The WeMed statistical database is the result of a complex work of collection, comparison and selection of data from the most accredited international statistical sources. Relevant differences in the development of the national statistical systems of the mediterranean countries mean that it is difficult to collect complete and comparable statistical information; such difficulties are exacerbated especially in territories affected by current or recent wars[1]. The complete coverage of the 26 countries considered is guaranteed in any case for many indicators by international agencies, also through estimation methodologies and statistical models.
An essential reference point for addressing these issues is the valuable statistical system World Bank Development Indicators, in which the World Bank collects from all international statistical sources and updates annually almost 1,500 national time series for all countries[2]. Analyzing the data for the Mediterranean countries released by this system, a path was followed in several steps: a) selection of indicators; b) collection and validation of data from primary sources; c) collection and validation of metadata.
Selection of indicators
Taking the time series of more than 1,400 indicators as of june 2025 in the World Bank Development Indicators database as a reference, a pre-selection was made of about 1,070 indicators showing a high coverage of the 26 WeMed countries, with the criterion of having at least 20 countries with available time series. Then, these indicators were classified according to a three-level thematic structure: the first two levels (Thematic Areas and Themes) were derived from a Eurostat conceptual grid[3], the third one (Sub-themes) was developed ad hoc in order to facilitate the selection of indicators. Finally, the 159 WeMed indicators were chosen: some of them connected to those in World Bank Development Indicators, plus 4 composite indicators of Human Development annually released by the United Nations Development Programme.
Data collection and validation
Where possible, priority was given to data that can be downloaded directly from the international primary sources indicated and used by the World Bank. This, in order to obtain more controlled and sometimes even more up-to-date data. Furthermore, as far as Italy is concerned, we wanted to give priority to the values published for many indicators by Istat (with the same definition and algorithm as for other countries), so as to avoid inconsistencies with these data. Therefore, the 2001-2024 time series (where available) for all indicators were collected from international sources for the 26 WeMed countries and from Istat for Italy. In addition, time series of the numeratorand denominator values of the indicators were also downloaded, in order to make up for the possible absence of calculated values in the primary sources and to allow the possibility of calculating indicators at a supra-national level.
The time series of the different sources for each indicator and those already published by the World Bank were cross-checked for discrepancies between values by country and year. The discrepancies are mainly attributable to different timing of series updates, or relate to the last year of data update. As a final outcome of the selection process, international primary sources and Istat for Italy were favoured in case of available data and absence of specific problems. On the other hand, it was decided to use the series already published by the World Bank in the following cases: i) if this source is the primary source of data, through its own surveys or estimation procedures; ii) if it has developed ad hoc historical series that are more complete, through the collection from multiple primary sources; iii) if it has supplemented the database of indicators published by the international primary sources with the values of some countries not published by these primary sources (limited to the values of these countries).
The acquisition process was managed through an Access DB, up to the production of reports comparing the sources and the extraction and procession of data from the selected sources to form the WeMed database.
Metadata collection and validation
Along with the collection of statistical data, the information useful to populate a metadata dataset was also collected from the websites of the selected sources: type of survey, estimation procedures, data quality, limits of interpretation, links with SDG indicators, etc. After choosing the source(s) to be used for each indicator, this information was refined to provide a coherent summary of the most relevant aspects.
Dissemination of data and metadata in WeMed
In WeMed, the indicators’ values per country form the reference information base for the 12 thematic pages, where the main findings are highlighted. In addition, data for all indicators can be queried through the dashboard, which allows graphs and maps to be displayed.
Information on indicator metadata can be consulted in three ways:
in the Thematic Pages of the website, for the indicators therein, by clicking on the metadata button;
in the Dashboard of the website, for the selected indicators, by clicking on the metadata button;
at the end of the methodological note in the website, you can download the csv file for all indicators; a shortened version with list, definitions and sources is in Annex 1 of this chapter.
On the contents of the metadata, some details deserve to be added:
the information included in the ‘Methodology’ field is about the methods and techniques of data collection and the rules for processing the indicators;
the information included in the field ‘Notes’ concerns aspects such as details of the indicator's field of observation, limitations to comparability arising from the heterogeneous quality of the data between countries and from the different criteria adopted, warnings for a correct interpretation of the meaning of the indicators;
the information included in the field ‘Presence in policy-oriented information systems’ indicates that some indicators are also envisaged in the context of international statistical systems that address policy choices adopted by the countries. In particular, reference is made to: a) the United Nations 2030 Agenda for Sustainable Development (SDGs), articulated in 17 Goals, to which a system of targets and indicators is linked; b) the European Neighbourhood Policy South (ENP South), for which Eurostat carries out statistical cooperation activities with the countries involved and releases a specific section of its database. See also: https://sdgs.un.org/goals and https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Statistical_cooperation_-_European_Neighbourhood_Policy-South_(ENP-S)#Data_collection_and_dissemination..
Indicatori
#
Indicator
Subject area
Topic
Definition
Source
Methodology
Notes
Policy
Link
1
Population, total
Population and Society
Population
Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.
a) World Bank Development Indicators, from: United Nations Population Division, National Statistical Offices, Eurostat; b) Istat for Italy
Data are collected through different kinds of sources: national population censuses; estimates for the years before and after the census based on demographic models; administrative data.
Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence.
ENP-South Eurostat Data Browser: Area "Population and Social conditions"
2
Population growth (annual %)
Population and Society
Population
Exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage.
a) World Bank Development Indicators, from: United Nations Population Division, National Statistical Offices, Eurostat; b) Istat for Italy
Total population growth rates are calculated on the assumption that rate of growth is constant between two points in time. The growth rate is computed using the exponential growth formula: r = ln(pn/p0)/n, where r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.
Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence.
ENP-South Eurostat Data Browser: Area "Population and Social conditions"
3
Age dependency ratio, old (% of working-age population)
Population and Society
Population
Percentage of older dependents--people older than 64--to the working-age population--those ages 15-64, at the 1st January of each year.
a) Wemed calculations based on data from World Bank Development Indicators and United Nations Population Division; b) Istat for Italy
Age structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects. A description of the empirical data used and the methods applied in revising past estimates of population and components of demographic change is available for each country in: https://population.un.org/wpp/DataSources/.
Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.
ENP-South Eurostat Data Browser: Area "Population and Social conditions"
4
Population ages 0-14 (% of total population)
Population and Society
Population
Population between the ages 0 to 14 as a percentage of the total population at the 1st January of each year.
a) Wemed calculations based on data from World Bank Development Indicators and United Nations Population Division; b) Istat for Italy
Age structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects. A description of the empirical data used and the methods applied in revising past estimates of population and components of demographic change is available for each country in: https://population.un.org/wpp/DataSources/.
None
ENP-South Eurostat Data Browser: Area "Population and Social conditions"
5
Population ages 65 and above (% of total population)
Population and Society
Population
Population ages 65 and above as a percentage of the total population at the 1st January of each year.
a) Wemed calculations based on data from World Bank Development Indicators and United Nations Population Division; b) Istat for Italy
Age structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects. A description of the empirical data used and the methods applied in revising past estimates of population and components of demographic change is available for each country in: https://population.un.org/wpp/DataSources/.
None
ENP-South Eurostat Data Browser: Area "Population and Social conditions"
6
Birth rate, crude (per 1,000 people)
Population and Society
Population
Number of live births occurring during the year, per 1,000 population estimated at midyear.
a) World Bank Development Indicators, from: United Nations Population Division, National Statistical Offices, Eurostat; b) Istat for Italy
The rates are based on data from birth and death registration systems, censuses, and sample surveys by national statistical offices and other organizations, or on demographic analysis. The estimates may be projections based on extrapolations of levels and trends from earlier years or interpolations of population estimates and projections from the United Nations Population Division.
None
ENP-South Eurostat Data Browser: Area "Population and Social conditions"
7
Fertility rate, total (births per woman)
Population and Society
Population
The average number of live births a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates of a given period and if they were not subject to mortality. It is expressed as live births per woman.
a) elaborazioni World Bank Development Indicators su dati di United Nations Population Division (UNPD), agenzie nazionali di statistica, Eurostat; b) Istat per l'Italia
It results from the sum of the specific fertility rates calculated by comparing, for each fertile age, the number of live births to the average annual amount of the female population.
None
ENP-South Eurostat Data Browser: Area "Population and Social conditions"
8
Life expectancy at birth, total (years)
Population and Society
Population
Number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.
a) elaborazioni World Bank Development Indicators su dati di United Nations Population Division, agenzie nazionali di statistica, Eurostat; b) Istat per l'Italia
Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in 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 in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.
Annual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data. High mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years.
ENP-South Eurostat Data Browser: Area "Population and Social conditions"
9
Mortality rate, infant (per 1,000 live births)
Population and Society
Population
Number of infants dying before reaching one year of age, per 1,000 live births in a given year.
a) United Nations Inter-agency Group for Child Mortality Estimation; b) Istat per l'Italia
Depending on the data source, mortality rates can be calculated several ways: a) Vital Registration – The calculation of Infant mortality rates is derived from a standard period abridged life table using the age-specific deaths and mid-year population counts from civil registration data. b) Survey and Census Data (Birth Histories and Sibling Survival Histories) - Survey and census data on under-five child mortality typically come in one of two or forms: the full birth history (FBH), whereby women are asked for the date of birth of each of their children, whether the child is still alive, and if not, the age at death; and the summary birth history (SBH), whereby women are asked only about the number of children they have ever given birth to and the number that have died (or, equivalently, the number still alive). Either birth history results in retrospective child mortality rates referring to some period prior to the survey date. Rates can be derived using a direct estimation method from the FBH. SBH data, collected by censuses and many household surveys, can be used to derive retrospective infant, child and under-five mortality rate estimates by using an indirect estimation method, i.e. a proxy is used for the exposure time of the mother’s children to the risk of death. The Brass method and model life tables are used to obtain an indirect estimate of infant and under-five mortality rates. Istat data for Italy fall into case a) (Vital statistics on causes of death) and refer to mortality by territory of residence.
None
ENP-South Eurostat Data Browser: Area "Population and Social conditions"
10
Prevalence of current tobacco use (% of adults)
Population and Society
Other Social Issues
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.
World Health Organization
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.
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.
None
11
Prevalence of moderate or severe food insecurity in the population (%)
Population and Society
Other Social Issues
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.
Food and Agriculture Organization
data are collected via a survey form in a Gallup World Poll questionnaire.
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.
SDG Goal 2, indicator 2.1.2
12
Diabetes prevalence (% of population ages 20 to 79)
Population and Society
Other Social Issues
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.
International Diabetes Federation
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.
People with undiagnosed diabetes are included in the estimated total number of people with diabetes for 2021.
SDG Goal 3, indicator 3.8.1
13
Lower secondary completion rate, total (% of relevant age group)
Population and Society
Other Social Issues
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.
United Nations Scientific and Cultural Organization (UNESCO)
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.
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.
None
14
Labor force participation rate for ages 15-24, total (%) (modeled ILO estimate)
Population and Society
Labor Market
Percentage of the population aged 15-24 who are economically active: all people who offer labour on the market for the production of goods and services in a given period.
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
National data on labour force participation rates may not be comparable due to differences in concepts and methodologies. The most important factor affecting the comparability of data is the source of the data itself. Labour force data obtained from population censuses are often based on a limited number of questions about individuals' economic characteristics, with little scope for sampling. The resulting data are therefore generally not consistent with the corresponding labour force survey data and may vary considerably from one country to another, depending on the number and type of questions included in the census. Censuses and surveys of local units can, by their nature, only provide data on the employed population, excluding the unemployed and, in many countries, also excluding workers engaged in small production units or in the informal economy who do not fall within the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. However, despite their strength, labour force survey data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and the inclusion or exclusion of conscripted military personnel. In addition, there are variations in national definitions of the labour force concept, particularly with regard to the statistical treatment of certain specific groups, such as 'contributing family workers' and 'unemployed persons available for work but not seeking employment'. Non-comparability may also arise from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the labour force, with a cut-off point at 65 or 70 years, which affects broad comparisons, particularly those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of the ILO's modelled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the entire country (without geographical limitations) were used in the construction of the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates derived from this source were chosen in favour of those derived from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the 13th ICLS.
ENP-South Eurostat Data Browser: Population and Social Conditions Area
15
Labor force participation rate, total (% of total population ages 15-64) (modeled ILO estimate)
Population and Society
Labor Market
Percentage of the population aged 15-64 economically active: all people who offer labour on the market for the production of goods and services in a given period.
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
National data on labour force participation rates may not be comparable due to differences in concepts and methodologies. The most important factor affecting data comparability is the source of the data. Labour force data obtained from population censuses are often based on a limited number of questions about the economic characteristics of individuals, with little chance of survey. The resulting data, therefore, are generally not consistent with the corresponding LFS data and may vary considerably from one country to another, depending on the number and type of questions included in the census. Censuses and surveys of establishments can, by their nature, provide only data on the employed population, excluding the unemployed and, in many countries, also excluding workers engaged in small establishments or in the informal economy who are not covered by the survey or census.
ENP-South Eurostat Data Browser: Population and Social Conditions Area
16
Employment in agriculture (% of total employment) (modeled ILO estimate)
Population and Society
Labor Market
Persons of working age engaged in the agricultural sector in any activity of production of goods or provision of services for consideration or profit, whether working during the reference period or not working due to a temporary absence from work or an agreement on working time. The agricultural sector consists of agriculture, hunting, forestry and fishing, according to division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
The data presented by economic activity branch are based on the International Standard Industrial Classification of All Economic Activities (ISIC). Its main purpose is to provide a set of activity categories that can be used for the collection and reporting of statistics. The original version of ISIC was adopted in 1948 and has since been revised four times: in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of ISIC Rev. 3 was introduced in 2002 to take account of substantial changes in the economic structure of many countries (ISIC Rev. 3.1). It is important to note that countries may use different versions of ISIC, and that countries move to the adoption of the latest version at different times. A country may continue to use the previous version even after starting a new data series according to the most recent version. Although these different classification systems may have an impact on comparability at detailed levels of economic activity, changes from one ISIC to another should not have a significant impact on the information for the three broad sectors presented in ILOSTAT. A number of factors may limit the comparability of employment statistics by economic activity between countries or over time. The comparability of employment statistics between countries is significantly affected by variations in the definitions used for employment data. Differences may arise from age coverage, such as lower and upper age limits for labour force activity. Employment estimates may also vary depending on whether members of the armed forces are included. When the armed forces are included in the measurement of employment, they are usually assigned to the service sector. Therefore, in countries that do not include the armed forces, the service sector tends to be underestimated compared to countries where they are included. Another area of measurement difference concerns the national treatment of particular groups of workers. The international definition of employment includes all persons who worked for at least one hour during the reference period. Workers may be paid or self-employed, even in less obvious forms of work, some of which are discussed in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeships or non-market production. Most exceptions to the coverage of all employed persons in a labour force survey have to do with minor national variations from the international recommendation applicable to alternative employment statuses. For example, some countries measure only paid employees, while others measure 'all employed persons', i.e. paid workers and business owners who receive remuneration based on company shares. Other possible variations to the rules for measuring total employment include hour limits (over one hour) imposed on family members who contribute before being included in employment. Comparisons can also be problematic when the frequency of data collection varies. The interval for collecting information can range from one month to 12 months in a year. Since seasonality of various kinds is undoubtedly present in all countries, employment data may vary for this reason alone. In addition, changes in the level of employment may occur during the year, but this may be obscured when fewer observations are available.
ENP-South Eurostat Data Browser: Population and Social Conditions Area
17
Employment in industry (% of total employment) (modeled ILO estimate)
Population and Society
Labor Market
Persons of working age engaged in the industrial sector in any activity of production of goods or provision of services for consideration or profit, whether they are working during the reference period or not working because of a temporary absence from work or an agreement on working time. The industrial sector includes mineral extraction, manufacturing, construction and utilities (electricity, gas and water), according to divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) orcategories B-F (ISIC 4).
