Relevant territorial gaps span through the Mediterranean region in multiple aspects of social life: health, education, digital society, human development.
From health issues to lifestyles, from schooling of the younger generations to progress towards a digital society, and finally a summary measurement with human development indexes.
Prevalence of moderate or severe food insecurity in the population (%)
Diabetes prevalence (% of population ages 20 to 79)
Prevalence of current tobacco use (% of adults)
Gross intake ratio to the last grade of lower secondary general education, both sexes (%)
Individuals using the Internet (% of population)
Fixed broadband subscriptions (per 100 people)
Human Development Index (min=0, max=1)
Inequality-adjusted Human Development Index (min=0, max=1)
area_code
ordgeo
Countries
2021
2021
2022
2021
2022
2022
2022
2022
Portugal
12.4
9.1
25.6
100.6
84.5
43.5
0.9
0.8
A
1
Spain
8.0
10.3
28.4
94.7
94.5
36.0
0.9
0.8
A
2
France
6.6
5.3
34.6
99.8
85.3
49.4
0.9
0.8
A
3
Italy
5.7
6.4
22.4
100.3
85.1
31.5
0.9
0.8
A
4
Slovenia
7.0
5.8
20.1
95.7
88.9
31.9
0.9
0.9
A
5
Croatia
9.7
4.8
37.0
97.4
82.1
27.0
0.9
0.8
A
6
Greece
6.3
6.4
32.8
95.2
83.2
43.0
0.9
0.8
A
7
Malta
7.2
8.0
24.7
99.8
91.5
43.0
0.9
0.8
A
8
Cyprus
..
8.6
35.6
104.0
89.6
38.6
0.9
0.8
A
9
Serbia
14.8
9.1
39.5
97.3
83.5
29.3
0.8
0.7
B
10
Kosovo
..
..
..
..
..
..
..
..
B
11
Bosnia and Herzegovina
13.4
9.1
36.2
88.3
78.8
27.1
0.8
0.7
B
12
Montenegro
12.9
9.1
32.0
95.8
88.2
31.3
0.8
0.8
B
13
North Macedonia
24.0
6.1
..
86.8
84.2
24.6
0.8
0.7
B
14
Albania
30.2
10.2
21.9
97.6
82.6
20.6
0.8
0.7
B
15
Turkiye
..
14.5
30.5
93.4
83.4
22.3
0.9
0.7
C
16
Syrian Arab Republic
..
14.9
..
44.1
34.6
7.3
0.6
0.4
C
17
Lebanon
36.5
8.0
34.3
..
90.1
7.6
0.7
..
C
18
Jordan
..
15.4
35.6
67.6
90.5
7.1
0.7
0.6
C
19
Israel
13.2
8.5
20.4
93.7
92.1
29.4
0.9
0.8
C
20
West Bank and Gaza
28.1
..
..
90.7
88.6
8.0
0.7
0.6
C
21
Egypt, Arab Rep.
28.5
20.9
24.7
86.1
72.2
10.8
0.7
0.6
D
22
Libya
39.8
8.7
..
..
88.4
4.8
0.8
..
D
23
Tunisia
28.5
9.6
20.5
80.5
73.8
13.7
0.7
0.6
D
24
Algeria
19.4
7.1
21.2
84.5
71.2
10.5
0.8
0.6
D
25
Morocco
..
9.1
13.0
73.9
90.7
6.4
0.7
0.5
D
26
Prevalence of moderate or severe food insecurity in the population (%)
CyprusNo data available
KosovoNo data available
TurkiyeNo data available
Syrian Arab RepublicNo data available
JordanNo data available
MoroccoNo data available
Diabetes prevalence (% of population ages 20 to 79)
KosovoNo data available
West Bank and GazaNo data available
Prevalence of current tobacco use (% of adults)
KosovoNo data available
North MacedoniaNo data available
Syrian Arab RepublicNo data available
West Bank and GazaNo data available
LibyaNo data available
Gross intake ratio to the last grade of lower secondary general education, both sexes (%)
KosovoNo data available
LebanonNo data available
LibyaNo data available
Individuals using the Internet (% of population)
KosovoNo data available
Syrian Arab RepublicLatest available data: 2019
Fixed broadband subscriptions (per 100 people)
KosovoNo data available
West Bank and GazaLatest available data: 2021
Human Development Index (min=0, max=1)
KosovoNo data available
Inequality-adjusted Human Development Index (min=0, max=1)
KosovoNo data available
Syrian Arab RepublicLatest available data: 2015
LebanonNo data available
LibyaNo data available
Some highlighted topics
The availability of indicators with sufficient timeliness and territorial coverage for the entire
Mediterranean region is not such as to allow an articulate reading of some important social themes,
but at least it allows an exploration of several relevant matters.
