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Migrant integration policies and health inequalities in Europe


The conceptual model for the first step of the analysis is drawn from previous studies
16] (see Fig. 1). The theoretical framework is based on socio-ecological models assuming that self-assessed
health is affected by a large set of determinants at multiple levels. The most important
determinants are socio-economic factors, social and physical environments, healthcare use,
and health behaviors 18]. Being a non-European citizen or non-born in Europe, as a proxy for migrant status,
is considered one of the socio-economic determinants of health acting at the individual
or family level 19]. At the group level, socio-economic factors contribute to unequal social and physical
environmental exposures, which increase health inequalities 20]. In this context, the aim is to test if migrant policies affect the socio-economic
environment in which both migrants and non-migrants live. If individuals live in a
country where there are problems in terms of granting rights to migrants, this could
reasonably negatively affect the way they live and, ultimately, their health. This
hypothesis is tested in the present analysis by considering country policies towards
migration as a component of the social environment in which both migrants and non-migrants
live. Therefore, migrant policies are introduced at the country level using a migrant
integration policy variable in order to explain the observed socio-economic inequalities
in health. Migrant integration policies at country level may influence health through
several pathways. They are part of the social context of the country where individuals
live, and as such they can affect the health of all people living in the country.
Furthermore, their specific interaction with the status of non-EU citizenship, can
affect migrants health status at the individual level, such as other individual socio-economic
determinants (e.g. income, occupation, education, etc.).

thumbnailFig. 1. The conceptual model. Source: adapted from Franzini and Giannoni 20]

We use multilevel models with a dataset of individual observations made available
by Eurostat through the release of the 2012 wave of EU-SILC cross-sectional data 21]. Using multilevel models allows to estimate the proportion of the variation in health
that can be explained by the social status, controlling for other determinants of
health at both individual and country level, as well as country level unobserved factors
16]. Moreover, by using multilevel models it is possible to introduce simultaneously
individual level variables and country level factors, such as country specific policies
and attitudes towards migration. The use of cross-sectional data has its own limitations,
partially overcome by multilevel techniques. In this case, we decided not to use the
longitudinal survey. The main reason is that information on the citizenship status
or country of birth is limited compared to cross sectional waves, and it is not always
representative at country level. Moreover, cross-sectional data are overall richer
in terms of information recorded, i.e. more variables are available in cross sectional
waves than in the longitudinal version of the EU-SILC dataset 22]. For each response variable, we carried out two analyses: a global analysis and a
two-step analysis. The global analysis involves the entire study sample, whereas the
two-step analysis is conducted by running separate regressions for each country using
only individual level variables. Both analyses treat self-reported measures of health
status as dependent variables.

In the global analysis, due to the multistage sampling design used to collect the
data and considering the nature of the response variables, we use two-level models
with individuals nested within countries. In the first step of the analysis, multilevel
ordered mixed effects logit models are estimated for the dependent variables: self-assessed
poor health and self-reported limiting severe or very severe long standing illnesses.
These models allow for the estimation of the direct effect of individual-level and
group-level explanatory variables, as well as interactions between levels 23].

We consider the following two-level mixed effects ordered logistic model for the dependent
variable, yij
(for individual i, country j). The probability of observing outcome k for response y ij

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? ij
=?X ij ??+?Z ij u j
?+?offsett ij
, k
is taken as -?, and k
is taken as?+??. X
are the demographic and socio-economic explanatory variables at individual level (level
1), and Z
are the explanatory variables at country level (level 2). X ij
does not contain a constant term because its effect is absorbed into the cutpoints.

For cluster (country) j, j?=?1,…, M (with cluster j consisting of i?=?1,…,n j
observations), the conditional distribution of y j
=?(y j1 ,…, y jnj )’ given a set of cluster-level random effects u j

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Moreover, we estimate multilevel mixed-effects logistic regression models for self-reported
chronic illness. In order to analyze the differential influence of individual characteristics
over health, further models are estimated adding the interactions between the ecological
variables and the individual characteristics. In particular, we check if problems
in policies for migrant integration at country level influence non-European born or
non-European citizens’ health differently than local citizens’ health. Moreover, in
order to take into account possible interaction effects between socio-economic and
demographic conditions and the migrant status, the key variable “Non-EU citizen or
born outside the EU” is interacted with individual socio-economic characteristics.


The first part of the analysis is based on cross-sectional micro-data from the Eurostat,
EU-SILC, reference year: cross sectional 2012 21]. Participants are adults regularly residents in European countries. We select countries
for which citizenship status and country of birth is recorded and the sample is representative
of the population.1
The final sample has 375,110 observations grouped in 23 countries. Table 1 shows the summary statistics for individual and country variables used in the analysis.
The three dependent variables modeled are: self-assessed poor health, self-reported
limiting long-standing illnesses and self-reported chronic illness. Self-assessed
health is measured by the answer to the question “How is your health in general? Is it …”. Respondents choose from a scale of five options: very good, good, fair, bad and
very bad. SAH is one of the most widely used indicators of health in survey research,
and recommended by both the World Health Organization and the European Union Commission.
Evidence shows that SAH is a strong and independent predictor of morbidity and mortality,
as there is an association between SAH and mortality even after adjusting for prevalent
diseases and health behavioral factors 24]. Therefore, the analysis looks at the risk factors of SAH taking into account the
ordered nature of the variable. Estimates are reported for ordered logit models. To
complement the analysis, we also considered other measures of health: limiting long-standing
illness and chronic diseases. Limiting long standing illness is measured by the answer
to the question: “For at least the past 6 months, to what extent have you been limited because of a
health problem in activities people usually do? Would you say you have been …
“. Respondents choose their answer among the following three options: severely limited,
limited but not severely, not limited at all. For the purpose of this study, we consider
the ordered nature of the variable and estimate ordered logit models. Chronic illness
is measured by the answer to the question:”Do you have any longstanding illness or [longstanding] health problem?”. In this case, the estimates are reported for logit models. Moreover, in order to
perform the two-step analysis, responses for each of the three measures of health
are condensed into a dichotomous variable.