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
The data presented by economic activity branch are based on the International Standard Industrial Classification of All Economic Activities (ISIC). Its main purpose is to provide a set of activity categories that can be used for the collection and reporting of statistics. The original version of ISIC was adopted in 1948 and has since been revised four times: in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of ISIC Rev. 3 was introduced in 2002 to take account of substantial changes in the economic structure of many countries (ISIC Rev. 3.1). It is important to note that countries may use different versions of ISIC, and that countries move to the adoption of the latest version at different times. A country may continue to use the previous version even after starting a new data series according to the latest version. Although these different classification systems may have an impact on comparability at detailed levels of economic activity, changes from one ISIC to another should not have a significant impact on the information for the three broad sectors presented in ILOSTAT. A number of factors may limit the comparability of employment statistics by economic activity between countries or over time. The comparability of employment statistics between countries is significantly affected by variations in the definitions used for employment data. Differences may arise from age coverage, such as lower and upper age limits for labour force activity. Employment estimates may also vary depending on whether members of the armed forces are included. When the armed forces are included in the measurement of employment, they are usually assigned to the service sector. Therefore, in countries that do not include the armed forces, the service sector tends to be underestimated compared to countries where they are included. Another area of measurement difference concerns the national treatment of particular groups of workers. The international definition of employment includes all persons who worked for at least one hour during the reference period. Workers may be paid or self-employed, even in less obvious forms of work, some of which are discussed in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeships or non-market production. Most exceptions to the coverage of all employed persons in a labour force survey have to do with minor national variations from the international recommendation applicable to alternative employment statuses. For example, some countries measure only paid employees, while others measure 'all employed persons', i.e. paid workers and business owners who receive remuneration based on company shares. Other possible variations to the rules for measuring total employment include hour limits (over one hour) imposed on family members who contribute before being included in employment. Comparisons can also be problematic when the frequency of data collection varies. The interval for collecting information can range from one month to 12 months in a year. Since seasonality of various kinds is undoubtedly present in all countries, employment data may vary for this reason alone. In addition, changes in the level of employment may occur during the year, but this may be obscured when fewer observations are available.
ENP-South Eurostat Data Browser: Population and Social Conditions Area
18
Employment in services (% of total employment) (modeled ILO estimate)
Population and Society
Labor Market
Persons of working age engaged in the Services sector in any activity of producing goods or providing services for remuneration or profit, whether they were at work during the reference period, or not at work due to a temporary absence from a workplace or an agreement on working time. The sectorof services includes wholesale and retail trade, restaurants and hotels, transport, warehousing and communications, financing, insurance, real estate and business services, as well as social and personal services, according to divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
The data presented by economic activity branch are based on the International Standard Industrial Classification of All Economic Activities (ISIC). Its main purpose is to provide a set of activity categories that can be used for the collection and reporting of statistics. The original version of ISIC was adopted in 1948 and has since been revised four times: in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of ISIC Rev. 3 was introduced in 2002 to take account of substantial changes in the economic structure of many countries (ISIC Rev. 3.1). It is important to note that countries may use different versions of ISIC, and that countries move to the adoption of the latest version at different times. A country may continue to use the previous version even after starting a new data series according to the latest version. Although these different classification systems may have an impact on comparability at detailed levels of economic activity, changes from one ISIC to another should not have a significant impact on the information for the three broad sectors presented in ILOSTAT. A number of factors may limit the comparability of employment statistics by economic activity between countries or over time. The comparability of employment statistics between countries is significantly affected by variations in the definitions used for employment data. Differences may arise from age coverage, such as lower and upper age limits for labour force activity. Employment estimates may also vary depending on whether members of the armed forces are included. When the armed forces are included in the measurement of employment, they are usually assigned to the service sector. Therefore, in countries that do not include the armed forces, the service sector tends to be underestimated compared to countries where they are included. Another area of measurement difference concerns the national treatment of particular groups of workers. The international definition of employment includes all persons who worked for at least one hour during the reference period. Workers may be paid or self-employed, even in less obvious forms of work, some of which are discussed in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeships or non-market production. Most exceptions to the coverage of all employed persons in a labour force survey have to do with minor national variations from the international recommendation applicable to alternative employment statuses. For example, some countries measure only paid employees, while others measure 'all employed persons', i.e. paid workers and business owners who receive remuneration based on company shares. Other possible variations to the rules for measuring total employment include hour limits (over one hour) imposed on family members who contribute before being included in employment. Comparisons can also be problematic when the frequency of data collection varies. The interval for collecting information can range from one month to 12 months in a year. Since seasonality of various kinds is undoubtedly present in all countries, employment data may vary for this reason alone. In addition, changes in the level of employment may occur during the year, but this may be obscured when fewer observations are available.
ENP-South Eurostat Data Browser: Population and Social Conditions Area
19
Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)
Population and Society
Labor Market
Percentage of a country's employed population in the 15-24 age group. Employment is defined as persons of working age who, during a short reporting period, have been engaged in any activity of producing goods or providing services for remuneration or profit, whether they were at work during the reporting period (i.e. worked at a workplace for at least one hour) or were not at work due to temporary absence from a post or agreements on working time. The age between 15 and 24 is generally considered the reference for the young population.
a) International Labour Organization Modelled Estimates (ILOEST)
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
None
None
20
Employers, total (% of total employment) (modeled ILO estimate)
Population and Society
Labor Market
Workers who, working on their own account or with one or more partners, carry out jobs in which the salary depends directly on the profits deriving from the goods and services produced, and who, in this capacity, have hired, on a continuous basis, one or more people who work for them as employees.
a) International Labour Organization Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, labour force surveys are undoubtedly the most comprehensive source. However, despite their strength, labour force survey data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and the inclusion or exclusion of conscripted military personnel. In addition, there are variations in national definitions of the labour force concept, particularly with regard to the statistical treatment of certain specific groups, such as 'contributing family workers' and 'unemployed persons available for work but not seeking employment'. Non-comparability may also arise from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the labour force, with a cut-off point at 65 or 70 years, which affects broad comparisons, particularly those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of the ILO's modelled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the entire country (without geographical limitations) were used in the construction of the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates derived from this source were chosen in favour of those derived from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the 13th ICLS.
None
21
Self-employed, total (% of total employment) (modeled ILO estimate)
Population and Society
Labor Market
People who, working on their own account or with one or more members or in a cooperative, carry out jobs in which remuneration depends directly on the profits deriving from the goods and services produced. The self-employed comprise four subcategories: employers, self-employed persons, members of producer cooperatives and family workers.
a) International Labour Organization Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
None
None
22
Wage and salaried workers, total (% of total employment) (modeled ILO estimate)
Population and Society
Labor Market
People who perform the type of work defined as "subordinate work", where employees have explicit (written or oral) or implicit employment contracts that give them a basic wage that does not depend directly on the income of the unit for which they work.
a) International Labour Organization Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
23
Vulnerable employment, total (% of total employment) (modeled ILO estimate)
Population and Society
Labor Market
Family workers and own-account workers as a percentage of total employment.
a) International Labour Organization Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
24
Contributing family workers, total (% of total employment) (modeled ILO estimate)
Population and Society
Labor Market
People who are "self-employed" as workers on their own account in a market-oriented business run by a person living in the same household.
a) International Labour Organization Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
25
Unemployment, total (% of total labor force) (modeled ILO estimate)
Population and Society
Labor Market
Share of the workforce that does not have a job but is available and looking for a job.
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for countries for which this procedure is possible. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
The unemployed include all persons of working age who: (a) were not in employment during the reference period, i.e. were not in paid employment or self-employment; (b) were currently available for employment, i.e. were available for paid employment or self-employment during the reference period; (c) were looking for a job, i.e. had taken specific actions in a certain recent period to seek paid employment or self-employment. Future start-ups, i.e. people who have not looked for work but have a future interest in the labour market (have made arrangements for a future start of work), as well as participants in vocational training or retraining programmes as part of employment promotion programmes, who were not "employed" on this basis, are also counted as unemployed. they were not "currently available" and did not "look for work" because they had a job offer to start within a short subsequent period, generally no longer than three months. The unemployed also include "unemployed" people who have migrated abroad to work for pay or profit, but who were still waiting for the opportunity to leave. A country's overall unemployment rate is a widely used measure of unused labor supply. Unemployment rates for specific groups, defined by age, gender, occupation or industry, are also useful for identifying the groups of workers and sectors most vulnerable to unemployment.
SDG Goal 8, indicator 8.5.2; ENP-South Eurostat Data Browser: Population and Social Conditions Area
26
Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate)
Population and Society
Labor Market
Share of the labour force aged 15-24 who are unemployed but available and looking for work.
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for countries for which this procedure is possible. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
The unemployed include all persons of working age who: (a) were not in employment during the reference period, i.e. were not in paid employment or self-employment; (b) were currently available for employment, i.e. were available for paid employment or self-employment during the reference period; (c) were looking for a job, i.e. had taken specific actions in a certain recent period to seek paid employment or self-employment. Future start-ups, i.e. people who have not looked for work but have a future interest in the labour market (have made arrangements for a future start of work), as well as participants in vocational training or retraining programmes as part of employment promotion programmes, who were not "employed" on this basis, are also counted as unemployed. they were not "currently available" and did not "look for work" because they had a job offer to start within a short subsequent period, generally no longer than three months. The unemployed also include "unemployed" people who have migrated abroad to work for pay or profit, but who were still waiting for the opportunity to leave. A country's overall unemployment rate is a widely used measure of unused labor supply. Unemployment rates for specific groups, defined by age, gender, occupation or industry, are also useful for identifying the groups of workers and sectors most vulnerable to unemployment.
ENP-South Eurostat Data Browser: Population and Social Conditions Area
27
Fixed broadband subscriptions (per 100 people)
Population and Society
Other Social Issues
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.
a) International Telecommunication Union; b) World Bank Development Indicators for Palestine
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.
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.
None
28
Individuals using the Internet (% of population)
Population and Society
Other Social Issues
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.
a) International Telecommunication Union; b) World Bank Development Indicators for Palestine
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.
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.
SDG Goal 17, indicator 17.8.1
29
Mobile cellular subscriptions (per 100 people)
Population and Society
Other Social Issues
Share per 100 residents of subscriptions to a public mobile phone service that provides access to the PSTN network using cellular technology.
World Bank Development Indicators elaborations on International Telecommunication Union data
Data on mobile cellular subscribers is obtained using administrative data that countries (usually the telecommunications regulator or the ministry responsible for telecommunications) collect regularly, and at least annually, from telecommunications operators. Data for this indicator are readily available for around 90% of countries, either through the ITU questionnaires on global telecommunications indicators, or through official information available on the website of the Ministry or regulator. Otherwise, the information can be aggregated through operator data (mainly through annual reports) and supplemented by market research reports.
The indicator includes post-paid and prepaid subscriptions and includes analogue and digital cellular systems using cellular technology, including the number of active prepaid SIM cards in the last three months. This includes analogue and digital cellular subscriptions (IMT-2000 (Third Generation, 3G) and 4G), but excludes mobile broadband subscriptions via data cards or USB modems. All cellular subscriptions offering voice communications are also included, while subscriptions to public mobile data services, private mobile radio, telepoint or radio paging and telemetry services are also excluded. .
None
30
Indice di sviluppo umano
Population and Society
Other Social Issues
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.
United Nations Development Programme (UNDP)
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).
None
None
31
Indice di sviluppo umano inequality adjusted
Population and Society
Other Social Issues
Composite index that corrects the Human Development Index to account for inequality in the distribution of each dimension in the population.
United Nations Development Programme (UNDP)
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.
None
None
32
GDP (constant 2015 US$)
Economy
Macroeconomics and Public Finance
GDP at purchase prices is the sum of the gross value added of all producers resident in the economy, plus taxes on products and minus subsidies not included in the value of products. It is calculated without making deductions for the depreciation of manufactured goods or for the depletion and degradation of natural resources. Data are at constant 2015 prices, expressed in US dollars. GDP dollar figures are converted from national currencies using official 2015 exchange rates. For some countries where the official exchange rate does not reflect the actual rate applied to foreign currency transactions, an alternative conversion factor is used.
World Bank Development Indicators elaborations on World Bank and OECD data
Gross domestic product (GDP) is the sum of the value added of all producers plus any taxes on the products and minus any subsidies not included in the value of the products. Value added is the value of the gross output of producers minus the value of intermediate goods and services consumed in production, before accounting for the consumption of fixed capital in production. The United Nations System of National Accounts provides that value added is valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both assessments exclude transport costs which are invoiced separately by the manufacturers. Total GDP is measured at purchase prices. The added value of industry is normally measured at basic prices.
The growth of an economy is measured by the change in the volume of its output or the real incomes of its residents. The 2008 United Nations System of National Accounts (SCN 2008) offers three plausible indicators for calculating growth: gross domestic product (GDP) volume, real gross domestic income, and real gross national income. The volume of GDP is the sum of the value added, measured at constant prices, by households, general government and the industries operating in the economy. GDP takes into account all domestic output, regardless of whether the income is allocated to domestic or foreign institutions. Among the difficulties that the compilers of national accounts face is the extension of undeclared economic activity in the informal or secondary economy. In developing countries, a large share of agricultural production is not traded (because it is consumed within the family) or is not exchanged for money. Agricultural production often has to be estimated indirectly, using a combination of methods that involve estimates of inputs, yields and cultivated areas. This approach sometimes leads to rough approximations that may differ from real values over time and between crops for reasons other than climatic conditions or agricultural techniques. Similarly, agricultural inputs that cannot be easily attributed to specific outputs are often "compensated" using equally crude and ad hoc approximations. Note: Data for OECD countries are based on ISIC, Revision 4.
None
33
GDP (current US$)
Economy
Macroeconomics and Public Finance
GDP at purchase prices is the sum of the gross value added of all producers resident in the economy, plus taxes on products and minus subsidies not included in the value of products. It is calculated without making deductions for the depreciation of manufactured goods or for the depletion and degradation of natural resources. Data are at constant 2015 prices, expressed in US dollars. GDP dollar figures are converted from national currencies using official 2015 exchange rates. For some countries where the official exchange rate does not reflect the actual rate applied to foreign currency transactions, an alternative conversion factor is used.
World Bank Development Indicators elaborations on World Bank and OECD data
Gross domestic product (GDP) is the sum of the value added of all producers plus any taxes on the products and minus any subsidies not included in the value of the products. Value added is the value of the gross output of producers minus the value of intermediate goods and services consumed in production, before accounting for the consumption of fixed capital in production. The United Nations System of National Accounts provides that value added is valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both assessments exclude transport costs which are invoiced separately by the manufacturers. Total GDP is measured at purchase prices. The added value of industry is normally measured at basic prices.
The growth of an economy is measured by the change in the volume of its output or the real incomes of its residents. The 2008 United Nations System of National Accounts (SCN 2008) offers three plausible indicators for calculating growth: gross domestic product (GDP) volume, real gross domestic income, and real gross national income. The volume of GDP is the sum of the value added, measured at constant prices, by households, general government and the industries operating in the economy. GDP takes into account all domestic output, regardless of whether the income is allocated to domestic or foreign institutions. Among the difficulties that the compilers of national accounts face is the extension of undeclared economic activity in the informal or secondary economy. In developing countries, a large share of agricultural production is not traded (because it is consumed within the family) or is not exchanged for money. Agricultural production often has to be estimated indirectly, using a combination of methods that involve estimates of inputs, yields and cultivated areas. This approach sometimes leads to rough approximations that may differ from real values over time and between crops for reasons other than climatic conditions or agricultural techniques. Similarly, agricultural inputs that cannot be easily attributed to specific outputs are often "compensated" using equally crude and ad hoc approximations. Note: Data for OECD countries are based on ISIC, Revision 4.
ENP-South Eurostat Data Browser: "Economics and Finance" Area
34
GDP growth (annual %)
Economy
Macroeconomics and Public Finance
Annual percentage growth rate of GDP at constant market prices in local currency.
World Bank Development Indicators elaborations on World Bank and OECD data
Gross domestic product (GDP) is the sum of the value added of all producers plus any taxes on the products and minus any subsidies not included in the value of the products. Value added is the value of the gross output of producers minus the value of intermediate goods and services consumed in production, before accounting for the consumption of fixed capital in production. The United Nations System of National Accounts provides that value added is valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both assessments exclude transport costs which are invoiced separately by the manufacturers. Total GDP is measured at purchase prices. The added value of industry is normally measured at basic prices.
The growth of an economy is measured by the change in the volume of its output or the real incomes of its residents. The 2008 United Nations System of National Accounts (SCN 2008) offers three plausible indicators for calculating growth: gross domestic product (GDP) volume, real gross domestic income, and real gross national income. The volume of GDP is the sum of the value added, measured at constant prices, by households, general government and the industries operating in the economy. GDP takes into account all domestic output, regardless of whether the income is allocated to domestic or foreign institutions. Among the difficulties that the compilers of national accounts face is the extension of undeclared economic activity in the informal or secondary economy. In developing countries, a large share of agricultural production is not traded (because it is consumed within the family) or is not exchanged for money. Agricultural production often has to be estimated indirectly, using a combination of methods that involve estimates of inputs, yields and cultivated areas. This approach sometimes leads to rough approximations that may differ from real values over time and between crops for reasons other than climatic conditions or agricultural techniques. Similarly, agricultural inputs that cannot be easily attributed to specific outputs are often "compensated" using equally crude and ad hoc approximations. Note: Data for OECD countries are based on ISIC, Revision 4.
SDG Goal 8, indicator 8.1.1; ENP-South Eurostat Data Browser: "Economics and Finance" Area
35
GDP per capita (constant 2015 US$)
Economy
Macroeconomics and Public Finance
Gross domestic product divided by population in the middle of the year.