Health
Among the significant health-related phenomena, the issue of food insecurity and that of the
prevalence of diabetes are linked - albeit with very different connotations - to the issues of the
population's living conditions. Figure 1 shows how the incidence of the respective indicators differs
within the Mediterranean area.
The percentage of the population in moderate or severe food insecurity in 2021 shows critical or even
very critical values in some non-European Mediterranean countries. This is especially the case in
Libya (39.8%) and Lebanon (36.5%), but also in Egypt, Tunisia and Palestine more than a quarter of the
population is in this condition (without taking into account the various countries with missing data).
Moreover, this is not only a problem in the Middle East and North Africa: in the Western Balkans, the
percentages in Albania (30.2%) and North Macedonia (24%) are at similar levels. The problem is much
less widespread in the other Balkan countries (less than 15%) and even less so in the EU countries,
which, with the exception of Portugal, have an incidence of the phenomenon of less than 10%, with the
most favourable values in Italy (5.7%), Greece (6.3%) and France (6.6%).
With regard to diabetes, the incidence of this pathology in the population aged 20-79 years also shows
the highest values in some Middle Eastern and Mediterranean African countries: in order, Egypt
(20.9%), Jordan (15.4%), Syria (14.9%) and Turkey (14.5%). Apart from these cases, it can be observed
that in all the macro-regions of the Mediterranean area, variability is manifested at lower levels,
generally below an incidence of around 10%. In fact, the highest and lowest values are 10.3% for Spain
and 4.8% for Croatia in the European Union, 10.2% for Albania and 6.1% for North Macedonia in the
Western Balkans; in the Middle East and North Africa, all but the most critical countries show values
between 7 and 10%.
Figure 1 - Moderate or severe food insecurity in the population (%) and diabetes (% of population aged
20-79). Year 2021
...
Health issues also depend on the population's lifestyles, such as smoking habits. Tobacco consumption
in the population aged 15 years and over shows the highest prevalence in 2022 in some countries of the
former Yugoslavia: in order, Serbia (39.5%), Croatia (37%) and Bosnia-Herzegovina (36.2%). Proportions
of smokers in the adult population exceeding one third also concern Cyprus and France in the European
Union, and Lebanon and Jordan in the Middle East. On the other hand, in all macro-regions there are
countries where the smoking habit is much more limited (percentages around 20% in the case of
Slovenia, Italy, Albania, Israel, Tunisia and Algeria), while the lowest value is reported for Morocco
(13%). In this regard, it can be considered that tobacco consumption by the population is affected not
only by the prevention policies adopted in the different countries, but also by complex socio-cultural
factors linked also to gender differences (see page ‘Gender comparisons/Other gender issues’).
Schooling and digitalisation
The percentage of children admitted to the last grade of lower secondary education, to be considered
as a proxy measure of the attainment of a basic level of schooling, highlights some significant
differences between the Mediterranean macro-regions. In fact, this percentage - calculated with
respect to the population at the age envisaged for entry into that grade (generally 14 years old) - is
at least 95 per cent in all the Mediterranean countries of the European Union, and this threshold is
also exceeded in the Western Balkans, as far as Serbia, Montenegro and Albania are concerned. In the
Middle East, Turkey, Israel and Palestine reach an incidence of more than 90%, while in North Africa
the highest value of basic schooling, that of Egypt, is 86.1%. This indicator suggests that the
countries lagging furthest behind in the process of schooling the new generations are Syria (44.1%),
Jordan (67.6%) and Morocco (73.9%).
