Deep regional disparities persist across the Mediterranean region, affecting many aspects of social life, from health and education to access to digital technologies and levels of 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)
Lower secondary completion rate, total (% of relevant age group)
Individuals using the Internet (% of population)
Fixed broadband subscriptions (per 100 people)
Indice di sviluppo umano
Indice di sviluppo umano inequality adjusted
area_code
ordgeo
Countries
2023
2024
2025
2022
2023
2023
2023
2023
Portugal
11.9
10.5
25.8
102.1
85.8
44.1
0.9
0.8
A
1
Spain
6.5
9.7
27.8
90.9
95.5
37.2
0.9
0.8
A
2
France
8.4
6.5
34.6
100.2
86.8
48.7
0.9
0.8
A
3
Italy
1.7
7.7
22.1
95.9
87.0
31.8
0.9
0.8
A
4
Slovenia
8.2
7.0
19.5
94.4
90.4
31.9
0.9
0.9
A
5
Croatia
6.0
10.5
37.6
101.6
83.2
28.5
0.9
0.8
A
6
Greece
6.6
8.0
30.6
95.2
85.0
43.9
0.9
0.8
A
7
Malta
9.2
10.0
23.9
98.5
92.1
44.3
0.9
0.8
A
8
Cyprus
1.4
10.0
35.0
104.5
91.2
38.7
0.9
0.8
A
9
Serbia
9.5
10.5
39.0
95.3
85.4
31.3
0.8
0.8
B
10
Kosovo
..
..
..
..
..
..
..
..
B
11
Bosnia and Herzegovina
9.9
10.3
35.2
90.4
83.4
28.5
0.8
0.7
B
12
Montenegro
10.4
10.7
31.5
98.8
89.8
32.0
0.9
0.8
B
13
North Macedonia
15.2
7.4
..
93.0
87.2
29.2
0.8
0.7
B
14
Albania
33.0
10.6
20.7
97.1
83.1
22.5
0.8
0.7
B
15
Turkiye
..
16.5
30.2
92.4
86.0
22.5
0.8
0.7
C
16
Syrian Arab Republic
..
19.0
..
44.5
34.6
6.9
0.6
0.4
C
17
Lebanon
42.4
12.3
34.1
56.0
83.5
7.3
0.8
..
C
18
Jordan
..
20.5
36.3
90.0
92.5
7.0
0.8
0.6
C
19
Israel
9.2
10.1
19.2
93.7
87.0
29.4
0.9
0.8
C
20
West Bank and Gaza
27.0
15.5
..
92.9
86.6
8.4
0.7
0.5
C
21
Egypt, Arab Rep.
30.8
22.4
25.8
82.3
72.7
10.9
0.8
0.6
D
22
Libya
35.1
15.8
..
..
88.5
4.5
0.7
..
D
23
Tunisia
25.4
16.0
19.5
82.4
72.3
14.1
0.8
0.6
D
24
Algeria
17.6
17.5
21.1
74.7
76.9
12.0
0.8
0.6
D
25
Morocco
..
11.9
12.2
70.2
91.0
7.0
0.7
0.5
D
26
Prevalence of moderate or severe food insecurity in the population (%)
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
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
Lower secondary completion rate, total (% of relevant age group)
KosovoNo data available
Egypt, Arab Rep.Latest available data: 2021
LibyaNo data available
TunisiaLatest available data: 2021
Individuals using the Internet (% of population)
KosovoNo data available
Syrian Arab RepublicLatest available data: 2019
Fixed broadband subscriptions (per 100 people)
KosovoNo data available
LebanonLatest available data: 2022
LibyaLatest available data: 2022
Indice di sviluppo umano
KosovoNo data available
Indice di sviluppo umano inequality adjusted
KosovoNo data available
Syrian Arab RepublicLatest available data: 2015
LebanonNo data available
LibyaNo data available
Some highlighted topics
The analysis of social indicators in the Mediterranean basin plays a crucial role in understanding the dynamics and challenges that characterize this area of great socioeconomic and health complexity, marked by profound cultural, climatic, demographic, and political diversity. The availability of timely indicators with adequate territorial coverage in the Mediterranean area does not yet allow for an in-depth analysis of some important social issues; however, it does at least allow for the exploration of various relevant aspects such as health, education, poverty, and social integration, offering useful insights for assessing emerging trends and critical issues.
