People in Mediterranean countries have a higher life expectancy and a particularly marked ageing trend among women. At the same time, infant mortality rates for girls are higher than those for boys, highlighting demographic imbalances and gender-specific health disparities.
Population ages 65 and above, female (% of female population)
Population ages 65 and above, male (% of male population)
Life expectancy at birth, female (years)
Life expectancy at birth, male (years)
Mortality rate, infant, female (per 1,000 live births)
Mortality rate, infant, male (per 1,000 live births)
area_code
ordgeo
Countries
2024
2024
2024
2023
2023
2023
2023
Portugal
52.4
26.6
21.9
85.2
79.5
2.3
2.8
A
1
Spain
50.9
23.1
18.6
86.7
81.2
2.3
2.8
A
2
France
51.5
24.1
19.6
85.9
80.1
3.1
3.7
A
3
Italy
51.0
26.6
21.9
85.1
81.0
2.1
2.5
A
4
Slovenia
49.8
24.3
18.8
85.0
79.1
1.7
2.0
A
5
Croatia
51.8
26.3
19.5
81.7
75.4
3.5
4.2
A
6
Greece
51.6
26.1
21.2
84.2
79.0
2.9
3.4
A
7
Malta
48.1
22.4
17.8
85.3
81.8
4.5
5.2
A
8
Cyprus
49.6
15.7
13.2
83.7
79.6
2.6
3.1
A
9
Serbia
52.6
25.0
19.7
78.7
73.9
4.0
4.9
B
10
Kosovo
50.8
11.0
8.7
80.1
75.8
7.5
9.1
B
11
Bosnia and Herzegovina
52.4
25.9
17.6
80.9
74.4
4.9
5.8
B
12
Montenegro
51.9
20.2
14.9
80.2
75.1
2.0
2.2
B
13
North Macedonia
51.4
19.8
15.7
77.6
73.2
2.7
3.0
B
14
Albania
50.6
17.5
15.7
81.5
77.7
7.4
9.1
B
15
Turkiye
50.1
11.3
8.9
79.9
74.5
8.5
9.7
C
16
Syrian Arab Republic
50.0
5.3
4.2
74.4
69.8
17.0
20.9
C
17
Lebanon
51.4
10.9
9.0
79.7
75.7
14.9
17.0
C
18
Jordan
48.4
4.7
4.2
80.2
75.7
11.0
13.4
C
19
Israel
50.2
13.7
11.2
85.5
81.0
2.5
2.9
C
20
West Bank and Gaza
50.4
4.2
3.5
71.5
59.7
13.3
15.2
C
21
Egypt, Arab Rep.
49.5
5.8
4.3
73.8
69.5
15.0
17.2
D
22
Libya
49.1
5.5
4.5
70.4
68.3
15.1
16.7
D
23
Tunisia
50.6
10.1
8.6
79.2
73.9
9.8
11.3
D
24
Algeria
49.0
6.8
6.1
77.7
74.9
17.9
21.5
D
25
Morocco
49.6
8.5
7.5
77.6
73.2
13.9
17.0
D
26
Some highlighted topics
Demographic structure
The gender composition of the population in the Mediterranean region in 2024, calculated using the feminisation rate, shows a slight prevalence of women in all Western Balkan countries (51.9% of the total for the area) and in most European Union countries (51.3% of the total). In the Middle East, there is a levelling off between the female and male populations, while in North Africa the ratio is reversed, with a slight prevalence of men (49.5%). This indicator is influenced by complex phenomena, such as gender differences in migratory movements and the longevity of the population. The highest value in the Mediterranean region is in Portugal (52.4%), while the lowest is in Malta (48.1%).
A comparison between the sexes in the age structure of the population shows that the age group of 65 years and over is where the differences between women and men are generally most marked. In all Mediterranean countries, the percentage of elderly women is higher than that of elderly men in their respective populations, but with differences within the four macro-areas. The Middle East and North Africa, which have a very low incidence of people aged 65 and over in the total population, which is also reflected in the gender composition (less than 10% of the female and male population), also show a limited gap between the two components of this population group. The EU countries bordering the Mediterranean, partly due to the greater longevity of their population, have higher percentages of older people: on average, one in five men and one in four women. Italy stands out for having the highest incidence of older people in the population (26.6% for women and 21.9% for men). The largest differences, exceeding 5 percentage points, are found in Croatia, Slovenia, Serbia and Montenegro. The smallest gap is found in Jordan (-0.5 percentage points).
Life expectancy
Life expectancy at birth, calculated on the basis of mortality rates by age group in a given year, represents the average number of years a person can expect to live from birth if, throughout their life, the risks of mortality by age remain the same in the future. Therefore, it is considered a measure of the longevity of a population, which differs between males and females in relation to different mortality patterns.