The gaps between the macro-regions that emerge on the subject of schooling seem less marked, on the
other hand, with regard to the diffusion of Internet use by the population, although there are clear
gaps with regard to digital infrastructures and broadband access.
The percentage of people using the Internet is fairly homogeneous between and within macro-regions,
although some North African countries lag behind. In the countries of the European Union it ranges
from the highest value in Spain (94.5%) to the lowest in Croatia (82.1%); in the Western Balkans from
88.2% in Montenegro to 82.6% in Albania; in the Middle East from the highest in Israel (92.1%) to the
lowest in Turkey (86%); finally, in North Africa Morocco and Libya are around 90%, while the other
three countries lag significantly behind.
The degree of development of digital infrastructures obviously conditions the diffusion of fixed
broadband subscriptions in the population. Digital infrastructure gaps clearly emerge between the EU
and the other Mediterranean macro-regions. In 2022 the highest broadband accessibility is in France
(49.4% of subscriptions relative to the population) and Portugal (43.5%); the lowest values in the EU
Mediterranean countries are around 30% and concern Croatia (27%), Italy (31.5%) and Slovenia (31.9%).
In the other macro-regions, the highest broadband development corresponds to percentages of 20-30%,
for all Western Balkan countries and also for Israel and Turkey. In the remaining Middle Eastern and
North African countries, on the other hand, the lag in broadband deployment is significant, with the
rate of subscriptions reaching a maximum of 13.7% in Tunisia.
Human development
A widely known statistical measure summarising several dimensions of a country's level of
socioeconomic development is the Human Development Index (HDI), adopted since 1993 by the UN to assess
the quality of life of the population in all countries, with the explicit aim of shifting the focus of
economic development from GDP to people-centred development policies (https://www.undp.org/). The
index, calculated as the geometric mean of three basic indices related respectively to life
expectancy, educational attainment and per capita income, has since given rise to several variants,
which also consider information on equality and gender (see also page ‘Gender comparisons/Other gender
issues’).
The latest update of the Human Development Index and the Inequality Adjusted Index returns a picture
of clear segmentation between different levels achieved in the four macro-regions of the Mediterranean
area, with some exceptions (Figure 2). The index exceeds the value of 0.90 in all EU countries except
Portugal, Croatia and Greece where it nevertheless approaches this threshold. The highest level is
reached by Slovenia (0.93) and Malta (0.92). Outside the EU, the only Mediterranean country with a
similar level is Israel. In the Western Balkans, this indicator varies from the highest in Montenegro
(0.84) to the lowest in North Macedonia (0.77), while in the Middle East, Turkey also comes out on top
(0.86). In the remaining non-European countries, the index lies in the range of 0.70/0.75, with Syria
(0.56) assigned a dramatic figure, evidently linked to the consequences of the war events of the
previous years.
A measure derived from the previous indicator is the Inequality Adjusted Human Development Index,
which takes into account inequalities within countries. It reproduces the same spatial profile as the
Human Development Index, albeit with less advanced levels of progress. The gap between the two
indices, measured in terms of the difference between their respective values, ranges from a minimum in
Slovenia, Croatia and Serbia (0.05/0.07 points difference) to a maximum in the North African countries
(more than 0.15 points difference).
Figure 2 - Human Development Index and Inequality Adjusted Human Development Index. Year 2022 (min =
0; max =1)
...
The dynamics of the Human Development Index is of great relevance in order to grasp the trends of
narrowing gaps between realities characterised by different levels of development. From this point of
view, it is interesting to highlight that in the last two decades, the most consistent advances in the
index with respect to 2001 have been achieved in Turkey, with an increase in value of + 0.18 points,
as well as in Morocco (+ 0.16), Malta (+ 0.13) and Bosnia-Herzegovina (+ 0.12), while the only country
that has experienced a decrease is Syria (- 0.04).
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Metadata
Indicators
Definition
The percentage of the population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis.