Health
The spread of factors related to economic well-being and therefore to food insecurity or the spread of specific diseases such as diabetes significantly reflect the living conditions of populations living in the Mediterranean area, influencing overall health and quality of life outcomes. Food insecurity, defined as the lack of constant access to nutritious and safe food, is a phenomenon that manifests itself in different ways and intensities, determined by factors such as political instability, economic inequalities, and environmental crises. Data from 2023 show alarming rates of moderate or severe food insecurity in several countries in the non-European Mediterranean area. The most critical situation is in Lebanon, where 42.4% of the population lives in moderate or severe food insecurity, followed by Libya with 35.1%. Other countries in the region with high vulnerability are Egypt, Palestine, and Tunisia, where the percentage of the population facing moderate or severe food insecurity exceeds 25%, more than a quarter of the population of each country. The phenomenon is not only concentrated in the most vulnerable areas of North Africa and the Middle East but also in the wider Western Balkans region. In this area, Albania stands out with a very high percentage of 33%, indicating a structural criticality that places it in a higher vulnerability bracket than many other countries in the same macro-area and in the European Union, which instead record values below 15%. In particular, except for Portugal (11.9%), all countries in this macro-area have an incidence of less than 10%, with more favourable values for Cyprus (1.4%) and Italy (1.7%).
Figure 1 - Moderate or severe food insecurity (% in the population, Year 2023) and diabetes (% of the population aged 20 to 79, Year 2024)
...
Diabetes is one of the fastest growing chronic diseases, with incidence varying significantly among Mediterranean countries. This condition, closely linked to sedentary lifestyles and poor diets, often increases in incidence alongside rising food insecurity. The interaction between food insecurity and diabetes highlights how socio-economic vulnerability can increase the risk of developing metabolic diseases. Understanding these correlations is essential for designing policy interventions aimed at improving the living conditions and health of Mediterranean populations. Analysis of the incidence of this disease in the adult and working-age population, specifically in the 20-79 age group, for the year 2024, reveals significant patterns and territorial disparities. In some countries in the Middle East and Mediterranean Africa, we find a marked incidence. In fact, particularly high concentrations are observed in Egypt (22.4%), Jordan (20.5%), Syria (19%), Algeria (17.5%), and Turkey (16.5%). Outside the most critical cases, most Mediterranean countries have incidence levels generally below 11%, with limited variability within the different macro-regions considered. In fact, in the European Union, the maximum and minimum values are 10.5% for Portugal and Croatia and 6.5% for France, respectively; in the Western Balkans, 10.7% in Montenegro and 7.4% in North Macedonia; in the Middle East and North Africa, most countries show an incidence range between 10% (Israel) and 16% (Tunisia).
Another lifestyle-related health indicator is smoking, which is one of the main risk factors for many chronic diseases and deaths from noncommunicable diseases (NCDs), significantly affecting public health. The prevalence of smoking varies considerably between countries, highlighting differences related to historical, cultural, social, and political factors. In 2025, data collected on populations aged 15 and over show higher tobacco consumption in some countries of the former Yugoslavia, such as Serbia (39%), Croatia (37.6%), and Bosnia and Herzegovina (35.2%), where smoking rates exceed one-third of the population. There are multiple causes for this, but in these areas, there is certainly a historical and cultural legacy linked to the influence of the communist period, when smoking was widely tolerated and prevalent and anti-smoking policies were almost non-existent. Among European countries, Cyprus and France stand out with rates above 30%, indicating a significant prevalence of smoking even in more developed contexts with different prevention policies. In other European contexts, percentages are generally lower, around 20%, as in the case of Slovenia and Albania. In Italy, the implementation of effective tobacco control policies that have been in place for many years and greater health education have contributed to reducing the indicator over time, from 26.1% in 2000 to 22.1% today. In the Middle East and North Africa (MENA) macro-regions, Lebanon and Jordan have smoking rates above 30%, partly due to the strong cultural roots of smoking, which is seen as a social ritual (hookah). Israel, Tunisia, and Algeria, on the other hand, have rates similar to those in Europe, also around 20%, while the lowest rate is found in Morocco, with a prevalence of 12.2%. In these North African countries, the rate is characterized by high gender variability (see chapter “Other Gender Issues”). In summary, tobacco consumption is influenced by various factors, including socio-cultural characteristics linked to gender differences and the prevention and public health policies adopted.