The gender analysis for this indicator highlights the existence of gaps between countries belonging to the four macro-areas, reflecting those present in the total population, with a more favourable level of life expectancy for women (Figure 1). Spain continues to have the highest life expectancy, as it does for the total population (86.7 years for women and 81.2 for men); the most unfavourable, among all countries, is West Bank and Gaza for men (59.7 years) and Libya for women (70.4 years). The gender gap is also highest in West Bank and Gaza (+11.8 years for women). The smallest gap is found in Libya (2.2 years longer for women).
Three types of country groups can be identified in 2023 based on female life expectancy at birth and the difference in life expectancy between the two components of the population. The group with the highest longevity, i.e. with the most favourable values in terms of female life expectancy (over 84 years), consists of seven European Union countries (excluding Croatia and Cyprus) plus Israel, with a low difference in life expectancy between men and women (less than six years).
The second group can be divided into two subtypes. The first, in which female life expectancy is between 80 and 82 years, is found in most Western Balkan countries, Jordan, Israel and Croatia. The second subtype, with values between 77 and 80 years, is found in the Middle East (Turkey, Lebanon and Syria), North Africa (Tunisia, Algeria and Morocco), Macedonia and Serbia. In these groups, the gender gap is less than six years of life, wider in Bosnia and Croatia (about 6.5 years more for women) and smaller in Algeria (+2.8 years for women).
Finally, a third group comprising Libya, West Bank and Gaza, Egypt and Syria presents a more critical picture, with the lowest female life expectancy in Libya (70.4 years) but with the smallest gap in life expectancy between men and women; on the other hand, the widest gender gap is found in West Bank and Gaza.
Figure 1 - Female life expectancy at birth and differential to male life expectancy at birth. Year 2023 (life years)
...
Infant mortality
The gender analysis of infant mortality rates confirms, as for the total population, a disadvantage for North African and Middle Eastern countries, a phenomenon closely linked to the different levels of socio-economic development in the Mediterranean area.
In all countries in the area, infant mortality is higher for males, with a minimal gender differential in the European Union and Western Balkan countries, but the disadvantage increases in the other two macro-areas with high health relevance for the indicator considered, with the largest gap in Syria, Algeria and Morocco (Figure 2).
Figure 2 - Infant mortality rates by gender. Year 2023 (per 1,000 live births)
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Metadata
Indicators
Definition
Percentage of female population (mid-year estimates).
Sources
a) elaborations of World Bank Development Indicators on data from the United Nations Population Division (UNPD), national statistical agencies, Eurostat; b) Istat for Italy
Methodology
The data are derived from different types of statistical sources: national population censuses; estimates for the years before and after the census based on demographic models; administrative data.
Notes
Even in high-income countries, errors and miscalculations occur. In developing countries, errors can be substantial because of limitations in transportation, communications, and other resources needed to conduct and analyze a comprehensive census. The quality and reliability of official demographic data are also influenced by public trust in the government, the government's commitment to a comprehensive and accurate census, the confidentiality and protection against misuse of census data, and the independence of census agencies from political influences. In addition, the comparability of demographic indicators is limited by differences in the concepts, definitions, collection procedures and estimation methods used by national statistical agencies and other organisations collecting data. The timeliness of a census and the availability of complementary data from surveys or registration systems are objective ways of judging the quality of demographic data.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Population and Social Conditions Area
Female population aged 65 years and older as a percentage of the total population as of 1 January each year.
Sources
a) Wemed elaborations on World Bank Development Indicators and United Nations Population Division data; b) Istat for Italy
Methodology
The age structure in the World Bank's demographic estimates is based on the age structure contained in the United Nations Population Division's World Population Prospects. A description of the empirical data used and the methods applied in reviewing past estimates of population and components of demographic change is available for each country in: https://population.un.org/wpp/DataSources/.
Male population aged 65 years and older as a percentage of the total population as of 1 January each year.
Sources
a) Wemed elaborations on World Bank Development Indicators and United Nations Population Division data; b) Istat for Italy
Methodology
The age structure in the World Bank's demographic estimates is based on the age structure contained in the United Nations Population Division's World Population Prospects. A description of the empirical data used and the methods applied in reviewing past estimates of population and components of demographic change is available for each country in: https://population.un.org/wpp/DataSources/.
Number of years a female infant would live if the mortality patterns prevalent at birth remained unchanged throughout life.