World Bank Development Indicators elaborations on World Bank and OECD data
Gross domestic product (GDP) is the sum of the value added of all producers plus any taxes on the products and minus any subsidies not included in the value of the products. Value added is the value of the gross output of producers minus the value of intermediate goods and services consumed in production, before accounting for the consumption of fixed capital in production. The United Nations System of National Accounts provides that value added is valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both assessments exclude transport costs which are invoiced separately by the manufacturers. Total GDP is measured at purchase prices. The added value of industry is normally measured at basic prices.
The growth of an economy is measured by the change in the volume of its output or the real incomes of its residents. The 2008 United Nations System of National Accounts (SCN 2008) offers three plausible indicators for calculating growth: gross domestic product (GDP) volume, real gross domestic income, and real gross national income. The volume of GDP is the sum of the value added, measured at constant prices, by households, general government and the industries operating in the economy. GDP takes into account all domestic output, regardless of whether the income is allocated to domestic or foreign institutions. Among the difficulties that the compilers of national accounts face is the extension of undeclared economic activity in the informal or secondary economy. In developing countries, a large share of agricultural production is not traded (because it is consumed within the family) or is not exchanged for money. Agricultural production often has to be estimated indirectly, using a combination of methods that involve estimates of inputs, yields and cultivated areas. This approach sometimes leads to rough approximations that may differ from real values over time and between crops for reasons other than climatic conditions or agricultural techniques. Similarly, agricultural inputs that cannot be easily attributed to specific outputs are often "compensated" using equally crude and ad hoc approximations. Note: Data for OECD countries are based on ISIC, Revision 4.
ENP-South Eurostat Data Browser: "Economics and Finance" Area
36
GDP per capita, PPP (constant 2021 international $)
Economy
Macroeconomics and Public Finance
Gross domestic product converted into international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States.
elaborations of World Bank Development Indicators on data from the World Bank International Comparison Program and Eurostat-OECD PPP Programme
Purchasing power parity GDP (PPP) is the gross domestic product converted into international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchase prices is the sum of the gross value added of all producers resident in the country, plus taxes on products and minus subsidies not included in the value of products. It is calculated without deducting the depreciation of manufactured goods or the depletion and degradation of natural resources.
For the comparability of individual sectors, labour productivity is estimated according to national accounting conventions. However, there are still significant limitations in the availability of reliable data. Information on consistent production series in both national currency and dollars at purchasing power parity is not readily available, especially in developing countries, because the definition, coverage and methodology are not always consistent across countries. For example, countries use different methodologies to estimate shortfalls for non-market service sectors and use different definitions of the informal sector.
None
37
GDP per person employed (constant 2021 PPP $)
Economy
Macroeconomics and Public Finance
Gross domestic product (GDP) divided by the total number of people employed in the economy. GDP at purchasing power parity (PPP) is GDP converted to constant 2017 international dollars using PPP rates. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States.
World Bank Development Indicators elaborations on ILO, United Nations Population Division, Eurostat, OECD and World Bank data
The estimates are based on employment, population, GDP and PPP data provided by the ILO, the United Nations Population Division, Eurostat, the OECD and the World Bank.
For the comparability of individual sectors, labour productivity is estimated according to national accounting conventions. However, there are still significant limitations in the availability of reliable data. Information on consistent production series in both national currency and dollars at purchasing power parity is not readily available, especially in developing countries, because the definition, coverage and methodology are not always consistent across countries. For example, countries use different methodologies to estimate shortfalls for non-market service sectors and use different definitions of the informal sector.
None
38
Agriculture, forestry, and fishing, value added (% of GDP)
Economy
Macroeconomics and Public Finance
Agriculture, forestry and fisheries correspond to ISIC divisions 1-3 and include forestry, hunting and fishing, as well as crops and livestock. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without deducting the depreciation of manufactured assets or the depletion and degradation of natural resources. Reference is made to the International Standard Industrial Classification (ISIC), revision 4.
WeMed elaborations on World Bank Development Indicators data
Value added is the value of the gross output of producers minus the value of intermediate goods and services consumed in production, before accounting for the consumption of fixed capital in production. The United Nations System of National Accounts provides that value added is valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both assessments exclude transport costs which are invoiced separately by the manufacturers.
Agricultural production often has to be estimated indirectly, using a combination of methods that involve estimates of inputs, yields and cultivated areas. This approach sometimes leads to rough approximations that may differ from real values over time and between crops for reasons other than climatic conditions or agricultural techniques. Similarly, agricultural inputs that cannot be easily attributed to specific productions are often "networked" using equally crude and ad hoc approximations.
ENP-South Eurostat Data Browser: "Economics and Finance" Area
39
Agriculture, forestry, and fishing, value added (annual % growth)
Economy
Macroeconomics and Public Finance
Annual growth rate of the added value of agriculture, forestry and fisheries at constant local currency. Aggregates are based on 2015 constant prices, expressed in US dollars. Agriculture corresponds to ISIC divisions 01-03 and includes forestry, hunting and fishing, as well as crop cultivation and livestock. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without deducting the depreciation of manufactured goods or the depletion and degradation of natural resources. Reference is made to the International Standard Industrial Classification (ISIC), revision 4.
World Bank Development Indicators elaborations on World Bank and OECD data
Value added is the value of the gross output of producers minus the value of intermediate goods and services consumed in production, before accounting for the consumption of fixed capital in production. The United Nations System of National Accounts provides that value added is valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both assessments exclude transport costs which are invoiced separately by the manufacturers.
Agricultural production often has to be estimated indirectly, using a combination of methods that involve estimates of inputs, yields and cultivated areas. This approach sometimes leads to rough approximations that may differ from real values over time and between crops for reasons other than climatic conditions or agricultural techniques. Similarly, agricultural inputs that cannot be easily attributed to specific productions are often "networked" using equally crude and ad hoc approximations.
None
40
Industry (including construction), value added (% of GDP)
Economy
Macroeconomics and Public Finance
Industry (including construction) corresponds to ISIC divisions 05-43 and includes manufacturing industry (ISIC divisions 10-33). It includes value added from mining, manufacturing (also reported as a separate subgroup), construction, electricity, water and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without deducting the depreciation of manufactured assets or the depletion and degradation of natural resources. Reference is made to the International Standard Industrial Classification (ISIC), revision 4.
WeMed elaborations on World Bank Development Indicators data
Value added is the value of the gross output of producers minus the value of intermediate goods and services consumed in production, before accounting for the consumption of fixed capital in production. The United Nations System of National Accounts provides that value added is valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both assessments exclude transport costs which are invoiced separately by the manufacturers.
Ideally, industrial production should be measured through censuses and periodic surveys of enterprises. However, in most developing countries such surveys are infrequent, so the results of previous surveys must be extrapolated using an appropriate indicator. The choice of the sampling unit, which can be the enterprise (where responses can be based on financial records) or the local unit (where production units can be recorded separately), also affects the quality of the data. In addition, much of industrial production is organized in enterprises that are not incorporated or managed by the owners, which are not detected by surveys aimed at the formal sector. Even in large industries, where regular investigations are more likely, evasion of excise duties and other taxes and failure to disclose income lower estimates of value-added. These problems are exacerbated when countries move from state control of industry to private enterprise, because new businesses and a growing number of established firms do not declare. In accordance with the System of National Accounts, production should include all these unreported activities, as well as the value of illegal activities and other unregistered, informal or small-scale transactions. Data on these activities must be collected using techniques other than traditional business surveys. Data for OECD countries are based on ISIC, revision 4.
ENP-South Eurostat Data Browser: "Economics and Finance" Area
41
Industry (including construction), value added (annual % growth)
Economy
Macroeconomics and Public Finance
Annual growth rate of industrial value added (including construction) in constant local currency. Aggregates are based on 2015 constant prices, expressed in US dollars. Industry corresponds to ISIC divisions 05-43 and includes manufacturing (ISIC divisions 10-33). It includes value added from mining, manufacturing (also reported as a separate subgroup), construction, electricity, water and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without deducting the depreciation of manufactured assets or the depletion and degradation of natural resources. Reference is made to the International Standard Industrial Classification (ISIC), revision 4.
World Bank Development Indicators elaborations on World Bank and OECD data
Value added is the value of the gross output of producers minus the value of intermediate goods and services consumed in production, before accounting for the consumption of fixed capital in production. The United Nations System of National Accounts provides that value added is valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both assessments exclude transport costs which are invoiced separately by the manufacturers.
Ideally, industrial production should be measured through censuses and periodic surveys of enterprises. However, in most developing countries such surveys are infrequent, so the results of previous surveys must be extrapolated using an appropriate indicator. The choice of the sampling unit, which can be the enterprise (where responses can be based on financial records) or the local unit (where production units can be recorded separately), also affects the quality of the data. In addition, much of industrial production is organized in enterprises that are not incorporated or managed by the owners, which are not detected by surveys aimed at the formal sector. Even in large industries, where regular investigations are more likely, evasion of excise duties and other taxes and failure to disclose income lower estimates of value-added. These problems are exacerbated when countries move from state control of industry to private enterprise, because new businesses and a growing number of established firms do not declare. In accordance with the System of National Accounts, production should include all these unreported activities, as well as the value of illegal activities and other unregistered, informal or small-scale transactions. Data on these activities must be collected using techniques other than traditional business surveys. Data for OECD countries are based on ISIC, revision 4.
None
42
Medium and high-tech manufacturing value added (% manufacturing value added)
Economy
Macroeconomics and Public Finance
Percentage of the value added of medium and high-tech industry in the total value added of manufacturing industry.
elaborations of World Bank Development Indicators on data from the United Nations Industrial Development Organization (UNIDO)
Data are collected using the General Questionnaire on Industrial Statistics, compiled by the National Institutes of Statistics and sent annually to UNIDO. Data for OECD countries are obtained directly from the OECD. Country data is also collected from official publications and official websites. The missing values at national level are imputed according to the methodology of the Competitive Industrial Performance Report (UNIDO, 2017).
Conversion to dollars or differences in ISIC combinations can cause discrepancies between national and international data.
SDG Goal 9, indicator 9.b.1
43
Services, value added (% of GDP)
Economy
Macroeconomics and Public Finance
Services correspond to ISIC divisions 45-99 and include the added value of wholesale and retail trade (including hotels and restaurants), transportation, and governmental, financial, professional, and personal services such as education, healthcare, and real estate services. Also included are charges charged for banking services, import duties, and any statistical discrepancies detected by domestic compilers, as well as discrepancies resulting from downsizing. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for the depreciation of manufactured goods or for the depletion and degradation of natural resources. Reference is made to the International Standard Classification of Industries (ISIC), revision 3 or 4.
WeMed elaborations on World Bank Development Indicators data
Value added is the value of the gross output of producers minus the value of intermediate goods and services consumed in production, before accounting for the consumption of fixed capital in production. The United Nations System of National Accounts provides that value added is valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both assessments exclude transport costs which are invoiced separately by the manufacturers.
In the service sector, the many self-employed and sole proprietorships are sometimes difficult to identify and have little incentive to respond to surveys, let alone declare all their earnings. Compounding these problems are the many forms of economic activity that go unregistered, including the work that women and children do for little or no pay.
ENP-South Eurostat Data Browser: "Economics and Finance" Area
44
Services, value added (annual % growth)
Economy
Macroeconomics and Public Finance
Annual growth rate of the value added of services in constant local currency. Aggregates are based on 2015 constant prices, expressed in US dollars. The services correspond to ISIC divisions 45-99. They include value added in wholesale and retail trade (including hotels and restaurants), transportation, and governmental, financial, professional, and personal services such as education, healthcare, and real estate services. Also included are charges charged for banking services, import duties, and any statistical discrepancies detected by domestic compilers, as well as discrepancies resulting from downsizing. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for the depreciation of manufactured goods or for the depletion and degradation of natural resources. The industrial origin of the value added is determined by the International Standard Industrial Classification (ISIC), Revision 4.
World Bank Development Indicators elaborations on World Bank and OECD data
Value added is the value of the gross output of producers minus the value of intermediate goods and services consumed in production, before accounting for the consumption of fixed capital in production. The United Nations System of National Accounts provides that value added is valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both assessments exclude transport costs which are invoiced separately by the manufacturers.
In the service sector, the many self-employed and sole proprietorships are sometimes difficult to identify and have little incentive to respond to surveys, let alone declare all their earnings. Compounding these problems are the many forms of economic activity that go unregistered, including the work that women and children do for little or no pay.
None
45
General government final consumption expenditure (annual % growth)
Economy
Macroeconomics and Public Finance
Annual percentage growth of general government final consumption expenditure in constant local currency. Aggregates are based on 2015 constant prices, expressed in US dollars. General government final consumption expenditure includes all current general government expenditure on the purchase of goods and services (including compensation of employees). It also includes most defense and national security expenditures, but excludes government military expenditures that are part of public capital formation.
World Bank Development Indicators elaborations on World Bank and OECD data
Gross domestic product (GDP) is the sum of the value added of all producers plus any taxes on the products and minus any subsidies not included in the value of the products. Value added is the value of the gross output of producers minus the value of intermediate goods and services consumed in production, before accounting for the consumption of fixed capital in production. The United Nations System of National Accounts provides that value added is valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both assessments exclude transport costs which are invoiced separately by the manufacturers. Total GDP is measured at purchase prices. The added value of industry is normally measured at basic prices.
Because policymakers tend to focus on promoting output growth, and because production data is easier to collect than spending data, many countries generate their primary estimate of GDP using the production approach. In addition, many countries do not estimate all components of national spending, but derive some of the main aggregates indirectly, using GDP (based on the production approach) as the control total. Measures of consumption growth and capital formation are subject to two types of imprecision. The first derives from the difficulty of measuring expenses at current prices. The second comes from deflation of current price data to measure volume growth, where results depend on the relevance and reliability of the price indices and weights used. Measuring price changes is more difficult for capital goods than for consumer goods, due to the one-off nature of many investments and because the rate of technological advancement of investment goods makes it difficult to capture changes in quality. An example is computers, whose prices have fallen in the face of an improvement in quality. To obtain government consumption at constant prices, countries can deflate current values by applying a wage index (prices) or extrapolate from the change in government employment. Neither technique captures productivity improvements or changes in the quality of public services.
None
46
Household and NPISHs Final consumption expenditure (annual % growth)
Economy
Macroeconomics and Public Finance
Annual percentage growth in final consumption expenditure by households and non-profit institutions in constant local currency. Aggregates are based on 2015 constant prices, expressed in US dollars. Final consumption expenditure of households and non-profit institutions (formerly referred to as private consumption) is the market value of all goods and services, including durable products (such as cars, washing machines and household computers), purchased by households. It does not include home purchases, but it does include imputed rents for owner-occupied dwellings. It also includes payments and fees to public administrations to obtain permits and licenses. This indicator includes the expenditures of nonprofit institutions serving households, even if reported separately by country.
World Bank Development Indicators elaborations on World Bank and OECD data
Gross domestic product (GDP) is the sum of the value added of all producers plus any taxes on the products and minus any subsidies not included in the value of the products. Value added is the value of the gross output of producers minus the value of intermediate goods and services consumed in production, before accounting for the consumption of fixed capital in production. The United Nations System of National Accounts provides that value added is valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both assessments exclude transport costs which are invoiced separately by the manufacturers. Total GDP is measured at purchase prices. The added value of industry is normally measured at basic prices.
Because policymakers tend to focus on promoting output growth, and because production data is easier to collect than spending data, many countries generate their primary estimate of GDP using the production approach. In addition, many countries do not estimate all components of national spending, but derive some of the main aggregates indirectly, using GDP (based on the production approach) as the control total. Measures of consumption growth and capital formation are subject to two types of imprecision. The first derives from the difficulty of measuring expenses at current prices. The second comes from deflation of current price data to measure volume growth, where results depend on the relevance and reliability of the price indices and weights used. Measuring price changes is more difficult for capital goods than for consumer goods, due to the one-off nature of many investments and because the rate of technological advancement of investment goods makes it difficult to capture changes in quality. An example is computers, whose prices have fallen in the face of an improvement in quality.
None
47
Social contributions (% of revenue)
Economy
Macroeconomics and Public Finance
Social contributions include social security contributions paid by employees, employers and the self-employed, as well as other contributions the source of which cannot be determined. They also include actual or imputed contributions to government-run social insurance schemes.
a) International Monetary Fund (IMF); b) World Bank Development Indicators for Lebanon
Data on public revenues and expenditures are collected by the IMF through questionnaires to member countries and by the Organization for Economic Co-operation and Development (OECD). The IMF's 2014 Handbook of Public Finance Statistics, harmonised with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, income and expenditure, not just those represented by cash transactions. The method takes into account all changes in inventories, so inventory data at the end of an accounting period is equivalent to inventory data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks. Public finance statistics are reported in local currency. Many countries report public finance data by fiscal year.