School enrolment and digitalisation
The school enrolment indicator is defined here as the percentage of individuals admitted to the last grade of lower secondary education, calculated in relation to the population of typical age for entry into that grade, generally 14 years old. This indicator serves as a proxy for the achievement of a basic level of education and highlights differences in education systems and lower secondary education completion rates between different macro-regions. In the Mediterranean countries of the EU, the basic schooling indicator is very high, reaching around 95%. However, a lower value is recorded for Spain (90.9%). In the Western Balkans, too, the 95% threshold is exceeded in several countries. In particular, Serbia, Montenegro, and Albania record percentages that indicate a good level of basic schooling. The Middle East region presents a more heterogeneous picture. Countries such as Turkey, Israel, and Palestine achieve an incidence of over 90% for admission to the last grade of lower secondary education. However, other countries show a more marked delay, with Syria having the lowest figure in the entire Mediterranean area, at 44.5%. In North Africa, the highest rate of basic schooling is observed in Egypt, with 83.1%. Morocco also lags significantly behind, at 70.3%.
One of the key findings in the analysis of Internet penetration is its relatively high homogeneity across the Mediterranean macro-regions. This means that, despite significant differences in digital infrastructure, a substantial proportion of the population uses the Internet. Variations in usage are less marked than for other indicators, such as basic schooling, suggesting that Internet adoption is influenced by a variety of factors, such as digital culture, digital literacy, device availability, and accessibility of online services. In the European Union, the range is from the highest value in Spain (95.5%) to the lowest in Croatia (83.2%), in the Western Balkans from Montenegro (89.8%) to Albania (83.1%), in the Middle East from Jordan (92.5%) to Lebanon (83.5%), and in North Africa from Morocco (91%) to Tunisia (72.4%).
The level of development of digital infrastructure directly influences the spread of fixed broadband subscriptions, highlighting clear gaps between macro-regions. The European Union has a relatively high penetration rate, but with significant differences between individual member countries. France has the highest rate among the countries considered, with 48.7% of subscriptions. The lowest rates for the other Mediterranean EU countries are around 30% (e.g., Croatia 28.5%, Italy 31.8%, Slovenia 31.9%).
In the Western Balkans, broadband is less widespread than in the EU, with access rates ranging from 20% to 30%. In the remaining Middle Eastern and North African countries, the gap is significant, with the exception of Israel (29.4%) and Turkey (22.5%). In Syria, Jordan, and Morocco, the rate stands at around 7%.
Human development
The Human Development Index (HDI), adopted by the UN in 1993, is a composite measure designed to assess the level of well-being and progress of a society by synthesizing various dimensions such as health, education, and income. The HDI has the explicit aim of shifting the focus of economic development from GDP to people-centered development policies. To calculate the index, three key indicators are selected: life expectancy at birth, education, and per capita income, and their https://www.undp.org/ is calculated. The index is normalized by assigning values from 0 to 1, where 0 represents the least favourable condition and 1 the most favourable.
Figure 2 - Human Development Index and Inequality Adjusted Human Development Index. Year 2023 (min =
0; max =1)
...