Sources
a) elaborations of World Bank Development Indicators on data from the United Nations Population Division (UNPD), national statistical agencies, Eurostat; b) Istat for Italy
Methodology
Life expectancy at birth used here is the average number of years that an infant is expected to live if mortality patterns at the time of its birth remain constant into the future. It reflects the overall mortality level of a population and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated via a mortality table that provides a snapshot of a population's mortality pattern at a given point in time. It therefore does not reflect the mortality pattern that a person actually experiences over the course of their lifetime, which can be calculated in a cohort table.
Notes
The annual World Population Prospects data series of the United Nations Population Division are data interpolated over 5-year periods. Therefore, they may not reflect real events as much as observed data. High mortality in younger age groups significantly lowers life expectancy at birth. But if a person survives a high-mortality childhood, they can live much longer. For example, in a population with a life expectancy at birth of 50 years, there may be few people who die at 50. Life expectancy at birth can be low due to high infant mortality, so a person who survives childhood can live much longer than 50 years.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Population and Social Conditions Area
The number of years a male infant would live if the mortality patterns prevalent at birth remained unchanged throughout life.
Sources
a) elaborations of World Bank Development Indicators on data from the United Nations Population Division (UNPD), national statistical agencies, Eurostat; b) Istat for Italy
Methodology
Life expectancy at birth used here is the average number of years that an infant is expected to live if mortality patterns at the time of its birth remain constant into the future. It reflects the overall mortality level of a population and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated via a mortality table that provides a snapshot of a population's mortality pattern at a given point in time. It therefore does not reflect the mortality pattern that a person actually experiences over the course of their lifetime, which can be calculated in a cohort table.
Notes
The annual World Population Prospects data series of the United Nations Population Division are data interpolated over 5-year periods. Therefore, they may not reflect real events as much as observed data. High mortality in younger age groups significantly lowers life expectancy at birth. But if a person survives a high-mortality childhood, they can live much longer. For example, in a population with a life expectancy at birth of 50 years, there may be few people who die at 50. Life expectancy at birth can be low due to high infant mortality, so a person who survives childhood can live much longer than 50 years.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Population and Social Conditions Area
Number of female infants who die before reaching one year of age, per 1,000 live births in a given year.
Sources
a) World Bank elaborations on United Nations Inter-agency Group data for Child Mortality Estimation (UNCM); b) Istat for Italy
Methodology
Depending on the data source, death rates can be calculated in several ways: a) Population registration - The calculation of infant mortality rates is derived from a mortality table, using age-specific deaths and mid-year population counts from civil registration data. b) Survey and census data - Survey and census data on child mortality under five typically come in one of two forms: the complete medical history (FBH), in which women are asked about the date of birth of each of their children, whether the child is still alive, and, otherwise, the age at the time of death; and the summary medical history (SBH), in which women are asked only the number of babies they have given birth to and the number of those who have died (or, equivalently, the number of those still alive). Both medical histories give rise to retrospective infant mortality rates that refer to a period prior to the date of the investigation. Rates can be derived using a direct estimation method from the FBH. SBH data, collected from censuses and many household surveys, can be used to derive retrospective estimates of infant, infant, and under-five mortality rates using an indirect estimation method, i.e., using a proxy for the time of exposure of the mother's children to the risk of death. The Brass method and the model's mortality tables are used to obtain an indirect estimate of infant and under-five mortality rates. The Istat data for Italy fall under case a) (Survey on deaths and causes of death) and refer to mortality by territory of residence.
Number of male infants who die before reaching one year of age, per 1,000 live births in a given year.
Sources
a) World Bank elaborations on United Nations Inter-agency Group data for Child Mortality Estimation (UNCM); b) Istat for Italy
Methodology
Depending on the data source, death rates can be calculated in several ways: a) Population registration - The calculation of infant mortality rates is derived from a mortality table, using age-specific deaths and mid-year population counts from civil registration data. b) Survey and census data - Survey and census data on child mortality under five typically come in one of two forms: the complete medical history (FBH), in which women are asked about the date of birth of each of their children, whether the child is still alive, and, otherwise, the age at the time of death; and the summary medical history (SBH), in which women are asked only the number of babies they have given birth to and the number of those who have died (or, equivalently, the number of those still alive). Both medical histories give rise to retrospective infant mortality rates that refer to a period prior to the date of the investigation. Rates can be derived using a direct estimation method from the FBH. SBH data, collected from censuses and many household surveys, can be used to derive retrospective estimates of infant, infant, and under-five mortality rates using an indirect estimation method, i.e., using a proxy for the time of exposure of the mother's children to the risk of death. The Brass method and the model's mortality tables are used to obtain an indirect estimate of infant and under-five mortality rates. The Istat data for Italy fall under case a) (Survey on deaths and causes of death) and refer to mortality by territory of residence.