For most countries, data on central government finances have been consolidated into a single account, while for others, only central government balance sheet accounts are available. Because budget accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In the federal states, the accounts of the central government provide an incomplete view of total public finances.
None
48
Tax revenue (% of GDP)
Economy
Macroeconomics and Public Finance
Tax revenues refer to mandatory transfers to the central government for public purposes. Some mandatory transfers such as fines, penalties and most social security contributions are excluded. Refunds and corrections of tax revenue that are incorrectly collected are treated as negative revenue.
a) International Monetary Fund; b) World Bank Development Indicators for Lebanon, Jordan, Palestine, Tunisia, Morocco
Data on public revenues and expenditures are collected by the IMF through questionnaires to member countries and by the Organization for Economic Co-operation and Development (OECD). The IMF's 2014 Handbook of Public Finance Statistics, harmonised with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, income and expenditure, not just those represented by cash transactions. The method takes into account all changes in inventories, so inventory data at the end of an accounting period is equivalent to inventory data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks. Public finance statistics are reported in local currency. Many countries report public finance data by fiscal year.
For most countries, data on central government finances have been consolidated into a single account, while for others, only central government balance sheet accounts are available. Because budget accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In the federal states, the accounts of the central government provide an incomplete view of total public finances.
SDG Goal 17, indicator 17.1.1
49
Taxes on goods and services (% of revenue)
Economy
Macroeconomics and Public Finance
Goods and services taxes include general sales and turnover or value-added taxes, selective excise duties on goods, selective taxes on services, taxes on the use of goods or property, taxes on the extraction and production of minerals and profits from taxing monopolies.
a) International Monetary Fund; b) World Bank Development Indicators for Lebanon, Jordan, Palestine
Data on public revenues and expenditures are collected by the IMF through questionnaires to member countries and by the Organization for Economic Co-operation and Development (OECD). The IMF's 2014 Handbook of Public Finance Statistics, harmonised with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, income and expenditure, not just those represented by cash transactions. The method takes into account all changes in inventories, so inventory data at the end of an accounting period is equivalent to inventory data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks. Public finance statistics are reported in local currency. Many countries report public finance data by fiscal year.
For most countries, data on central government finances have been consolidated into a single account, while for others, only central government balance sheet accounts are available. Because budget accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In the federal states, the accounts of the central government provide an incomplete view of total public finances.
None
50
Expense (% of GDP)
Economy
Macroeconomics and Public Finance
Cash expenses for the operational activities of the public administration in the supply of goods and services. It includes employee compensation (such as wages and salaries), interest and subsidies, grants, social benefits, and other expenses such as rents and dividends.
a) International Monetary Fund; b) World Bank Development Indicators for Lebanon, Jordan, Palestine, Morocco
Data on public revenues and expenditures are collected by the IMF through questionnaires to member countries and by the Organization for Economic Co-operation and Development (OECD). The IMF's 2014 Handbook of Public Finance Statistics, harmonised with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, income and expenditure, not just those represented by cash transactions. The method takes into account all changes in inventories, so inventory data at the end of an accounting period is equivalent to inventory data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks. Public finance statistics are reported in local currency. Many countries report public finance data by fiscal year.
For most countries, data on central government finances have been consolidated into a single account, while for others, only central government balance sheet accounts are available. Because budget accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In the federal states, the accounts of the central government provide an incomplete view of total public finances.
None
51
Agricultural raw materials exports (% of merchandise exports)
Economy
International Relations
Agricultural commodities comprise Section 2 of the (SITC) (raw materials, excluding fuels), excluding divisions 22, 27 (fertilizers and ores, excluding coal, petroleum and precious stones) and 28 (ores and scrap).
a) UNComtrade; b) World Bank Development Indicators for Spain and Montenegro
Statistics on trade in goods consist of data reported to United Nations Comtrade and estimated data for missing declarants. When not reported, statistics on the total imports and exports of goods of countries (or areas) are mainly derived from the International Financial Statistics (IFS) published monthly by the International Monetary Fund (IMF). They are complemented by statistics from other sources, such as national publications and websites, and by the Questionnaire of the United Nations Monthly Statistical Bulletin.
Missing data from product statistics are estimated by extrapolating statistics for the two adjacent years or, if this is not possible, by using statistics reported by trading partners, i.e. mirror statistics. These are also used in cases where the reported data needs to be corrected due to partner distribution or confidential data. All estimates are reviewed and adjusted as necessary. The classification of product groups is based on the Standard International Trade Classification (SITC), Revision 3.
None
52
Exports of goods and services (% of GDP)
Economy
International Relations
Exports of goods and services represent the value of all goods and other market services supplied to the rest of the world. They include the value of goods, freight, insurance, transportation, travel, royalties, license fees, and other services, such as communication, construction, financial, information, commercial, personal, and government services. Income from employment and investment (formerly called factor services) and monetary transfers are excluded.
World Bank Development Indicators elaborations on World Bank and OECD data
Gross domestic product (GDP) on the expenditure side is composed of household final consumption expenditure, general government final consumption expenditure, gross capital formation (private and public investment in fixed assets, changes in inventories and net acquisitions of valuables) and net exports (exports minus imports) of goods and services. These charges are recorded at purchase prices and include net taxes on products.
Export and import data are compiled from customs reports and balance of payments data. While payment-side data provides reasonably reliable records of cross-border transactions, they may not strictly adhere to the appropriate valuation and timing definitions used in the balance of payments or match the change of ownership criterion. This issue has become more relevant with the increasing globalization of international trade. Neither customs nor balance of payments data usually captures the illegal transactions that take place in many countries. Goods transported by travelers across borders in legal but undeclared ship trade can further distort trade statistics.
ENP-South Eurostat Data Browser: International Trade in Goods Area
53
Exports of goods and services (annual % growth)
Economy
International Relations
Annual growth rate of exports of goods and services in constant local currency. Aggregates are based on 2015 constant prices, expressed in US dollars. Exports of goods and services represent the value of all goods and other market services supplied to the rest of the world. They include the value of goods, freight, insurance, transportation, travel, royalties, license fees, and other services, such as communication, construction, financial, informational, business, personal, and government services. Compensation from employment and investment (formerly called factor services) and cash transfers are excluded.
World Bank Development Indicators elaborations on World Bank and OECD data
Gross domestic product (GDP) on the expenditure side is composed of household final consumption expenditure, general government final consumption expenditure, gross capital formation (private and public investment in fixed assets, changes in inventories and net acquisitions of valuables) and net exports (exports minus imports) of goods and services. These charges are recorded at purchase prices and include net taxes on products.
Export and import data are compiled from customs reports and balance of payments data. While payment-side data provides reasonably reliable records of cross-border transactions, they may not strictly adhere to the appropriate valuation and timing definitions used in the balance of payments or match the change of ownership criterion. This issue has become more relevant with the increasing globalization of international trade. Neither customs nor balance of payments data usually captures the illegal transactions that take place in many countries. Goods transported by travelers across borders in legal but undeclared ship trade can further distort trade statistics.
None
54
Exports of goods and services (current US$)
Economy
International Relations
Exports of goods and services represent the value of all goods and other market services supplied to the rest of the world. They include the value of goods, freight, insurance, transportation, travel, royalties, license fees, and other services, such as communication, construction, financial, informational, commercial, personal, and government services. Compensation of employees, income from capital (formerly called factor services) and cash transfers are excluded. Data is expressed in current U.S. dollars.
World Bank Development Indicators elaborations on World Bank and OECD data
Gross domestic product (GDP) on the expenditure side is composed of household final consumption expenditure, general government final consumption expenditure, gross capital formation (private and public investment in fixed assets, changes in inventories and net acquisitions of valuables) and net exports (exports minus imports) of goods and services. These charges are recorded at purchase prices and include net taxes on products.
Export and import data are compiled from customs reports and balance of payments data. While payment-side data provides reasonably reliable records of cross-border transactions, they may not strictly adhere to the appropriate valuation and timing definitions used in the balance of payments or match the change of ownership criterion. This issue has become more relevant with the increasing globalization of international trade. Neither customs nor balance of payments data usually captures the illegal transactions that take place in many countries. Goods transported by travelers across borders in legal but undeclared ship trade can further distort trade statistics.
ENP-South Eurostat Data Browser: International Trade in Goods Area
55
Food exports (% of merchandise exports)
Economy
International Relations
Foods include products from SITC Sections 0 (food and live animals), 1 (beverages and tobacco) and 4 (animal and vegetable oils and fats) and SITC Division 22 (seeds, nuts and oily almonds).
a) UNComtrade; b) World Bank Development Indicators for Spain and Montenegro
Statistics on trade in goods consist of data reported to United Nations Comtrade and estimated data for missing declarants. When not reported, statistics on the total imports and exports of goods of countries (or areas) are mainly derived from the International Financial Statistics (IFS) published monthly by the International Monetary Fund (IMF). They are complemented by statistics from other sources, such as national publications and websites, and by the Questionnaire of the United Nations Monthly Statistical Bulletin.
Missing data from product statistics are estimated by extrapolating statistics for the two adjacent years or, if this is not possible, by using statistics reported by trading partners, i.e. mirror statistics. These are also used in cases where the reported data needs to be corrected due to partner distribution or confidential data. All estimates are reviewed and adjusted as necessary. The classification of product groups is based on the Standard International Trade Classification (SITC), Revision 3.
None
56
Fuel exports (% of merchandise exports)
Economy
International Relations
Fuels include products in section 3 of the SITC (mineral fuels, lubricants and related materials).
a) UNComtrade; b) World Bank Development Indicators for Montenegro
Statistics on trade in goods consist of data reported to United Nations Comtrade and estimated data for missing declarants. When not reported, statistics on the total imports and exports of goods of countries (or areas) are mainly derived from the International Financial Statistics (IFS) published monthly by the International Monetary Fund (IMF). They are complemented by statistics from other sources, such as national publications and websites, and by the Questionnaire of the United Nations Monthly Statistical Bulletin.
Missing data from product statistics are estimated by extrapolating statistics for the two adjacent years or, if this is not possible, by using statistics reported by trading partners, i.e. mirror statistics. These are also used in cases where the reported data needs to be corrected due to partner distribution or confidential data. All estimates are reviewed and adjusted as necessary. The classification of product groups is based on the Standard International Trade Classification (SITC), Revision 3.
None
57
Merchandise exports (current US$)
Economy
International Relations
Exports of goods expressed in FOB values. of goods supplied to the rest of the world.
a) World Trade Organization (WTO); b) World Bank Development Indicators for Serbia
Data on trade in goods comes from customs reports of goods entering or leaving an economy or from financial transaction reports related to trade in goods recorded in the balance of payments.
Exports are recorded as the cost of goods delivered at the border of the exporting country for shipment: free-on-board (FOB) values. Due to differences in timing and definitions, estimates of trade flows derived from customs reports and balance of payments may differ. Several international agencies process trade data, each correcting undeclared or incorrectly declared data, which leads to other differences. Countries can declare trade according to the general or special system of trade. According to the general system, exports include goods moving outwards that are: (a) goods produced in whole or in part in the country; (b) foreign goods, not processed or declared for domestic consumption in the country, which leave bonded warehouses; (c) goods previously included as imports for domestic consumption but subsequently exported without processing. In the special system, exports include categories a and c. In some compilations, categories b and c are classified as re-exports. Due to differences in reporting practices, export data may not be fully comparable across economies. Goods export data comes from the same sources as import data. In principle, world exports and imports should be identical. Similarly, an economy's exports should be equal to the sum of the rest of the world's imports from that economy. However, differences in timing and definitions result in discrepancies in the values reported at all levels.
None
58
Agricultural raw materials imports (% of merchandise imports)
Economy
International Relations
Agricultural commodities comprise Section 2 of the SITC (raw materials, excluding fuels), excluding divisions 22, 27 (fertilisers and raw ores, excluding coal, oil and precious stones) and 28 (ores and scrap).
a) UNComtrade; b) World Bank Development Indicators for Spain and Montenegro
Statistics on trade in goods consist of data reported to United Nations Comtrade and estimated data for missing declarants. When not reported, statistics on the total imports and exports of goods of countries (or areas) are mainly derived from the International Financial Statistics (IFS) published monthly by the International Monetary Fund (IMF). They are complemented by statistics from other sources, such as national publications and websites, and by the Questionnaire of the United Nations Monthly Statistical Bulletin.
Missing data from product statistics are estimated by extrapolating statistics for the two adjacent years or, if this is not possible, by using statistics reported by trading partners, i.e. mirror statistics. These are also used in cases where the reported data needs to be corrected due to partner distribution or confidential data. All estimates are reviewed and adjusted as necessary. The classification of product groups is based on the Standard International Trade Classification (SITC), Revision 3.
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59
Food imports (% of merchandise imports)
Economy
International Relations
Foods include products from SITC Sections 0 (food and live animals), 1 (beverages and tobacco) and 4 (animal and vegetable oils and fats) and SITC Division 22 (oilseeds, nuts and kernels).
a) UNComtrade; b) World Bank Development Indicators for Spain and Montenegro
Statistics on trade in goods consist of data reported to United Nations Comtrade and estimated data for missing declarants. When not reported, statistics on the total imports and exports of goods of countries (or areas) are mainly derived from the International Financial Statistics (IFS) published monthly by the International Monetary Fund (IMF). They are complemented by statistics from other sources, such as national publications and websites, and by the Questionnaire of the United Nations Monthly Statistical Bulletin.
Missing data from product statistics are estimated by extrapolating statistics for the two adjacent years or, if this is not possible, by using statistics reported by trading partners, i.e. mirror statistics. These are also used in cases where the reported data needs to be corrected due to partner distribution or confidential data. All estimates are reviewed and adjusted as necessary. The classification of product groups is based on the Standard International Trade Classification (SITC), Revision 3.
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60
Fuel imports (% of merchandise imports)
Economy
International Relations
Fuels include products in SITC section 3 (mineral fuels, lubricants and related materials).
a) UNComtrade; b) World Bank Development Indicators for Spain and Montenegro
Statistics on trade in goods consist of data reported to United Nations Comtrade and estimated data for missing declarants. When not reported, statistics on the total imports and exports of goods of countries (or areas) are mainly derived from the International Financial Statistics (IFS) published monthly by the International Monetary Fund (IMF). They are complemented by statistics from other sources, such as national publications and websites, and by the Questionnaire of the United Nations Monthly Statistical Bulletin.
Missing data from product statistics are estimated by extrapolating statistics for the two adjacent years or, if this is not possible, by using statistics reported by trading partners, i.e. mirror statistics. These are also used in cases where the reported data needs to be corrected due to partner distribution or confidential data. All estimates are reviewed and adjusted as necessary. The classification of product groups is based on the Standard International Trade Classification (SITC), Revision 3.
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61
Imports of goods and services (% of GDP)
Economy
International Relations
Imports of goods and services represent the value of all goods and other market services received from the rest of the world. They include the value of goods, freight, insurance, transportation, travel, royalties, license fees, and other services, such as communication, construction, financial, informational, commercial, personal, and government services. Compensation from employment and investment (formerly called factor services) and cash transfers are excluded.
World Bank Development Indicators elaborations on World Bank and OECD data
Gross domestic product (GDP) on the expenditure side is composed of household final consumption expenditure, general government final consumption expenditure, gross capital formation (private and public investment in fixed assets, changes in inventories and net acquisitions of valuables) and net exports (exports minus imports) of goods and services. These charges are recorded at purchase prices and include net taxes on products.
Export and import data are compiled from customs reports and balance of payments data. While payment-side data provides reasonably reliable records of cross-border transactions, they may not strictly adhere to the appropriate valuation and timing definitions used in the balance of payments or match the change of ownership criterion. This issue has become more relevant with the increasing globalization of international trade. Neither customs nor balance of payments data usually captures the illegal transactions that take place in many countries. Goods transported by travelers across borders in legal but undeclared ship trade can further distort trade statistics.
None
62
Imports of goods and services (annual % growth)
Economy
International Relations
Annual growth rate of imports of goods and services in constant local currency. Aggregates are based on 2015 constant prices, expressed in US dollars.
World Bank Development Indicators elaborations on World Bank and OECD data
Gross domestic product (GDP) on the expenditure side is composed of household final consumption expenditure, general government final consumption expenditure, gross capital formation (private and public investment in fixed assets, changes in inventories and net acquisitions of valuables) and net exports (exports minus imports) of goods and services. These charges are recorded at purchase prices and include net taxes on products.
Export and import data are compiled from customs reports and balance of payments data. While payment-side data provides reasonably reliable records of cross-border transactions, they may not strictly adhere to the appropriate valuation and timing definitions used in the balance of payments or match the change of ownership criterion. This issue has become more relevant with the increasing globalization of international trade. Neither customs nor balance of payments data usually captures the illegal transactions that take place in many countries. Goods transported by travelers across borders in legal but undeclared ship trade can further distort trade statistics.