Over time, several variants of the HDI have been developed, some of which also incorporate information on equality and gender, culminating in the introduction of the Inequality-Adjusted Human Development Index (IHDI, see chapter “Other gender issues”). The IHDI is a measure derived from the HDI that stands out for its ability to take into account internal disparities within each country. The gap between the HDI and the IHDI is a crucial indicator for understanding how inclusive human development growth has been or, conversely, how concentrated it has been in specific segments of the population or regions. Countries with a low gap between HDI and IHDI generally have a more equitable distribution of resources and development benefits. Conversely, countries with greater internal inequality show wider gaps, even when the HDI is relatively high. Data updated to 2023 show clear territorial segmentation. European Union member countries score high on both the Human Development Index (HDI) and the Inequality-adjusted Human Development Index (IHDI). Slovenia ranks at the top with a score of 0.93 on the Human Development Index, followed by Spain, France, Italy, and Malta, each with a score of 0.92. Among non-EU countries, Israel stands out with a similar level of 0.92. In the Western Balkans, the range goes from a maximum of 0.86 in Montenegro to a minimum of 0.80 in Bosnia and Herzegovina. In the Middle East, Turkey stands out positively, with a value of 0.86. Non-European countries outside the European area have values between 0.71 and 0.76, with Syria (0.56) and Palestine (0.67) showing particularly low values, partly due to conflicts. There are significant differences in the gap between HDI and IHDI. The gap ranges from a minimum of 0.05-0.06 points in Slovenia, Croatia, and Serbia to a maximum of more than 0.15 points in North African countries. This indicates that, despite similar levels of development, internal inequality can vary considerably between countries.
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Metadata
Indicators
Definition
Total number of new students enrolled in the last class of lower secondary education, irrespective of age, expressed as a percentage of the population at the age at which entry into the last class of lower secondary education is expected. The age at which pupils would enter the class if they had started school at the official age of entry into primary education, had studied full-time and progressed without repeating or skipping a class.
Sources
United Nations Scientific and Cultural Organization (UNESCO)
Methodology
The data comes from population censuses and household surveys that collect data on the highest level of education or the level of education completed by the children and young people in a household, either by self-declaration or household declaration. In the first case, each family member over a certain age declares his or her level of education. In the second case, a person, usually the head of the household or another reference person, indicates the highest degree and/or level of education completed by each family member. Administrative data from the Ministries of Education on the structure of the education system (entry age and duration) are also needed. Surveys can serve as a source of data if they collect information for the age groups concerned. In addition to national surveys, international sample surveys, such as Demographic and Health Surveys (DHS, http://dhsprogram.com) or multi-indicator cluster surveys (MICS, http://mics.unicef.org), are another source. These surveys are designed to meet agreed international data needs and aim to ensure cross-border comparability while providing data for national policy purposes. These surveys are conducted regularly in selected countries, on average every 3-5 years.
Notes
The number of new students enrolled in the final year of a given level of education, regardless of age, is expressed as a percentage of the population of the age of entry into the last year of that level of education. If data on new students are not collected directly, they can be calculated by subtracting the number of pupils who repeat the last grade from the total number of students enrolled in the last grade. This is a gross measure and can therefore exceed 100% if there is a large number of pupils who have entered school early or late and/or who have repeated previous grades. The fact that the indicator can exceed 100% also makes it more difficult to interpret than the completion rate. With respect to the completion rate, the gross entry ratio to the last grade does not indicate how many children complete the last grade, but only how many children enter that grade. If final-year students drop out of school before graduation, the gross entry ratio to senior year overestimates completion. Data limitations prevent the number of students dropping out of school during the last year of lower secondary education from being taken into account. Therefore, this rate is a proxy that should be considered as a higher estimate of the actual lower secondary school completion rate.
Percentage of the population aged 15 years and older who currently use any tobacco product (smoked and/or smokeless) on a daily or non-daily basis.