Table 1. Summary statistics and variables definition

Table 2 shows country-level statistics for the total sample of observations used and for
the dependent variables. There is a noticeable variation across countries in all the
three health measures. The percentage of individuals with poor or very poor self-assessed
health shows the largest variation, from a minimum of 3 % (Malta, Switzerland) to
a maximum of 25 % (Croatia). Conversely, the variation in the percentage of people
with severe or very severe limitations in daily life is less remarkable, and ranges
between 10 % (Malta) and 37 % (Finland and Portugal). Finally, the proportion of people
with at least one chronic disease is the lowest in Bulgaria (18 %) and the highest
in Finland (50 %). Overall, we do not observe a clear geographical gradient (North–south
or East–west).

Table 2. Sample statistics for the dependent variables

The individual independent variables correspond to socio-demographic (age, sex, marital
status and nationality) and socio-economic (educational level, personal income and
employment status) dimensions. Education is measured as the highest ISCED level attained.
The variable for low education is a dummy variable that takes the value of one if
the individual attained up to a lower secondary level of education, and zero otherwise.
There is remarkable variation between and within countries in the average level of
education attained by non-EU citizens/non-EU born individuals. Southern countries,
like Portugal, France, Luxembourg, Italy and Spain, show a higher proportion of low-educated
non-EU citizens/non-EU born individuals, as compared to the UK, Finland, Sweden, and
most Eastern European countries. The reference individual is a local citizen or EU
born living in a country without problems in migrant integration policies.

In order to measure migrant integration policies in European countries we use MIPEX
data for 2010, the latest available year of the survey 25]. Today, the MIPEX project is led by CIDOB and the Migration Policy Group, and includes
up to 37 national-level organizations, such as think-tanks, NGOs, foundations, universities,
research institutes and equality bodies. Research activities are coordinated by the
Migration Policy Group, in cooperation with the research partners. Our MIPEX data
cover the following six policy areas: labor market mobility, family reunion for third
countries nationals, political rights, long-term residence, access to nationality,
anti-discrimination policies. MIPEX indicators are on a 0–100 % scale for each policy
area, where 100 % is the top score.

In order to build a composite measure of migrant policies, we develop an index based
on MIPEX data. The index measures the number of problematic policy areas in 2010,
i.e. areas ranked with a value below 50 % of the maximum MIPEX score. The problematic
migrant policy scale can take values from 0 to 5. For example, in countries scoring
the maximum value of the index, such as Latvia, political participation and anti-discrimination
policies are limited, while access to citizenship is difficult, labor mobility and
access policies are limited. Moreover, procedures for family reunion and long-term
residence acquisition are complicated, as well as rights of access to health care.
Table 3 shows the distribution of MIPEX scores by area of integration and country. We observe
high variation across countries for all 6 areas as well as for the overall score.
There is a remarkable correlation between the scores of different dimensions. The
overall score more than doubles when moving from countries with problematic integration
policies (minimum of 33 in Latvia) to countries with good levels of migrant integration
(maximum of 84 in Sweden). The number of problematic dimensions reflects well the
overall MIPEX score. We initially tested several alternative specification of the
MIPEX index. The sub-dimensions were aggregated using a factor analysis. We also considered
all sub-dimensions separately as independent variables in the model. However, the
best and most parsimonious specification was obtained by using the number of problematic
dimensions. This approach is also particularly useful for the interpretation of the
results. According to the index, countries such as Finland, The Netherlands, Portugal,
Finland and Sweden appear to be less problematic than Latvia, Malta, Greece, Switzerland
and Estonia (Fig. 2).

Table 3. MIPEX data

thumbnailFig. 2. N. of problematic areas in migrant integration policy by country

In the estimation, we included country-level variables controlling for both the health
care system and the overall economy. The following country-level variables were obtained
from the OECD Health Data and the Eurostat statistics 26], 27]: the Gini index for income inequality, poverty, pollution and homicide rates, the number of
hospital beds per 1000 inhabitants, the proportion of immigrants amongst residents,
the Gross Domestic Product (GDP) per capita, total healthcare expenditure as a share
of GDP, the healthy life years expectancy, and the level of corruption. Out of these
variables, only two were significant in some models, namely: the healthy years life
expectancy and the healthcare expenditure as a share of GDP. Therefore, the results
reported were obtained by controlling for these variables.