None
63
Imports of goods and services (current US$)
Economy
International Relations
Imports of goods and services represent the value of all goods and other market services received from the rest of the world. They include the value of goods, freight, insurance, transportation, travel, royalties, license fees, and other services, such as communication, construction, financial, informational, commercial, personal, and government services. Compensation from employment and investment (formerly called factor services) and cash transfers are excluded.
World Bank Development Indicators elaborations on World Bank and OECD data
Gross domestic product (GDP) on the expenditure side is composed of household final consumption expenditure, general government final consumption expenditure, gross capital formation (private and public investment in fixed assets, changes in inventories and net acquisitions of valuables) and net exports (exports minus imports) of goods and services. These charges are recorded at purchase prices and include net taxes on products.
Export and import data are compiled from customs reports and balance of payments data. While payment-side data provides reasonably reliable records of cross-border transactions, they may not strictly adhere to the appropriate valuation and timing definitions used in the balance of payments or match the change of ownership criterion. This issue has become more relevant with the increasing globalization of international trade. Neither customs nor balance of payments data usually captures the illegal transactions that take place in many countries. Goods transported by travelers across borders in legal but undeclared ship trade can further distort trade statistics.
None
64
Merchandise imports (current US$)
Economy
International Relations
Imports of goods expressed in CIF values of goods received from the rest of the world.
a) World Trade Organization (WTO); b) World Bank Development Indicators for Serbia
Data on trade in goods comes from customs reports of goods entering or leaving an economy or from financial transaction reports related to trade in goods recorded in the balance of payments.
The value of imports is generally recorded as the cost of goods at the time of purchase by the importer plus the cost of transportation and insurance to the border of the importing country – the value of cost, insurance and freight (CIF)), corresponding to the cost upon landing at the point of entry of foreign goods into the country. Some countries collect import data on a Free On Board (FOB) basis and adjust it for transportation and insurance costs. Due to differences in timing and definitions, estimates of trade flows derived from customs reports and balance of payments may differ. Several international agencies process trade data, each correcting undeclared or incorrectly declared data, which leads to other differences. Countries can bring trade back according to the general or special system of trade. According to the general system, imports include goods imported for domestic consumption and imports into bonded warehouses and free trade zones. In the special system, imports include goods imported for domestic consumption (including conversions and repairs) and levies for domestic consumption from customs warehouses and free trade areas. Goods transported through one country en route to another are excluded. Goods import data comes from the same sources as export data. In principle, world exports and imports should be identical. Similarly, an economy's exports should be equal to the sum of the rest of the world's imports from that economy. However, differences in timing and definitions result in discrepancies in the values reported at all levels.
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65
Foreign direct investment, net inflows (% of GDP)
Economy
International Relations
Net investment inflows, calculated as new investments minus divestments in the reporting economy by foreign investors, in order to acquire a durable operating interest (10% or more of the voting shares) in a company. This is the sum of equity capital, reinvestment of profits, other long-term capital, and short-term capital, as shown in the balance of payments.
a) International Monetary Fund (IMF); b) World Bank Development Indicators for Kosovo, Bosnia and Herzegovina, Montenegro, Syria, Lebanon, Jordan, Palestine, Egypt, Libya, Tunisia, Algeria
The data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Equity flows include foreign direct investment (FDI) and portfolio equities. The FDI data is complemented by World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.
The internationally accepted definition of foreign direct investment (taken from the sixth edition of the IMF's Balance of Payments Handbook [2009]) includes the following components: equity investments, including investments associated with actions that give rise to control or influence; investments in indirectly influenced or controlled companies; investments in associated companies; indebtedness (except for selected indebtedness); inverse investments. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership gives rise to a direct investment relationship, based on control and influence. Unlike other types of international investments, FDI is carried out to establish a lasting interest or effective management control over a company in another country. A lasting interest in an investment firm typically involves setting up permanent or long-term warehouses, production facilities, and other organizations abroad. Direct investments can take the form of greenfield investments, in which the investor starts a new business in a foreign country by building new operating structures; joint venture, in which the investor enters into a partnership agreement with a company abroad to set up a new business; or merger and acquisition, in which the investor acquires an existing company abroad. The IMF suggests that investments should account for at least 10% of voting shares to be considered FDI. In practice, many countries set a higher threshold. Many countries do not report reinvested profits, and the definition of long-term loans varies from country to country. FDI data does not provide a complete picture of international investment in an economy. FDI balance of payments data does not include locally raised capital, an important source of investment financing in some developing countries. In addition, FDI data omits non-equity cross-border transactions, such as intra-unit flows of goods and services. The volume of global private financial flows reported by the World Bank generally differs from that reported by other sources due to differences in sources, the classification of economies and the method used to adjust and disaggregate the information reported. In addition, especially with regard to debt financing, differences may also reflect the treatment of some transaction installments and some offshore issuances. Data from 2005 onwards are based on the sixth edition of the IMF's Balance of Payments Handbook (BPM6).
ENP-South Eurostat Data Browser: Tourism Area
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Foreign direct investment, net outflows (% of GDP)
Economy
International Relations
Net foreign investment flows calculated as new investments minus divestments from the reporting economy to the rest of the world, in order to acquire a durable management interest (10% or more of the voting shares) in a company. This is the sum of equity capital, reinvestment of profits, other long-term capital, and short-term capital, as shown in the balance of payments.
a) International Monetary Fund (IMF); b) World Bank Development Indicators for Kosovo, Bosnia and Herzegovina, Montenegro, Lebanon, Jordan, Palestine, Egypt, Libya, Tunisia, Algeria
The data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Equity flows include foreign direct investment (FDI) and portfolio equities. The FDI data is complemented by World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.
The internationally accepted definition of foreign direct investment (taken from the sixth edition of the IMF's Balance of Payments Handbook [2009]) includes the following components: equity investments, including investments associated with actions that give rise to control or influence; investments in indirectly influenced or controlled companies; investments in associated companies; indebtedness (except for selected indebtedness); inverse investments. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership gives rise to a direct investment relationship, based on control and influence. Unlike other types of international investments, FDI is carried out to establish a lasting interest or effective management control over a company in another country. A lasting interest in an investment firm typically involves setting up permanent or long-term warehouses, production facilities, and other organizations abroad. Direct investments can take the form of greenfield investments, in which the investor starts a new business in a foreign country by building new operating structures; joint venture, in which the investor enters into a partnership agreement with a company abroad to set up a new business; or merger and acquisition, in which the investor acquires an existing company abroad. The IMF suggests that investments should account for at least 10% of voting shares to be considered FDI. In practice, many countries set a higher threshold. Many countries do not report reinvested profits, and the definition of long-term loans varies from country to country. FDI data does not provide a complete picture of international investment in an economy. FDI balance of payments data does not include locally raised capital, an important source of investment financing in some developing countries. In addition, FDI data omits non-equity cross-border transactions, such as intra-unit flows of goods and services. The volume of global private financial flows reported by the World Bank generally differs from that reported by other sources due to differences in sources, the classification of economies and the method used to adjust and disaggregate the information reported. In addition, especially with regard to debt financing, differences may also reflect the treatment of some transaction installments and some offshore issuances. Data from 2005 onwards are based on the sixth edition of the IMF's Balance of Payments Handbook (BPM6).
ENP-South Eurostat Data Browser: "Economics and Finance" Area
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International tourism, number of arrivals
Economy
Other Economic Issues
Overnight visitors who travel to a country other than the one in which they have their habitual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose of visit is other than a paid activity within the country visited. When data on the number of tourists is not available, the number of visitors is reported, which includes tourists, day visitors, cruise passengers and crew members.
a) UN Tourism; b) World Bank Development Indicators for Portugal, Slovenia, Serbia, Bosnia and Herzegovina, Montenegro, North Macedonia, Lebanon, Palestine, Tunisia
Data is obtained from different sources: administrative registers (immigration, traffic statistics and other possible types of controls), border surveys or a mix of these. If the data is obtained from surveys of accommodation facilities, the number of guests is used as an estimate of the arrival figures; Consequently, in this case, the breakdowns by region, main purpose of the trip, modes of transport used or forms of travel arrangements are based on complementary visitor surveys.
For some countries, the number of arrivals is limited to arrivals by air and for others to hotel arrivals. Some countries include arrivals of citizens residing abroad, while others do not. Caution should therefore be exercised when comparing arrivals between countries. Inbound tourist data refers to the number of arrivals, not the number of people traveling. Therefore, a person who travels to a country several times in a given period is counted as a new arrival each time.
ENP-South Eurostat Data Browser: "Economics and Finance" Area
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International tourism, number of departures
Economy
Other Economic Issues
Outbound international tourists measured as the number of departures that people make from their country of habitual residence to any other country for any purpose other than a paid activity in the country visited. The data on outbound tourists do not refer to the number of people traveling; therefore, a person who makes multiple trips from a country in a given period is counted as a new departure each time.
a) UN Tourism for Spain, France, Italy, Slovenia, Croatia, Malta, Cyprus, Albania; b) World Bank Development Indicators elaborations on World Tourism Organisation data for other countries
Data is collected using one of three methods, or a combination of these, to determine outbound visitor flows: using an entry/departure card, a specific survey at the border, or by observing them from household surveys because they belong to resident households. In the latter case, information on outbound travel is usually collected at the same time as information on domestic travel.
The data refer to international tourism, when the traveler's country of residence is different from the country of visit. The data on incoming and outgoing tourists refers to the number of arrivals and departures, not the number of people traveling.
ENP-South Eurostat Data Browser: Tourism Area
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High-technology exports (% of manufactured exports)
Economy
Other Economic Issues
Exports of R&D-intensive products, such as aerospace products, computers, pharmaceuticals, scientific instruments and electrical machinery, as a percentage of manufacturing exports.
a) UNComtrade; b) World Bank Development Indicators for Spain and Montenegro
The methodology used to determine high-tech exports was developed by the OECD in collaboration with Eurostat. It adopts a "product approach" (rather than a "sectoral approach") based on R&D intensity (expenditure divided by total sales) by product groups found in Germany, Italy, Japan, the Netherlands, Sweden and the United States. The original classification of high-tech products is based on SITC Rev. 3 and is taken from Table 4 of Annex 2 of the 1997 working paper by Thomas Hatzichronouglou, OECD. The classification is based on the importance of R&D expenditures in relation to the gross output and value added of different types of industries producing goods for export. It contains four categories: high, medium-high, medium-low and low technology. Examples of high-tech industries are aircraft, computers, and pharmaceuticals; medium-high technology includes motor vehicles, electrical equipment and most chemicals; Low-to-middle technology includes rubber, plastics, base metals and shipbuilding; Low-tech industries include food processing, textiles, clothing, and footwear.
Because industries that specialize in some high-tech products can also produce low-tech products, the per-product approach is more appropriate for international trade. The method only takes into account the intensity of R&D, but other high-tech features are also important, such as know-how, scientific personnel and the technology contained in patents. Considering these characteristics, a different list would be obtained (cf. Hatzichronoglou 1997).
None
70
ICT goods exports (% of total goods exports)
Economy
Other Economic Issues
Exports of information and communication technology (ICT) goods – including computers and peripheral equipment, communications equipment, consumer electronic equipment, electronic components and other information technology goods – as a percentage of exports of goods.
United Nations on Trade and Development (UNCTAD)
The data are reported by the national authorities with details of the products coded according to the World Customs Organisation (WCO) Harmonised Commodity Description and Coding System 1992, HS 1996, HS 2002, HS 2007, HS 2012, HS 2017 or HS 2022, depending on the economy and the reference year. In case multiple classifications are available for a given economy and a given year, preference is given to the most recent HS edition. The data are downloaded from UN Comtrade and aggregated into ICT product groups by UNCTAD.
None
None
71
ICT goods imports (% total goods imports)
Economy
Other Economic Issues
Imports of information and communication technology (ICT) goods – including computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components and other information technology goods – as a percentage of imports of goods.
United Nations on Trade and Development (UNCTAD)
The data are reported by the national authorities with details of the products coded according to the World Customs Organisation (WCO) Harmonised Commodity Description and Coding System 1992, HS 1996, HS 2002, HS 2007, HS 2012, HS 2017 or HS 2022, depending on the economy and the reference year. In case multiple classifications are available for a given economy and a given year, preference is given to the most recent edition. The data are downloaded from UN Comtrade and aggregated into ICT product groups by UNCTAD.
None
None
72
Patent applications, residents
Economy
Other Economic Issues
Worldwide patent applications filed through the Patent Cooperation Treaty procedure or at a national patent office to obtain exclusive rights to an invention: a product or process that provides a new way of doing something or offers a new technical solution to a problem. The patent guarantees the protection of the invention to its owner for a limited period, generally 20 years.
WeMed elaborations on World Intellectual Property Organization (WIPO) data
Intellectual property (IP) data is mainly based on WIPO's annual intellectual property statistical surveys and data compiled by WIPO in the processing of international applications/registrations through the Patent Cooperation Treaty (PCT) and the Madrid and Hague Systems. The intellectual property survey, which has been carried out regularly and for a long time, covers patents, utility models, trademarks, industrial designs and plant varieties. It consists of 27 questionnaires, all available in Arabic, Chinese, English, French, Russian and Spanish on the www.wipo.int/ipstats/en/data_collection/questionnaire website. In 2017, WIPO began collecting data on geographical indications through an annual survey. This simple questionnaire aims to collect data on geographical information divided by legal means of protection (e.g., sui generis systems, trademarks, international agreements, etc.) and product types (e.g., wines and spirits, agricultural products, and so on).
Ongoing efforts are underway to improve the quality and availability of intellectual property statistics and to collect data from as many offices and countries as possible.
None
73
Commercial bank branches (per 100,000 adults)
Economy
Other Economic Issues
Ratio (per 100 persons aged 15 years and over) of retail locations of resident commercial banks and other resident banks that operate as commercial banks and provide financial services to customers. They are physically separate from the main office, but not organized as legally separate branches.
International Monetary Fund (IMF)
The data is collected through the Financial Access Survey (FAS) based on administrative data collected by central banks and other financial regulators and reported to the IMF. The FAS, launched in 2009, is a dataset on access to and use of financial services, which helps policymakers measure and monitor financial inclusion and assess progress against other countries.
The banking sector includes monetary authorities (the central bank) and deposit banks, as well as other financial companies for which data are available (including institutions that do not accept transferable deposits but have liabilities such as term deposits and savings deposits). Examples of other financial companies are finance and leasing companies, money lenders, insurance companies, pension funds, and foreign exchange companies.
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74
Domestic credit to private sector (% of GDP)
Economy
Other Economic Issues
Percentage of GDP of financial resources provided to the private sector by financial corporations, for example through loans, purchases of non-equity securities, trade receivables and other receivables, which give rise to a request for repayment. For some countries, these receivables also include those from public companies.
World Bank Development Indicators (WB)
The data is collected through the Financial Access Survey (FAS) based on administrative data collected by central banks and other financial regulators and reported to the IMF. The FAS, launched in 2009, is a dataset on access to and use of financial services, which helps policymakers measure and monitor financial inclusion and assess progress against other countries.
Financial companies include monetary authorities and depository banks, as well as other financial companies where data is available (including companies that do not accept transferable deposits, but which have liabilities such as time and savings deposits). Examples of other financial companies are finance and leasing companies, money lenders, insurance companies, pension funds, and foreign exchange companies.
None
75
Domestic credit to private sector by banks (% of GDP)
Economy
Other Economic Issues
Percentage of GDP of financial resources provided to the private sector by other depository companies (except central banks), for example through loans, purchases of non-equity securities, trade receivables and other accounts receivable, which give rise to a credit for repayment. For some countries, these credits include those from public companies.
World Bank Development Indicators (WB)
The data is collected through the Financial Access Survey (FAS) based on administrative data collected by central banks and other financial regulators and reported to the IMF. The FAS, launched in 2009, is a dataset on access to and use of financial services, which helps policymakers measure and monitor financial inclusion and assess progress against other countries.
The banking sector includes monetary authorities (the central bank) and deposit banks, as well as other financial companies for which data are available (including institutions that do not accept transferable deposits but have liabilities such as term deposits and savings deposits). Examples of other financial companies are finance and leasing companies, money lenders, insurance companies, pension funds, and foreign exchange companies.
None
76
Air transport, freight (million ton-km)
None
None
Volume of shipments of diplomatic cargo and baggage carried at each stage of flight (operations of an aircraft from takeoff to the next landing), measured in metric tons per kilometer flown.