Sources
World Health Organization
Methodology
A statistical model based on a Bayesian negative binomial meta-regression is used to model the prevalence of current tobacco use for each country, separately for men and women. A full description of the method is available in The Lancet, volume 385, no. 9972, pp. 966-976 (2015). Once age- and sex-specific prevalence rates from national surveys were collected into a dataset, the model was adapted to calculate trend estimates from 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 with a 95% credibility interval around the estimate. Depending on the completeness/exhaustiveness of survey data from a particular country, the model sometimes makes use of data from other countries to fill in information gaps. When a country has fewer than two national representative population surveys in different years, no attempt is made to fill in the data gaps and no estimates are calculated. To fill in the data gaps, the information is "borrowed" from countries in the same UN sub-region. The resulting trend lines are used to derive estimates for individual years, so that a number can be reported even if the country did not conduct a survey in that year. To make the results comparable across countries, prevalence rates have been standardized by age compared to the WHO standard population.
Notes
Tobacco products include cigarettes, pipes, cigars, cigarillos, water pipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless tobacco (oral and nasal). Tobacco products exclude e-cigarettes (which do not contain tobacco), e-cigarettes, e-hookahs, JUULs, and e-pipes. Rates are age-standardized relative to the WHO standard population. Estimates for countries with irregular surveys or with many gaps in the data have wide ranges of uncertainty and such results should be interpreted with caution.
Percentage of people in the population living 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 reported that they were exposed to low-quality diets at some time of the year and were forced to reduce the amount of food they would normally eat due to a lack of money or other resources.
Sources
Food and Agriculture Organization
Methodology
data are collected via a survey form in a Gallup World Poll questionnaire.
Notes
The validity and reliability of this data is very high. The margin of error can vary from 0.5% to 10% of the prevalence value depending on the sample size.
Percentage of people aged 20-79 who have type 1 or type 2 diabetes. It is calculated based on a standard age structure of the population.
Sources
International Diabetes Federation
Methodology
The data comes from a variety of sources, such as peer-reviewed scientific articles and national and regional health surveys. Official reports from international organizations, such as the World Health Organization (WHO), have also been evaluated for their quality, defined in agreement with a group of international experts. Data sources that passed rigorous selection criteria were included in the data analysis.
Notes
People with undiagnosed diabetes are included in the estimated total number of people with diabetes for 2021.
Quota per 100 residents of fixed subscriptions for high-speed access to the public Internet (a TCP/IP connection), with downstream speeds of 256 kbit/s or more.
Sources
a) International Telecommunication Union; b) World Bank Development Indicators for Palestine
Methodology
Data is collected directly by governments through annual questionnaires sent to the agency responsible for telecommunications/ICT (regulatory authority or ministry). The data shall be verified and harmonised to ensure international comparability and compliance with international standards, as outlined in the ITU Handbook for Telecommunications/ICT Administrative Data Collection and the Central List of ICT Indicators developed by the Partnership on Measuring ICT for Development. Data is collected twice a year through questionnaires sent to governments. In April, a short questionnaire is sent out asking for data on the main telecommunications/ICT indicators, such as fixed subscriptions, mobile subscriptions, fixed broadband subscriptions (total and by speed levels), international bandwidth, mobile and fixed broadband traffic and mobile population coverage (total, 3G, 4G and beyond). In September, a lengthy questionnaire is sent asking for data on all telecommunications/ICT indicators included in the ITU Manual for the collection of administrative data on telecommunications/ICT. Data is validated and discrepancies clarified through communication with countries before being disseminated.
Notes
Fixed broadband internet includes cable modems, DSL, fiber, and other fixed broadband technologies (such as satellite broadband internet, Ethernet LAN, wireless fixed access, Wireless Local Area Network, WiMAX, etc.) Subscribers who access data communications (including the Internet) via mobile cellular networks are excluded. Advertised and actual speeds may differ substantially. Because survey questions and definitions differ, estimates may not be strictly comparable across countries. In some countries, regulators 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 country values. Regional and global penetration rates (per 100 inhabitants) are weighted averages of country values, weighted by the population of countries/regions. Discrepancies between global and national data can occur when countries use a different definition than the one used by the ITU. Discrepancies can also arise in cases where the end of the tax year differs from the one used by the ITU, which is the end of December each year. Some countries have a fiscal year that ends in March or June of each year.