Sources
WHO
Methodology
A statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015). Once the age-and-sex-specific prevalence rates from national surveys were compiled into a dataset, the model was fit to calculate trend estimates from the year 2000 to 2025. The model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. Depending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population.
Notes
Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population. Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution.
The percentage of people in the population who live in households classified as moderately or severely food insecure. A household is classified as moderately or severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to low quality diets and might have been forced to also reduce the quantity of food they would normally eat because of a lack of money or other resources.
Sources
FAO
Methodology
Data are collected through a survey module in a questionnaire (Gallup World Poll).
Notes
Validity and reliability is evaluated as very high. Margin of error can vary from 0.5% to 10% of the value of the prevalence depending on the sample size.
Percentage of people ages 20-79 who have type 1 or type 2 diabetes. It is calculated by adjusting to a standard population age-structure.
Sources
International Diabetes Federation
Methodology
Data come from a variety of sources such as peer-reviewed scientific papers, and national and regional health surveys. Official reports by international organisations, such as the World Health Organization (WHO), were also assessed for their quality that was defined in consensus with an international expert panel. Data sources that passed strict selection criteria were included in the data analysis.
Notes
People with undiagnosed diabetes are included in the total estimated number of people with diabetes for 2021.
Total number of new entrants into the last grade of lower secondary general education, regardless of age, expressed as a percentage of the population at the intended entrance age to the last grade of or lower secondary general education.The intended entrance age to the last grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade.
Sources
UNESCO
Methodology
Data come from Population censuses and household surveys which collect data on the highest level of education or grade completed by children and young people in a household, through self- or household-declaration. In the former case, each household member above a certain age reports his or her own level of educational attainment. In the latter case, one person, usually the head of the household or another reference person, indicates the highest grade and/or level of education completed by each member of the household. Administrative data from ministries of education on the structure of the education system (entrance ages and durations) are also needed. Surveys can serve as a source of data if they collect information for the age groups of concern. In addition to national surveys, international sample surveys, such as Demographic and Health Surveys (DHS, http://dhsprogram.com) or Multiple Indicator Cluster Surveys (MICS, http://mics.unicef.org), are another source. These surveys are designed to meet commonly agreed upon international data needs and aim to assure cross-national comparability, while also providing data for national policy purposes. These surveys are implemented on a regular basis in selected countries, on average every 3 to 5 years.
Notes
The number of new entrants in the last grade of the given level of education, regardless of age, is expressed as a percentage of the population of the intended entrance age to the last grade of that level of education. If data on new entrants are not collected directly, they can be calculated by subtracting the number of pupils repeating the last grade from total enrolment in the last grade. This is a gross measure and may therefore exceed 100% if there are large numbers of pupils who entered school either early or late and/or who have repeated earlier grades. The fact that the indicator can exceed 100% also makes it more difficult to interpret than the completion rate. Compared to the completion rate, the gross intake ratio to the last grade does not indicate how many children complete the last grade, only how many children enter that grade. If students in the last grade leave school before graduation, the gross intake ratio to the last grade overestimates completion. Data limitations preclude adjusting for students who drop out during the final year of lower secondary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual lower secondary completion rate.
Share per 100 residents of fixed subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s.
Sources
a) International Telecommunication Union; b) World Bank Development Indicators for West Bank and Gaza
Methodology
The data are collected directly from governments by means of annual questionnaires sent to the agency in-charge of telecommunications/ICT (regulator or ministry). The data are verified and harmonized to ensure international comparability and compliance with international standards as outlined in the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT and the Core list of ICT indicators developed by the Partnership on Measuring ICT for Development. Data are collected two times each year through questionnaires sent to governments. In April a short questionnaire is sent requesting data on key telecommunication/ICT indicators such as fixed-telephone subscriptions, mobile-cellular subscriptions, fixed-broadband subscriptions (total and by speed tiers), international bandwidth, mobile and fixed broadband traffic, and mobile population coverage (total, 3G, 4G and above). In September a long questionnaire is sent requesting data on all telecommunication/ICT indicators included in the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT. Data are validated and discrepancies clarified through communication with countries before they are disseminated.