World Bank Development Indicators elaborations on International Civil Aviation Organization (ICAO) data
Air transport data represent the total scheduled traffic (international and domestic) carried by air carriers registered in a country. Countries submit air transport data to the International Civil Aviation Organization (ICAO) based on standard instructions and definitions issued by ICAO. In many cases, however, the data include ICAO estimates for non-reporting carriers. Where possible, these estimates are based on previous communications complemented by information published by air carriers, such as flight schedules.
None
ENP-South Eurostat Data Browser: Transport Area
77
Air transport, passengers carried
None
None
Domestic and international passengers of air carriers registered in the country.
World Bank Development Indicators elaborations on International Civil Aviation Organization (CAO) data
Air transport data represent the total scheduled traffic (international and domestic) carried by air carriers registered in a country. Countries submit air transport data to the International Civil Aviation Organization (ICAO) based on standard instructions and definitions issued by ICAO. In many cases, however, the data include ICAO estimates for non-reporting carriers. Where possible, these estimates are based on previous communications complemented by information published by air carriers, such as flight schedules.
The data covers air traffic carried on scheduled services, but changes in air transport regulations in Europe have made it more difficult to classify traffic as scheduled or non-scheduled. Therefore, the recent increases recorded in some European countries may be due to changes in the classification of air traffic rather than actual growth. In the case of multinational air carriers owned by partner states, traffic within each partner state is indicated separately as domestic and all other traffic as international. "Foreign" cabotage traffic (i.e. traffic carried between pairs of cities in a State other than that in which the reporting carrier has its main office) is referred to as international traffic. A technical downtime does not entail the classification of a flight phase different from that which would have occurred if the technical downtime had not been carried out. For countries with few or only one air carriers, the addition or discontinuation of a domestic air carrier can cause significant changes in air traffic. Data for the transport sector are not always internationally comparable. Unlike demographic statistics, national economic accounts and international trade data, the collection of infrastructure data has not been "internationalized".
ENP-South Eurostat Data Browser: Transport Area
78
Liner shipping connectivity index (maximum value in 2004 = 100)
None
None
Composite index that measures the position of an economy within global liner shipping networks. It is calculated based on the number of ship stops, the container handling capacity of ports, the number of services and companies, the size of the largest vessel, and the number of countries connected through direct liner transport services. For each year, the value of the fourth quarter is considered.
United Nations on Trade and Development (UNCTAD)
The composite index is calculated based on the number of vessels, the container handling capacity of ports, the number of liner maritime services, the number of companies, the size of the largest vessel and the number of countries connected through direct liner transport services. For each component, the country value is divided by the average value of the component in Q1 2023 and then the average of the six components for the country is calculated. The average of the components for a given country and quarter is then multiplied by 100. The result is an average index of 100 in Q1 2023. This scalat was applied in March 2024 for the entire series. (from Q1 2006). This is a change from the previous calculation methodology, in which the constituents and the index were scaled to the maximum value in Q1 2006.
None
None
79
Logistics performance index: Overall (1=low to 5=high)
None
None
Composite index that measures a country's perception of logistics based on the efficiency of the customs clearance process, the quality of trade and transport-related infrastructure, the ease of arranging shipments at competitive prices, the quality of logistics services, the ability to track and trace shipments, and the frequency with which shipments reach the recipient on time. The index ranges from 1 to 5, with a higher score representing better performance.
World Bank
The indicator presents data from surveys on logistics performance conducted by the World Bank in collaboration with academic and international institutions and with private companies and private entities engaged in international logistics. The Logistic performance index (LPI) uses a structured online survey conducted on logistics professionals at multinational freight forwarders and major express couriers. Each respondent evaluates eight foreign markets based on six key components of logistics performance (the efficiency of customs clearance and border management, the quality of trade and transportation infrastructure, the ease of arranging shipments at competitive prices, the expertise and quality of logistics services, the ability to track and trace shipments, the frequency with which shipments reach recipients within delivery times planned or planned). The components are rated on a scale (from lowest score to highest score) from 1 to 5. The eight countries were chosen based on the most important export and import markets of the country in which the respondent is located, on the basis of a random selection and, for landlocked countries, on the basis of neighboring countries that are part of the connection lines with international markets. The method used to select the group of countries evaluated by each respondent varies depending on the characteristics of the country in which the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in the missing values. Missing values are replaced with the country's average answer for each question, corrected by the average deviation from the country's average in the questions it answered. The LPI is built from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a data set. In the LPI, the inputs for the PCA are the scores of countries to the questions covering the six main components, averaged across all respondents providing data on a given foreign market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before performing the PCA. The result of the PCA is a single indicator – the LPI – which is a weighted average of these scores. The weights are chosen to maximize the percentage of change of the original six LPI indicators.
None
None
80
Renewable energy consumption (% of total final energy consumption)
None
None
Share of renewable energy in total final energy consumption. Renewable energy includes hydropower, solid biofuels, liquid biofuels, biogas, wind, solar, geothermal, tidal/wave/ocean and renewable municipal waste.
International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistics Division (UNSD), World Bank, World Health Organization (WHO), "Tracking SDG7: The Energy Progress Report"
The data comes from the International Energy Agency's (IEA) global energy balances, with additional information on https://www.iea.org/reports/sdg7-data-and-projections; and the United Nations Energy Statistics Database (http://data.un.org/Explorer.aspx?d=EDATA); Both provide a breakdown of national energy flows by product over time. The indicator is calculated as the ratio of the final consumption of energy from renewable sources after the allocation of electricity and heat (AFECREN) to the total final consumption of energy (TFEC), calculated on the basis of the flows of the energy balances. The final consumption of electricity and heat is allocated to renewables based on the share of gross generation from renewable sources. In practice, this is done by calculating the percentage of electricity and heat produced by each renewable source, multiplying the final consumption of electricity and heat by these shares, and then allocating the resulting quantities
None
SDG Goal 7, indicator 7.2.1
81
Surface area (sq. km)
Environment and Natural Resources
Environment, climate, and territory
Total area of a country, including the surfaces of inland water bodies and some coastal waterways.
a) Istat for Italy; b) Eurostat for Portugal, Spain, France, Slovenia, Croatia, Greece, Malta, Cyprus; c) World Bank Development Indicators for Syria; d) FAO for other countries
FAO data are collected through a questionnaire on land use, irrigation and agricultural practices, based on the FAO land use classification. In other cases, these are data from the national statistical agencies.
Changes in a country's total land area from one year to the next may be due to data updating or revision rather than an actual change.
ENP-South Eurostat Data Browser: Environment and Energy Area
82
Forest area (% of land area)
Environment and Natural Resources
Environment, climate, and territory
Percentage of the land area covered by forest area, consisting of land with natural or planted stands of trees of at least 5 meters in situ, both productive and non-productive, and excluding tree stands in agricultural production systems (e.g., fruit plantations and agroforestry systems) and trees in urban parks and gardens.
a) FAO; b) World Bank Development Indicators for Egypt, Libya, Algeria
FAO data are collected through the Land Use, Irrigation and Agricultural Practices Questionnaire, based on the FAO Land Use Classification.
FAO's classification of land use is aligned with the United Nations System of Environmental and Economic Accounting (SEEA), the United Nations Framework for the Development of Environmental Statistics (FDES) and the World Census of Agriculture. It is also consistent with the Intergovernmental Panel on Climate Change Land Use Classes for Countries' Relations to the United Nations Framework Convention on Climate Change (UNFCCC). A mapping of the FAO, SEEA, World Census of Agriculture and IPCC classifications is provided in the FAO questionnaire.
SDG Goal 15, indicator 15.1.1; ENP-South Eurostat Data Browser: Environment and Energy Area
83
Rural population (% of total population)
Environment and Natural Resources
Environment, climate, and territory
Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between the total population and the urban population.
World Bank Development Indicators elaborations on United Nations Population Division (UNPD) data
Rural population is calculated as the difference between the total population and the urban population. The rural population is approximated as the non-urban population in the middle of the year. The United Nations Population Division and other agencies provide current population estimates for developing countries that do not have recent census data and pre- and post-census estimates for countries that do have census data.
The aggregation of urban and rural populations may not correspond to the total population due to the different coverage of countries. There is no consistent, universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations in different countries. Because estimates of cities and metropolitan areas are based on national definitions of what constitutes a city or metropolitan area, comparisons between countries must be made with caution. To estimate urban populations, the UN ratios of urban population to total population were applied to World Bank estimates of total population.
None
84
Population in the largest city (% of urban population)
Environment and Natural Resources
Environment, climate, and territory
Percentage of a country's urban population living in the country's largest metropolitan area.
World Bank Development Indicators elaborations on United Nations Population Division data
Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and the United Nations Urbanization Indices (World Urbanization Prospects). The United Nations Population Division and other agencies provide current population estimates for developing countries that do not have recent census data and pre- and post-census estimates for countries that do have census data.
A metropolitan area includes the urban area, its satellite cities, and intermediate rural areas that are socio-economically connected to the urban core, typically by employment links through commuting, with the urban core as the primary labor market. According to the UN definition, a metropolitan area includes both contiguous territory inhabited at urban levels of residential density, and additional surrounding areas of lower settlement density that are also under the direct influence of the city (e.g., through frequent transportation, road links, commuting facilities, etc.). Because estimates of cities and metropolitan areas are based on national definitions of what constitutes a city or metropolitan area, comparisons between countries must be made with caution. Population estimates are derived from demographic models and are therefore susceptible to bias and errors due to deficiencies in the model and data. Countries differ in how they classify the population as "urban" or "rural." The cohort-component method, used to estimate and project the population, requires data on fertility, mortality and net migration, often collected from sample surveys, which may be small or with limited coverage.
None
85
Urban population (% of total population)
Environment and Natural Resources
Environment, climate, and territory
Urban population refers to people living in urban areas as defined by national statistical offices.
a) United Nations Population Division (UNPD); b) World Bank Development Indicators for Palestine
Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and the United Nations Urbanization Indices (World Urbanization Prospects). The United Nations Population Division and other agencies provide current population estimates for developing countries that do not have recent census data and pre- and post-census estimates for countries that do have census data
Most countries use an urban classification linked to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructures and services. Other countries designate urban areas according to administrative provisions. Due to national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population does not lend itself to a single definition applicable to all countries. Because estimates of cities and metropolitan areas are based on national definitions of what constitutes a city or metropolitan area, comparisons between countries must be made with caution. Population estimates are derived from demographic models and are therefore susceptible to bias and errors due to deficiencies in the model and data. Countries differ in how they classify the population as "urban" or "rural." The cohort-component method, used to estimate and project population, requires data on fertility, mortality and net migration, often collected from sample surveys, which may be small or with limited coverage.
None
86
Marine protected areas (% of territorial waters)
Environment and Natural Resources
Environment, climate, and territory
Percentage of the territorial waters of marine protected areas, which are areas of intertidal or subtidal land – together with overlying waters, associated flora and fauna, and historical and cultural features – reserved by law or by other effective means to protect part or all of the enclosed environment.
wemed elaborations on World Bank Development Indicators and United Nations Environment World Conservation Monitoring Centre (UNEP-WCMC) data
This indicator is calculated using all nationally designated protected areas registered in the World Database on Protected Areas (WDPA) whose location and extent are known. The WDPA database is stored within a geographic information system (GIS) that stores information about protected areas such as name, type and date of designation, documented area, geographic location (point), and/or boundary (polygon). A GIS analysis is used to calculate land and sea protection.
The International Union for Conservation of Nature (IUCN) defines a protected area as "a clearly defined, recognized, dedicated and managed geographical space, through legal or other effective means, to achieve the long-term conservation of nature with the associated ecosystem services and cultural values". Designating an area as protected does not mean that protection is in place. In addition, for small countries that only have protected areas of less than 1,000 hectares in size, the size limit in the definition leads to an underestimation of protected areas. Nationally protected areas are defined using the IUCN's six management categories for areas of at least 1,000 hectares: scientific reserves and rigorous nature reserves with limited access to the public; national parks of national or international importance and not materially affected by human activity; natural monuments and natural landscapes with unique aspects; managed nature reserves and wildlife sanctuaries; protected landscapes (which may include cultural landscapes); areas managed primarily for the sustainable use of natural systems to ensure the long-term protection and maintenance of biological diversity.
SDG Goal 14, indicator 14.5.1
87
Terrestrial protected areas (% of total land area)
Environment and Natural Resources
Environment, climate, and territory
Percentage of total terrestrial areas of terrestrial protected areas, which are fully or partially protected areas of at least 1,000 hectares, designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes and areas managed primarily for sustainable use. Marine areas, unclassified areas, coastal (intertidal) areas and sites protected by local or provincial laws are excluded.
elaborations of World Bank Development Indicators on data from the United Nations Environment World Conservation Monitoring Centre (UNEP-WCMC)
This indicator is calculated using all nationally designated protected areas registered in the World Database on Protected Areas (WDPA) whose location and extent are known. The WDPA database is stored within a geographic information system (GIS) that stores information about protected areas such as name, type and date of designation, documented area, geographic location (point), and/or boundary (polygon). A GIS analysis is used to calculate land and sea protection.
The International Union for Conservation of Nature (IUCN) defines a protected area as "a clearly defined, recognized, dedicated and managed geographical space, through legal or other effective means, to achieve the long-term conservation of nature with the associated ecosystem services and cultural values". Designating an area as protected does not mean that protection is in place. In addition, for small countries that only have protected areas of less than 1,000 hectares in size, the size limit in the definition leads to an underestimation of protected areas. Nationally protected areas are defined using the IUCN's six management categories for areas of at least 1,000 hectares: scientific reserves and rigorous nature reserves with limited access to the public; national parks of national or international importance and not materially affected by human activity; natural monuments and natural landscapes with unique aspects; managed nature reserves and wildlife sanctuaries; protected landscapes (which may include cultural landscapes); areas managed primarily for the sustainable use of natural systems to ensure the long-term protection and maintenance of biological diversity.
None
88
Agricultural land (% of land area)
Environment and Natural Resources
Agriculture
Percentage of the land area devoted to arable land, permanent crops and permanent pastures.
Food and Agriculture Organization (FAO)
Data are collected through the FAO Questionnaire on Land Use, Irrigation and Agricultural Practices, based on the FAO Land Use Classification.
FAO's classification of land use is aligned with the United Nations System of Environmental and Economic Accounting (SEEA), the United Nations Framework for the Development of Environmental Statistics (FDES) and the World Census of Agriculture. It is also consistent with the Intergovernmental Panel on Climate Change Land Use Classes for Countries' Relations to the United Nations Framework Convention on Climate Change (UNFCCC). A mapping of the FAO, SEEA, World Census of Agriculture and IPCC classifications is provided in the FAO questionnaire.
SDG Goal 15, indicator 15.1.1; ENP-South Eurostat Data Browser: Agriculture and Fisheries Area
89
Arable land (% of land area)
Environment and Natural Resources
Agriculture
Percentage of land area of arable land, which includes land defined by FAO as temporary cropland (dual cropping areas are counted only once), temporary meadows for mowing or grazing, land planted with vegetable gardens and temporary fallow land. Land abandoned due to itinerant cultivation is excluded.
Food and Agriculture Organization (FAO)
Data are collected through the FAO Questionnaire on Land Use, Irrigation and Agricultural Practices, based on the FAO Land Use Classification.
FAO's classification of land use is aligned with the United Nations System of Environmental and Economic Accounting (SEEA), the United Nations Framework for the Development of Environmental Statistics (FDES) and the World Census of Agriculture. It is also consistent with the Intergovernmental Panel on Climate Change Land Use Classes for Countries' Relations to the United Nations Framework Convention on Climate Change (UNFCCC). A mapping of the FAO, SEEA, World Census of Agriculture and IPCC classifications is provided in the FAO questionnaire.
None
90
Arable land (hectares per person)
Environment and Natural Resources
Agriculture
Land cultivated with arable land (hectares per 1,000 inhabitants). Arable land includes land defined by FAO as temporary cropland (double-crop areas are counted once), temporary mowing or grazing meadows, land planted with vegetable gardens or gardens, and temporarily fallow land. Land abandoned due to itinerant cultivation is excluded.
Food and Agriculture Organization (FAO)
Data are collected through the FAO Questionnaire on Land Use, Irrigation and Agricultural Practices, based on the FAO Land Use Classification.
FAO's classification of land use is aligned with the United Nations System of Environmental and Economic Accounting (SEEA), the United Nations Framework for the Development of Environmental Statistics (FDES) and the World Census of Agriculture. It is also consistent with the Intergovernmental Panel on Climate Change Land Use Classes for Countries' Relations to the United Nations Framework Convention on Climate Change (UNFCCC). A mapping of the FAO, SEEA, World Census of Agriculture and IPCC classifications is provided in the FAO questionnaire.
SDG Goal 15, indicator 15.1.1; ENP-South Eurostat Data Browser: Agriculture and Fisheries Area
91
Permanent cropland (% of land area)
Environment and Natural Resources
Agriculture
Percentage of the earth's surface of permanent crops, i.e. those that occupy the land for long periods and do not need to be replanted after each harvest. This category includes land planted with flowering shrubs, fruit trees, walnuts and vines, but excludes land planted with timber trees.
