native
students
 Mean
Index
Mean
Index
Mean
Index
Mean
Index
Mean diff. Mean diff. Mean diff. Mean
Index
Mean
Index
Mean
Index
Mean
Index
Mean diff. Mean diff. Mean diff. Mean
Index
Mean
Index
Mean
Index
Mean
Index
Mean diff. Mean diff. Mean diff.
Australia               - - - -   
Austria     - - -     - - - - - - -  - 
Belgium     - - -     - - -     
Canada               - - - -   
Czech
Republic
           - -       
Denmark             -  - -  -   -
Finland             - - - - - - -
France     -  -       - - - - - - - -
Germany     - - -     - - - -      
Greece     - - -     - - - - - -  - -
Hungary              - - - -   
Iceland      -        -  - - - -
Ireland               - - - - - - -
Italy     - - -       - - - - - - -

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 341
Percentage of students expecting
to complete higher education*
Difference in the percentage of
students expecting to complete
higher education*
Percentage of students
expecting to complete higher
education*
Difference in the percentage of
students expecting to complete
higher education*
Percentage of students expecting
to complete higher education*
Difference in the percentage of
students expecting to complete
higher education*
Students with an
immigrant background
Students with an
immigrant background
Students with an
immigrant background
Native
stu-
dents
Total Native-
born
Foreign-
born
Between
students
with an
immig-
rant back-
ground
and
native
students
Between
native-
born
students
with an
immig-
rant back-
ground
and
native
students
Between
foreign-
born
students
with an
immig-
rant back-
ground
and
native
students
Native
stu-
dents
Total Native-
born
For-
eign-
born
Between
students
with an
immig-
rant back-
ground
and
native
students
Between
native-
born
students
with an
immig-
rant back-
ground
and
native
students
Between
foreign-
born
students
with an
immig-
rant back-
ground
and
native
students
Native
stu-
dents
Total Native-
born
Foreign-
born
Between
students
with an
immig-
rant back-
ground
and
native
students
Between
native-
born
students
with an
immig-
rant back-
ground
and
native
students
Between
for-
eign-born
students
with an
immi-
grant
back-
ground
and
native
students
 Mean
Index
Mean
Index
Mean
Index
Mean
Index
Mean diff. Mean diff. Mean diff. Mean
Index
Mean
Index
Mean
Index
Mean
Index
Mean diff. Mean diff. Mean diff. Mean
Index
Mean
Index
Mean
Index
Mean
Index
Mean diff. Mean diff. Mean diff.
Japan     -  -   c c  c c - m m - m m
Korea    c - - c  c c c c c c - m m m m m m
Latvia     - -      - -  - - -   - 
Luxem-
bourg
    - - -     - - -   - - - 
Mexico     - - -     - -   -  - 
Nether-
lands
                  
New
Zealand
                    -
Norway             -       
Poland         c c c c c c m m m m m m
Portugal       -     -  -      - 
Slovak
Republic
    - - -         - - - - - -
Spain     - - -     - -    - -  -
Table C3: Percentage of secondary school students expecting to complete HE - by country of residency, year and migration background.

342 Francesca Borgonovi, Gabriele Marconi
Percentage of students expecting
to complete higher education*
Difference in the percentage of
students expecting to complete
higher education*
Percentage of students
expecting to complete higher
education*
Difference in the percentage of
students expecting to complete
higher education*
Percentage of students expecting
to complete higher education*
Difference in the percentage of
students expecting to complete
higher education*
Students with an
immigrant background
Students with an
immigrant background
Students with an
immigrant background
Native
stu-
dents
Total Native-
born
Foreign-
born
Between
students
with an
immig-
rant back-
ground
and
native
students
Between
native-
born
students
with an
immig-
rant back-
ground
and
native
students
Between
foreign-
born
students
with an
immig-
rant back-
ground
and
native
students
Native
stu-
dents
Total Native-
born
For-
eign-
born
Between
students
with an
immig-
rant back-
ground
and
native
students
Between
native-
born
students
with an
immig-
rant back-
ground
and
native
students
Between
foreign-
born
students
with an
immig-
rant back-
ground
and
native
students
Native
stu-
dents
Total Native-
born
Foreign-
born
Between
students
with an
immig-
rant back-
ground
and
native
students
Between
native-
born
students
with an
immig-
rant back-
ground
and
native
students
Between
for-
eign-born
students
with an
immi-
grant
back-
ground
and
native
students
 Mean
Index
Mean
Index
Mean
Index
Mean
Index
Mean diff. Mean diff. Mean diff. Mean
Index
Mean
Index
Mean
Index
Mean
Index
Mean diff. Mean diff. Mean diff. Mean
Index
Mean
Index
Mean
Index
Mean
Index
Mean diff. Mean diff. Mean diff.
Sweden               
Switzer-
land
    - -             - - 
Turkey      -     -  - - - - -   
United
Kingdom
                    
United
States
    -  -     -  -      - 
OECD
average
                    
Brazil     - - -    c   c - - - m - - m
Hong Kong
(China)
    - - -     -  -   - - -
Macao
(China)
          - -  -    
Russia              - - - - -  - 
Table C3: Percentage of secondary school students expecting to complete HE - by country of residency, year and migration background.

Inequality in Higher Education: Why Did Expanding Access Not Reduce Skill Inequality? 343
Percentage of students expecting
to complete higher education*
Difference in the percentage of
students expecting to complete
higher education*
Percentage of students
expecting to complete higher
education*
Difference in the percentage of
students expecting to complete
higher education*
Percentage of students expecting
to complete higher education*
Difference in the percentage of
students expecting to complete
higher education*
Students with an
immigrant background
Students with an
immigrant background
Students with an
immigrant background
Native
stu-
dents
Total Native-
born
Foreign-
born
Between
students
with an
immig-
rant back-
ground
and
native
students
Between
native-
born
students
with an
immig-
rant back-
ground
and
native
students
Between
foreign-
born
students
with an
immig-
rant back-
ground
and
native
students
Native
stu-
dents
Total Native-
born
For-
eign-
born
Between
students
with an
immig-
rant back-
ground
and
native
students
Between
native-
born
students
with an
immig-
rant back-
ground
and
native
students
Between
foreign-
born
students
with an
immig-
rant back-
ground
and
native
students
Native
stu-
dents
Total Native-
born
Foreign-
born
Between
students
with an
immig-
rant back-
ground
and
native
students
Between
native-
born
students
with an
immig-
rant back-
ground
and
native
students
Between
for-
eign-born
students
with an
immi-
grant
back-
ground
and
native
students
 Mean
Index
Mean
Index
Mean
Index
Mean
Index
Mean diff. Mean diff. Mean diff. Mean
Index
Mean
Index
Mean
Index
Mean
Index
Mean diff. Mean diff. Mean diff. Mean
Index
Mean
Index
Mean
Index
Mean
Index
Mean diff. Mean diff. Mean diff.
Thailand    c - - c    c   c  - - m - - m
Tunisia     - -        - - - - - - - 
Uruguay               - - - -  - 
Source: PISA 2003 and 2015 Databases. Numbers denoted in bold imply that a difference is statistically significant at the 5% level.
* Students are considered to expect to complete HE if they reported that they expect to obtain a degree at level 5A, 5B or 6 according to the International Standard of Educational Classifications
(ISCED).
Table C3: Percentage of secondary school students expecting to complete HE - by country of residency, year and migration background.