Share per 100 residents of the sum of the active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local network (WLL) subscriptions, equivalent ISDN voice channels and fixed public pay telephones.
Sources
a) International Telecommunication Union; b) World Bank Development Indicators for Palestine
Methodology
Data is collected directly by governments through annual questionnaires sent to the agency responsible for telecommunications/ICT (regulatory authority or ministry). The data shall be verified and harmonised to ensure international comparability and compliance with international standards, as outlined in the ITU Handbook for Telecommunications/ICT Administrative Data Collection and the Central List of ICT Indicators developed by the Partnership on Measuring ICT for Development. Data is collected twice a year through questionnaires sent to governments. In April, a short form questionnaire is sent out asking for data on the main telecommunications/ICT indicators, such as fixed telephony subscriptions, mobile subscriptions, fixed broadband subscriptions (total and by speed levels), international bandwidth, mobile and fixed broadband traffic and mobile population coverage (total, 3G, 4G and beyond). In September, a long form questionnaire is sent asking for data on all telecommunications/ICT indicators included in the ITU Manual for the collection of administrative data on telecommunications/ICT. Data is validated and discrepancies clarified through communication with countries before being disseminated.
Notes
Operators are traditionally the main source of telecommunications data, so subscription information is widely available for most countries. This gives a general idea of access, but a more accurate measure would be the penetration rate, i.e. the share of households that have access to telecommunications. More information on the use of information and communication technologies has become available in recent years thanks to surveys of households and firms. Data on the actual use of telecommunications services is also important. Ideally, statistics on telecommunications (and other information and communication technologies) should be compiled for all three measures: subscriptions, access and usage. Data quality varies from country to country, due to differences in regulations governing data provision and availability. Discrepancies can also arise in cases where the end of a fiscal year differs from the one used by the International Telecommunication Union, which is the end of December of each year. Some countries have a fiscal year that ends in March or June of each year.
Composite index that measures achievements in three key dimensions of human development: a long and healthy life, access to knowledge, and a decent standard of living. The index is the geometric mean of the normalized indices for each of the three dimensions.
Sources
United Nations Development Programme (UNDP)
Methodology
It is a geometric mean of the normalised indices, based on 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) Average years of schooling: Barro and Lee (2018), ICF Macro Demographic and Health Surveys (various years), OECD (2022), UNESCO Institute of Statistics (2022) and UNICEF Multiple Indicator Cluster Surveys (various years). d) Gross national income per capita: IMF (2022), UNDESA (2022b), United Nations Ststistics Division (2022) and World Bank (2022).
Composite index that corrects the Human Development Index to account for inequality in the distribution of each dimension in the population.
Sources
United Nations Development Programme (UNDP)
Methodology
It is based on a class of distribution-sensitive composite indices proposed by Foster, Lopez-Calva and Szekely (2005), which refers to the Atkinson (1970) family of inequality measures. It is calculated as the geometric mean of the inequality-corrected dimensional indices. Inequality in the distribution of Human Development Index dimensions is estimated by: * Life expectancy, using data from the comprehensive mortality tables provided by UN/DESA (2022a). * Average years of schooling, using data from household surveys 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, UNICEF'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, data on school outcomes from the United Nations Educational, Scientific and Cultural Organization Institute for Statistics, and the World Income Inequality Database from the United Nations University. * Disposable household income or per capita consumption, using the databases and household surveys listed above and, for some countries, imputed income according to a wealth ratio matching methodology using household survey asset ratios (Harttgen and Vollmer 2013). The asset index is provided in microdata from the ICF Macro Demographic and Health Surveys and the United Nations Children's Fund Cluster Surveys.