Food and Agriculture Organization (FAO)
Data are collected through the FAO Questionnaire on Land Use, Irrigation and Agricultural Practices, based on the FAO Land Use Classification.
FAO's classification of land use is aligned with the United Nations System of Environmental and Economic Accounting (SEEA), the United Nations Framework for the Development of Environmental Statistics (FDES) and the World Census of Agriculture. It is also consistent with the Intergovernmental Panel on Climate Change Land Use Classes for Countries' Relations to the United Nations Framework Convention on Climate Change (UNFCCC). A mapping of the FAO, SEEA, World Census of Agriculture and IPCC classifications is provided in the FAO questionnaire.
SDG Goal 15, indicator 15.1.1; ENP-South Eurostat Data Browser: Agriculture and Fisheries Area
92
Livestock production index (2014-2016 = 100)
Environment and Natural Resources
Agriculture
Value of livestock production in each year compared to the 2014-2016 base period. It includes meat and milk from all sources, dairy products such as cheese and eggs, honey, raw silk, wool, and hides.
Food and Agriculture Organization (FAO)
The index is based on the sum of the quantities weighted by the price of various agricultural commodities produced, after deducting the quantities used as seeds and feed, weighted in a similar manner. The resulting aggregate therefore represents the production available for any use other than seeds and feed. All national, regional and global indices are calculated using the Laspeyres formula. The production quantities of each commodity are weighted by the average international commodity prices for 2014-2016 and added up for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 2014-2016. Since FAO indices are based on the concept of agriculture as a single enterprise, seed and feed quantities are subtracted from production data to avoid double counting, once in production data and once in the crops or livestock produced from them. Deductions for seeds (in the case of eggs, for hatching) and for feed for livestock and poultry apply to both domestic and imported products. They only concern primary agricultural products intended for animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feeds such as bran, oilcake, meal and molasses have been completely excluded from the calculations at all stages. It should be noted that in the calculation of agricultural, food and non-food production indices, all primary intermediate inputs of agricultural origin are deducted. However, for the indices of any other commodity group, only inputs originating within the same group are deducted; thus, only seeds are removed from the 'crops' group and all crop subgroups, such as cereals, oilseeds, etc., and both feed and seeds originating within the livestock sector (e.g. feed for milk, hatching eggs) are removed from the 'livestock products' group. For the two main livestock sub-groups, namely meat and milk, only feed originating from the respective sub-group is removed. Indices that take into account deductions for feed and seeds are referred to as 'net'. Indices calculated without deductions for feed and seeds are referred to as 'gross'. "International commodity prices" are used to avoid the use of exchange rates to obtain continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These "international prices", expressed in so-called "international dollars", are derived using the Geary-Khamis formula for the agricultural sector. This method assigns a single "price" to each commodity. For example, a metric tonne of wheat has the same price regardless of the country in which it was produced. The currency unit in which prices are expressed does not affect the published indices. The products covered in the calculation of agricultural production indices are all crops and livestock products originating in each country. Virtually all products are covered, with the main exception being fodder crops.
None
None
93
Crop production index (2014-2016 = 100)
Environment and Natural Resources
Agriculture
Value of agricultural production in each year compared to the 2014-2016 base period. It includes all crops except forage crops.
Food and Agriculture Organization (FAO)
The index is based on the sum of the quantities weighted by the price of various agricultural commodities produced, after deducting the quantities used as seeds and feed, weighted in a similar manner. The resulting aggregate therefore represents the production available for any use other than seeds and feed. All national, regional and global indices are calculated using the Laspeyres formula. The production quantities of each commodity are weighted by the average international commodity prices for 2014-2016 and added up for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 2014-2016. Since FAO indices are based on the concept of agriculture as a single enterprise, seed and feed quantities are subtracted from production data to avoid double counting, once in production data and once in the crops or livestock produced from them. Deductions for seeds (in the case of eggs, for hatching) and for feed for livestock and poultry apply to both domestic and imported products. They only concern primary agricultural products intended for animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feeds such as bran, oilcake, meal and molasses have been completely excluded from the calculations at all stages. It should be noted that in the calculation of agricultural, food and non-food production indices, all primary intermediate inputs of agricultural origin are deducted. However, for the indices of any other commodity group, only inputs originating within the same group are deducted; thus, only seeds are removed from the 'crops' group and all crop subgroups, such as cereals, oilseeds, etc., and both feed and seeds originating within the livestock sector (e.g. feed for milk, hatching eggs) are removed from the 'livestock products' group. For the two main livestock sub-groups, namely meat and milk, only feed originating from the respective sub-group is removed. Indices that take into account deductions for feed and seeds are referred to as 'net'. Indices calculated without deductions for feed and seeds are referred to as 'gross'. "International commodity prices" are used to avoid the use of exchange rates to obtain continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These "international prices", expressed in so-called "international dollars", are derived using the Geary-Khamis formula for the agricultural sector. This method assigns a single "price" to each commodity. For example, a metric tonne of wheat has the same price regardless of the country in which it was produced. The currency unit in which prices are expressed does not affect the published indices. The products covered in the calculation of agricultural production indices are all crops and livestock products originating in each country. Virtually all products are covered, with the main exception being fodder crops.
None
None
94
Food production index (2014-2016 = 100)
Environment and Natural Resources
Agriculture
Value of food production each year compared to the 2014-2016 base period. It includes food crops that are considered edible and contain nutrients. Coffee and tea are excluded because, although edible, they have no nutritional value.
Food and Agriculture Organization (FAO)
The index is based on the sum of the quantities weighted by the price of various agricultural commodities produced, after deducting the quantities used as seeds and feed, weighted in a similar manner. The resulting aggregate therefore represents the production available for any use other than seeds and feed. All national, regional and global indices are calculated using the Laspeyres formula. The production quantities of each commodity are weighted by the average international commodity prices for 2014-2016 and added up for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 2014-2016. Since FAO indices are based on the concept of agriculture as a single enterprise, seed and feed quantities are subtracted from production data to avoid double counting, once in production data and once in the crops or livestock produced from them. Deductions for seeds (in the case of eggs, for hatching) and for feed for livestock and poultry apply to both domestic and imported products. They only concern primary agricultural products intended for animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feeds such as bran, oilcake, meal and molasses have been completely excluded from the calculations at all stages. It should be noted that in the calculation of agricultural, food and non-food production indices, all primary intermediate inputs of agricultural origin are deducted. However, for the indices of any other commodity group, only inputs originating within the same group are deducted; thus, only seeds are removed from the 'crops' group and all crop subgroups, such as cereals, oilseeds, etc., and both feed and seeds originating within the livestock sector (e.g. feed for milk, hatching eggs) are removed from the 'livestock products' group. For the two main livestock sub-groups, namely meat and milk, only feed originating from the respective sub-group is removed. Indices that take into account deductions for feed and seeds are referred to as 'net'. Indices calculated without deductions for feed and seeds are referred to as 'gross'. "International commodity prices" are used to avoid the use of exchange rates to obtain continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These "international prices", expressed in so-called "international dollars", are derived using the Geary-Khamis formula for the agricultural sector. This method assigns a single "price" to each commodity. For example, a metric tonne of wheat has the same price regardless of the country in which it was produced. The currency unit in which prices are expressed does not affect the published indices. The products covered in the calculation of agricultural production indices are all crops and livestock products originating in each country. Virtually all products are covered, with the main exception being fodder crops.
None
None
95
Fertilizer consumption (kilograms per hectare of arable land)
Environment and Natural Resources
Agriculture
Amount of plant nutrients used per unit of arable land. Fertilising products include nitrogen, potassium and phosphate fertilisers (including natural phosphate). Traditional nutrients - animal and plant fertilizers - are not included.
World Bank Development Indicators elaborations on Food and Agriculture Organization (FAO) data
Fertilizer consumption measures the amount of nutrients for plants and is calculated as production plus imports minus exports.
FAO has revised the time series for fertilizer consumption and irrigation since 2002. In the previous version, the data was based on the total fertilizer consumption, while in the recent version, they are based on the nutrients contained in the fertilizers. Some countries compile fertilizer data based on the calendar year, while others compile it based on the crop year (July-June). Previous editions of this indicator, Fertilizer Consumption (100 grams per hectare of arable land), reported data on the basis of the year of cultivation, but this edition uses the calendar year, as adopted by FAO. The data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time cannot be guaranteed. Secondary sources include official country data from national ministries websites, national publications and country data reported by various international organisations. Arable land includes land defined by FAO as temporary cropland (areas of dual cropping are counted only once), temporary mowing or grazing meadows, land planted with vegetable gardens or gardens, and temporarily fallow land. Land abandoned due to itinerant cultivation is excluded. Because some chemicals used for fertilizers have other industrial applications, consumption data can overestimate the amount available to crops.
SDG Goal 2, indicator 2.4.1
96
Agricultural methane emissions (thousand metric tons of CH4 equivalent)
Environment and Natural Resources
Agriculture
Methane emissions from animals, animal waste, rice production, burning of agricultural waste (non-energy, on-site) and burning in the savannah.
World Resources Institute (WRI)
To estimate emissions, countries party to the Convention on Climate Change (UNFCCC) use complex, state-of-the-art methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC). Methane emissions are largely derived from agricultural activities, industrially produced landfills and wastewater treatment, and other sources such as tropical forest fires and other vegetation.
Emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the actual contributions of different gases to be compared. One kilogram of methane is 21 times more effective at trapping heat in the Earth's atmosphere than one kilogram of carbon dioxide within 100 years.
None
97
Agricultural nitrous oxide emissions (thousand metric tons of N2O equivalent)
Environment and Natural Resources
Agriculture
Nitrous oxide emissions from the use of fertilizers (synthetics and animal manure), animal waste management, burning of agricultural waste (non-energy, on-site), and savannah burning.
World Resources Institute (WRI)
Nitrous oxide emissions of agricultural origin are those produced by the use of fertilizers (synthetic and of animal origin), animal waste management, the burning of agricultural waste (non-energy, on site) and the burning of the savannah, with reference to IPCC category 4 = Agriculture. They are expressed in CO2 equivalent using the GWP100 metric of the IPCC's Second Assessment Report and include nitrogen dioxide (N2O)
Nitrous oxide is a potent greenhouse gas, with an estimated atmospheric lifespan of 114 years, compared to 12 years for methane. The global warming potential per kilogram (GWP) of nitrous oxide is nearly 310 times that of carbon dioxide within 100 years. Emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the actual contributions of different gases to be compared.
None
98
Population, female
Gender Gaps
Population and Gender
Population by gender is based on the definition of a de facto population, which counts all residents regardless of legal status or citizenship. The values indicated refer to the average population.
a) elaborations of World Bank Development Indicators on data from the United Nations Population Division (UNPD), national statistical agencies, Eurostat; b) Istat for Italy
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.
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.
ENP-South Eurostat Data Browser: Population and Social Conditions Area
99
Population, male
Gender Gaps
Population and Gender
Population by gender is based on the definition of a de facto population, which counts all residents regardless of legal status or citizenship. The values shown refer to the average population.
a) elaborations of World Bank Development Indicators on data from the United Nations Population Division (UNPD), national statistical agencies, Eurostat; b) Istat for Italy
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.
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.
ENP-South Eurostat Data Browser: Population and Social Conditions Area
100
Population, female (% of total population)
Gender Gaps
Population and Gender
Percentage of female population (mid-year estimates).
a) elaborations of World Bank Development Indicators on data from the United Nations Population Division (UNPD), national statistical agencies, Eurostat; b) Istat for Italy
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.
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.
ENP-South Eurostat Data Browser: Population and Social Conditions Area
101
Population ages 0-14, female (% of female population)
Gender Gaps
Population and Gender
Female population aged 0-14 as a percentage of the total population as of 1 January each year.
a) Wemed elaborations on World Bank Development Indicators and United Nations Population Division (UNPD) data; b) Istat for Italy
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/.
None
ENP-South Eurostat Data Browser: Population and Social Conditions Area
102
Population ages 0-14, male (% of male population)
Gender Gaps
Population and Gender
Male population aged 0-14 years as a percentage of the total population as of 1 January each year.
a) Wemed elaborations on World Bank Development Indicators and United Nations Population Division data; b) Istat for Italy
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/.
None
None
103
Population ages 65 and above, female (% of female population)
Gender Gaps
Population and Gender
Female population aged 65 years and older as a percentage of the total population as of 1 January each year.
a) Wemed elaborations on World Bank Development Indicators and United Nations Population Division data; b) Istat for Italy
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/.
None
None
104
Population ages 65 and above, male (% of male population)
Gender Gaps
Population and Gender
Male population aged 65 years and older as a percentage of the total population as of 1 January each year.
a) Wemed elaborations on World Bank Development Indicators and United Nations Population Division data; b) Istat for Italy
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/.
None
None
105
Life expectancy at birth, female (years)
Gender Gaps
Population and Gender
Number of years a female infant would live if the mortality patterns prevalent at birth remained unchanged throughout life.
a) elaborations of World Bank Development Indicators on data from the United Nations Population Division (UNPD), national statistical agencies, Eurostat; b) Istat for Italy
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.
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.
ENP-South Eurostat Data Browser: Population and Social Conditions Area
106
Life expectancy at birth, male (years)
Gender Gaps
Population and Gender
The number of years a male infant would live if the mortality patterns prevalent at birth remained unchanged throughout life.
a) elaborations of World Bank Development Indicators on data from the United Nations Population Division (UNPD), national statistical agencies, Eurostat; b) Istat for Italy
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.
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.
ENP-South Eurostat Data Browser: Population and Social Conditions Area
107
Mortality rate, infant, female (per 1,000 live births)
Gender Gaps
Population and Gender
Number of female infants who die before reaching one year of age, per 1,000 live births in a given year.
a) World Bank elaborations on United Nations Inter-agency Group data for Child Mortality Estimation (UNCM); b) Istat for Italy
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.
None
None
108
Mortality rate, infant, male (per 1,000 live births)
Gender Gaps
Population and Gender
Number of male infants who die before reaching one year of age, per 1,000 live births in a given year.
a) World Bank elaborations on United Nations Inter-agency Group data for Child Mortality Estimation (UNCM); b) Istat for Italy
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.
None
None
109
Prevalence of current tobacco use, females (% of female adults)
Gender Gaps
Other Gender Issues
Percentage of the female population aged 15 years and older who currently use any tobacco product (smoked and/or smokeless) on a daily or non-daily basis. 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.
World Health Organization (WHO)
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 as a peer-reviewed article 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 and a 95% credibility interval around the estimate. Depending on the completeness/completeness 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. Estimates for countries with irregular surveys or with many gaps in the data will have wide ranges of uncertainty and such results should be interpreted with caution.
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.
None
110
Prevalence of current tobacco use, males (% of male adults)
Gender Gaps
Other Gender Issues
Percentage of the male population aged 15 years and older who currently use any tobacco product (smoked and/or smokeless) on a daily or non-daily basis. 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.
World Health Organization (WHO)
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 as a peer-reviewed article 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 and a 95% credibility interval around the estimate. Depending on the completeness/completeness 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. Estimates for countries with irregular surveys or with many gaps in the data will have wide ranges of uncertainty and such results should be interpreted with caution.
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.
None
111
Lower secondary completion rate, female (% of relevant age group)
Gender Gaps
Other Gender Issues
The total number of newly enrolled females in the last class of primary or lower secondary education, regardless of age, expressed as a percentage of the female population at the age at which they are expected to enter the last class of primary or lower secondary education. 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.
UNESCO and estimate based on UNESCO data for Egypt 2022
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.
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.
None
112
Lower secondary completion rate, male (% of relevant age group)
Gender Gaps
Other Gender Issues
The total number of new male enrolments in the last class of primary or lower secondary education, regardless of age, expressed as a percentage of the male population at the age at which they are expected to enter the last class of primary or lower secondary education. 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.
UNESCO and estimate based on UNESCO data for Egypt 2022
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.
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.
None
113
Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate)
Gender Gaps
Labour and gender
Percentage of the female population aged 15-24 economically active: all people who offer labour on the market for the production of goods and services in a given period.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
National data on labour force participation rates may not be comparable due to differences in concepts and methodologies. The most important factor affecting the comparability of data is the source of the data itself. Labour force data obtained from population censuses are often based on a limited number of questions about individuals' economic characteristics, with little scope for sampling. The resulting data are therefore generally not consistent with the corresponding labour force survey data and may vary considerably from one country to another, depending on the number and type of questions included in the census. Censuses and surveys of local units can, by their nature, only provide data on the employed population, excluding the unemployed and, in many countries, also excluding workers engaged in small production units or in the informal economy who do not fall within the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. However, despite their strength, labour force survey data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and the inclusion or exclusion of conscripted military personnel. In addition, there are variations in national definitions of the labour force concept, particularly with regard to the statistical treatment of certain specific groups, such as 'contributing family workers' and 'unemployed persons available for work but not seeking employment'. Non-comparability may also arise from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the labour force, with a cut-off point at 65 or 70 years, which affects broad comparisons, particularly those at higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of the ILO's modelled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the entire country (without geographical limitations) were used in the construction of the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates derived from this source were chosen in favour of those derived from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the 13th ICLS.