Notes
Fixed broadband Internet includes cable modem, DSL, fibre and other fixed broadband technology (such as satellite broadband Internet, Ethernet LANs, fixed-wireless access, Wireless Local Area Network, WiMAX etc.). Subscribers with access to data communications (including the Internet) via mobile cellular networks are excluded. Advertised and real speeds can differ substantially. Because survey questions and definitions differ, the estimates may not be strictly comparable across countries. In some countries, regulatory authorities monitor the speed and quality of broadband services and oblige operators to provide accurate quality-of-service information to end users. Regional and global totals are calculated as unweighted sums of the country values. Regional and global penetration rates (per 100 inhabitants) are weighted averages of the country values weighted by the population of the countries/regions. Discrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is end of December of every year. A number of countries have fiscal years that end in March or June of every year.
Share per 100 residents of the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones.
Sources
a) International Telecommunication Union; b) World Bank Development Indicators for West Bank and Gaza
Methodology
The data are collected directly from governments by means of annual questionnaires sent to the agency in-charge of telecommunications/ICT (regulator or ministry). The data are verified and harmonized to ensure international comparability and compliance with international standards as outlined in the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT and the Core list of ICT indicators developed by the Partnership on Measuring ICT for Development. Data are collected two times each year through questionnaires sent to governments. In April a short questionnaire is sent requesting data on key telecommunication/ICT indicators such as fixed-telephone subscriptions, mobile-cellular subscriptions, fixed-broadband subscriptions (total and by speed tiers), international bandwidth, mobile and fixed broadband traffic, and mobile population coverage (total, 3G, 4G and above). In September a long questionnaire is sent requesting data on all telecommunication/ICT indicators included in the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT. Data are validated and discrepancies clarified through communication with countries before they are disseminated.
Notes
Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure would be the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year.
Composite index which measures achievements in three key dimensions of human development: a long and healthy life, access to knowledge and a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.
Sources
United Nations Development Programme
Methodology
It is a geometric mean of normalized indices, based upon the following indicators: a) Life expectancy at birth: UN/DESA (2022a). b) Expected years of schooling: CEDLAS and World Bank (2022), ICF Macro Demographic and Health Surveys (various years), UNESCO Institute for Statistics (2022) and United Nations Children’s Fund (UNICEF) Multiple Indicator Cluster Surveys (various years). c) Mean years of schooling: Barro and Lee (2018), ICF Macro Demographic and Health Surveys (var¬ious years), OECD (2022), UNESCO Institute for Statistics (2022) and UNICEF Multiple Indicator Cluster Surveys (various years). d) GNI per capita: IMF (2022), UN/DESA (2022b), United Nations Statistics Division (2022) and World Bank (2022).
Composite index which adjusts the Human Development Index (HDI) for inequality in the distribution of each dimension across the population.
Sources
United Nations Development Programme
Methodology
It is based on a distribution-sensitive class of composite indices proposed by Foster, Lopez-Calva and Szekely (2005), which draws on the Atkinson (1970) family of inequality measures. It is computed as a geometric mean of inequality-adjusted dimensional indices. Inequality in the distribution of HDI dimensions is estimated for: * Life expectancy, using data from complete life tables provided by UN/DESA (2022a). * Mean years of schooling, using household surveys data harmonized in international databases, including the Luxembourg Income Study, Eurostat’s European Union Survey of Income and Living Conditions, the World Bank’s International Income Distribution Database, ICF Macro’s Demographic and Health Surveys, United Nations Children’s Fund’s Multiple Indicators Cluster Surveys, the Center for Distributive, Labour and Social Studies and the World Bank’s Socio-Economic Database for Latin America and the Caribbean, the United Nations Educational, Scientific and Cultural Organization Institute for Statistics’ Educational Attainment Table and the United Nations University’s World Income Inequality Database. * Disposable household income or consumption per capita using the above listed databases and household surveys—and for some countries, income imputed based on an asset index matching methodology using household survey asset indices (Harttgen and Vollmer 2013). The asset index is provided in microdata from ICF Macro Demographic and Health Surveys and United Nations Children’s Fund Multiple Indicator Cluster Surveys.