None
114
Labor force participation rate for ages 15-24, male (%) (modeled ILO estimate)
Gender Gaps
Labour and gender
Percentage of the male population aged 15-24 economically active: all people who offer labour on the market for the production of goods and services in a given period.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
National data on labour force participation rates may not be comparable due to differences in concepts and methodologies. The most important factor affecting the comparability of data is the source of the data itself. Labour force data obtained from population censuses are often based on a limited number of questions about individuals' economic characteristics, with little scope for sampling. The resulting data are therefore generally not consistent with the corresponding labour force survey data and may vary considerably from one country to another, depending on the number and type of questions included in the census. Censuses and surveys of local units can, by their nature, only provide data on the employed population, excluding the unemployed and, in many countries, also excluding workers engaged in small production units or in the informal economy who do not fall within the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. However, despite their strength, labour force survey data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and the inclusion or exclusion of conscripted military personnel. In addition, there are variations in national definitions of the labour force concept, particularly with regard to the statistical treatment of certain specific groups, such as 'contributing family workers' and 'unemployed persons available for work but not seeking employment'. Non-comparability may also arise from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the labour force, with a cut-off point at 65 or 70 years, which affects broad comparisons, particularly those at higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of the ILO's modelled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the entire country (without geographical limitations) were used in the construction of the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates derived from this source were chosen in favour of those derived from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the 13th ICLS.
None
115
Labor force participation rate, female (% of female population ages 15-64) (modeled ILO estimate)
Gender Gaps
Labour and gender
Percentage of the female population aged 15-64 economically active: all people who offer labour on the market for the production of goods and services in a given period.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
National data on labour force participation rates may not be comparable due to differences in concepts and methodologies. The most important factor affecting the comparability of data is the source of the data itself. Labour force data obtained from population censuses are often based on a limited number of questions about individuals' economic characteristics, with little scope for sampling. The resulting data are therefore generally not consistent with the corresponding labour force survey data and may vary considerably from one country to another, depending on the number and type of questions included in the census. Censuses and surveys of local units can, by their nature, only provide data on the employed population, excluding the unemployed and, in many countries, also excluding workers engaged in small production units or in the informal economy who do not fall within the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. However, despite their strength, labour force survey data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and the inclusion or exclusion of conscripted military personnel. In addition, there are variations in national definitions of the labour force concept, particularly with regard to the statistical treatment of certain specific groups, such as 'contributing family workers' and 'unemployed persons available for work but not seeking employment'. Non-comparability may also arise from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the labour force, with a cut-off point at 65 or 70 years, which affects broad comparisons, particularly those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of the ILO's modelled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the entire country (without geographical limitations) were used in the construction of the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates derived from this source were chosen in favour of those derived from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the 13th ICLS.
ENP-South Eurostat Data Browser: Population and Social Conditions Area
116
Labor force participation rate, male (% of male population ages 15-64) (modeled ILO estimate)
Gender Gaps
Labour and gender
Percentage of the male population aged 15-64 economically active: all people who offer labour on the market for the production of goods and services in a given period.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
National data on labour force participation rates may not be comparable due to differences in concepts and methodologies. The most important factor affecting the comparability of data is the source of the data itself. Labour force data obtained from population censuses are often based on a limited number of questions about individuals' economic characteristics, with little scope for sampling. The resulting data are therefore generally not consistent with the corresponding labour force survey data and may vary considerably from one country to another, depending on the number and type of questions included in the census. Censuses and surveys of local units can, by their nature, only provide data on the employed population, excluding the unemployed and, in many countries, also excluding workers engaged in small production units or in the informal economy who do not fall within the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. However, despite their strength, labour force survey data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and the inclusion or exclusion of conscripted military personnel. In addition, there are variations in national definitions of the labour force concept, particularly with regard to the statistical treatment of certain specific groups, such as 'contributing family workers' and 'unemployed persons available for work but not seeking employment'. Non-comparability may also arise from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the labour force, with a cut-off point at 65 or 70 years, which affects broad comparisons, particularly those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of the ILO's modelled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the entire country (without geographical limitations) were used in the construction of the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates derived from this source were chosen in favour of those derived from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the 13th ICLS.
ENP-South Eurostat Data Browser: Population and Social Conditions Area
117
Employment in agriculture, female (% of female employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Women of working age engaged in the agricultural sector in any activity of production of goods or provision of services for consideration or profit, whether they are working in the reference period or not working due to a temporary absence from work or an agreement on working time. The agricultural sector consists of agriculture, hunting, forestry and fishing, according to division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
The data presented by economic activity branch are based on the International Standard Industrial Classification of All Economic Activities (ISIC). Its main purpose is to provide a set of activity categories that can be used for the collection and reporting of statistics. The original version of ISIC was adopted in 1948 and has since been revised four times: in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of ISIC Rev. 3 was introduced in 2002 to take account of substantial changes in the economic structure of many countries (ISIC Rev. 3.1). It is important to note that countries may use different versions of ISIC, and that countries move to the adoption of the latest version at different times. A country may continue to use the previous version even after starting a new data series according to the latest version. Although these different classification systems may have an impact on comparability at detailed levels of economic activity, changes from one ISIC to another should not have a significant impact on the information for the three broad sectors presented in ILOSTAT. A number of factors may limit the comparability of employment statistics by economic activity between countries or over time. The comparability of employment statistics between countries is significantly affected by variations in the definitions used for employment data. Differences may arise from age coverage, such as lower and upper age limits for labour force activity. Employment estimates may also vary depending on whether members of the armed forces are included. When the armed forces are included in the measurement of employment, they are usually assigned to the service sector. Therefore, in countries that do not include the armed forces, the service sector tends to be underestimated compared to countries where they are included. Another area of measurement difference concerns the national treatment of particular groups of workers. The international definition of employment includes all persons who worked for at least one hour during the reference period. Workers may be paid or self-employed, even in less obvious forms of work, some of which are discussed in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeships or non-market production. Most exceptions to the coverage of all employed persons in a labour force survey have to do with minor national variations from the international recommendation applicable to alternative employment statuses. For example, some countries measure only paid employees, while others measure 'all employed persons', i.e. paid workers and business owners who receive remuneration based on company shares. Other possible variations to the rules for measuring total employment include hour limits (over one hour) imposed on family members who contribute before being included in employment. Comparisons can also be problematic when the frequency of data collection varies. The interval for collecting information can range from one month to 12 months in a year. Since seasonality of various kinds is undoubtedly present in all countries, employment data may vary for this reason alone. In addition, changes in the level of employment may occur during the year, but this may be obscured when fewer observations are available.
None
118
Employment in agriculture, male (% of male employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Men of working age engaged in the agricultural sector in any activity of production of goods or provision of services for consideration or profit, whether they are working during the reference period or not working because of a temporary absence from work or an agreement on working time. The agricultural sector consists of agriculture, hunting, forestry and fishing, according to division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
The data presented by economic activity branch are based on the International Standard Industrial Classification of All Economic Activities (ISIC). Its main purpose is to provide a set of activity categories that can be used for the collection and reporting of statistics. The original version of ISIC was adopted in 1948 and has since been revised four times: in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of ISIC Rev. 3 was introduced in 2002 to take account of substantial changes in the economic structure of many countries (ISIC Rev. 3.1). It is important to note that countries may use different versions of ISIC, and that countries move to the adoption of the latest version at different times. A country may continue to use the previous version even after starting a new data series according to the latest version. Although these different classification systems may have an impact on comparability at detailed levels of economic activity, changes from one ISIC to another should not have a significant impact on the information for the three broad sectors presented in ILOSTAT. A number of factors may limit the comparability of employment statistics by economic activity between countries or over time. The comparability of employment statistics between countries is significantly affected by variations in the definitions used for employment data. Differences may arise from age coverage, such as lower and upper age limits for labour force activity. Employment estimates may also vary depending on whether members of the armed forces are included. When the armed forces are included in the measurement of employment, they are usually assigned to the service sector. Therefore, in countries that do not include the armed forces, the service sector tends to be underestimated compared to countries where they are included. Another area of measurement difference concerns the national treatment of particular groups of workers. The international definition of employment includes all persons who worked for at least one hour during the reference period. Workers may be paid or self-employed, even in less obvious forms of work, some of which are discussed in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeships or non-market production. Most exceptions to the coverage of all employed persons in a labour force survey have to do with minor national variations from the international recommendation applicable to alternative employment statuses. For example, some countries measure only paid employees, while others measure 'all employed persons', i.e. paid workers and business owners who receive remuneration based on company shares. Other possible variations to the rules for measuring total employment include hour limits (over one hour) imposed on family members who contribute before being included in employment. Comparisons can also be problematic when the frequency of data collection varies. The interval for collecting information can range from one month to 12 months in a year. Since seasonality of various kinds is undoubtedly present in all countries, employment data may vary for this reason alone. In addition, changes in the level of employment may occur during the year, but this may be obscured when fewer observations are available.
None
119
Employment in industry, female (% of female employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Women of working age engaged in the industrial sector in any activity of production of goods or provision of services for consideration or profit, whether they are working during the reference period or not working because of a temporary absence from work or an agreement on working time. The industrial sector includes mineral extraction, manufacturing, construction and utilities (electricity, gas and water), according to divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
The data presented by economic activity branch are based on the International Standard Industrial Classification of All Economic Activities (ISIC). Its main purpose is to provide a set of activity categories that can be used for the collection and reporting of statistics. The original version of ISIC was adopted in 1948 and has since been revised four times: in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of ISIC Rev. 3 was introduced in 2002 to take account of substantial changes in the economic structure of many countries (ISIC Rev. 3.1). It is important to note that countries may use different versions of ISIC, and that countries move to the adoption of the latest version at different times. A country may continue to use the previous version even after starting a new data series according to the latest version. Although these different classification systems may have an impact on comparability at detailed levels of economic activity, changes from one ISIC to another should not have a significant impact on the information for the three broad sectors presented in ILOSTAT. A number of factors may limit the comparability of employment statistics by economic activity between countries or over time. The comparability of employment statistics between countries is significantly affected by variations in the definitions used for employment data. Differences may arise from age coverage, such as lower and upper age limits for labour force activity. Employment estimates may also vary depending on whether members of the armed forces are included. When the armed forces are included in the measurement of employment, they are usually assigned to the service sector. Therefore, in countries that do not include the armed forces, the service sector tends to be underestimated compared to countries where they are included. Another area of measurement difference concerns the national treatment of particular groups of workers. The international definition of employment includes all persons who worked for at least one hour during the reference period. Workers may be paid or self-employed, even in less obvious forms of work, some of which are discussed in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeships or non-market production. Most exceptions to the coverage of all employed persons in a labour force survey have to do with minor national variations from the international recommendation applicable to alternative employment statuses. For example, some countries measure only paid employees, while others measure 'all employed persons', i.e. paid workers and business owners who receive remuneration based on company shares. Other possible variations to the rules for measuring total employment include hour limits (over one hour) imposed on family members who contribute before being included in employment. Comparisons can also be problematic when the frequency of data collection varies. The interval for collecting information can range from one month to 12 months in a year. Since seasonality of various kinds is undoubtedly present in all countries, employment data may vary for this reason alone. In addition, changes in the level of employment may occur during the year, but this may be obscured when fewer observations are available.
None
120
Employment in industry, male (% of male employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Men of working age engaged in the industrial sector in any activity of production of goods or provision of services for consideration or profit, whether they are working during the reference period or not working because of a temporary absence from work or an agreement on working time. The industrial sector includes mineral extraction, manufacturing, construction and utilities (electricity, gas and water), according to divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
The data presented by economic activity branch are based on the International Standard Industrial Classification of All Economic Activities (ISIC). Its main purpose is to provide a set of activity categories that can be used for the collection and reporting of statistics. The original version of ISIC was adopted in 1948 and has since been revised four times: in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of ISIC Rev. 3 was introduced in 2002 to take account of substantial changes in the economic structure of many countries (ISIC Rev. 3.1). It is important to note that countries may use different versions of ISIC, and that countries move to the adoption of the latest version at different times. A country may continue to use the previous version even after starting a new data series according to the latest version. Although these different classification systems may have an impact on comparability at detailed levels of economic activity, changes from one ISIC to another should not have a significant impact on the information for the three broad sectors presented in ILOSTAT. A number of factors may limit the comparability of employment statistics by economic activity between countries or over time. The comparability of employment statistics between countries is significantly affected by variations in the definitions used for employment data. Differences may arise from age coverage, such as lower and upper age limits for labour force activity. Employment estimates may also vary depending on whether members of the armed forces are included. When the armed forces are included in the measurement of employment, they are usually assigned to the service sector. Therefore, in countries that do not include the armed forces, the service sector tends to be underestimated compared to countries where they are included. Another area of measurement difference concerns the national treatment of particular groups of workers. The international definition of employment includes all persons who worked for at least one hour during the reference period. Workers may be paid or self-employed, even in less obvious forms of work, some of which are discussed in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeships or non-market production. Most exceptions to the coverage of all employed persons in a labour force survey have to do with minor national variations from the international recommendation applicable to alternative employment statuses. For example, some countries measure only paid employees, while others measure 'all employed persons', i.e. paid workers and business owners who receive remuneration based on company shares. Other possible variations to the rules for measuring total employment include hour limits (over one hour) imposed on family members who contribute before being included in employment. Comparisons can also be problematic when the frequency of data collection varies. The interval for collecting information can range from one month to 12 months in a year. Since seasonality of various kinds is undoubtedly present in all countries, employment data may vary for this reason alone. In addition, changes in the level of employment may occur during the year, but this may be obscured when fewer observations are available.
None
121
Employment in services, female (% of female employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Women of working age engaged in the Services sector in any activity of producing goods or providing services for remuneration or profit, whether they were at work during the reference period, or not at work due to a temporary absence from a workplace or an agreement on working time. The services sector includes wholesale and retail trade, restaurants and hotels, transport, warehousing and communications, financing, insurance, real estate and business services, as well as social and personal services, according to divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
The data presented by economic activity branch are based on the International Standard Industrial Classification of All Economic Activities (ISIC). Its main purpose is to provide a set of activity categories that can be used for the collection and reporting of statistics. The original version of ISIC was adopted in 1948 and has since been revised four times: in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of ISIC Rev. 3 was introduced in 2002 to take account of substantial changes in the economic structure of many countries (ISIC Rev. 3.1). It is important to note that countries may use different versions of ISIC, and that countries move to the adoption of the latest version at different times. A country may continue to use the previous version even after starting a new data series according to the latest version. Although these different classification systems may have an impact on comparability at detailed levels of economic activity, changes from one ISIC to another should not have a significant impact on the information for the three broad sectors presented in ILOSTAT. A number of factors may limit the comparability of employment statistics by economic activity between countries or over time. The comparability of employment statistics between countries is significantly affected by variations in the definitions used for employment data. Differences may arise from age coverage, such as lower and upper age limits for labour force activity. Employment estimates may also vary depending on whether members of the armed forces are included. When the armed forces are included in the measurement of employment, they are usually assigned to the service sector. Therefore, in countries that do not include the armed forces, the service sector tends to be underestimated compared to countries where they are included. Another area of measurement difference concerns the national treatment of particular groups of workers. The international definition of employment includes all persons who worked for at least one hour during the reference period. Workers may be paid or self-employed, even in less obvious forms of work, some of which are discussed in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeships or non-market production. Most exceptions to the coverage of all employed persons in a labour force survey have to do with minor national variations from the international recommendation applicable to alternative employment statuses. For example, some countries measure only paid employees, while others measure 'all employed persons', i.e. paid workers and business owners who receive remuneration based on company shares. Other possible variations to the rules for measuring total employment include hour limits (over one hour) imposed on family members who contribute before being included in employment. Comparisons can also be problematic when the frequency of data collection varies. The interval for collecting information can range from one month to 12 months in a year. Since seasonality of various kinds is undoubtedly present in all countries, employment data may vary for this reason alone. In addition, changes in the level of employment may occur during the year, but this may be obscured when fewer observations are available.
None
122
Employment in services, male (% of male employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Men of working age engaged in the Services sector in any activity of producing goods or providing services for remuneration or profit, whether they were at work during the reference period, or not at work due to a temporary absence from a workplace or an agreement on working time. The services sector includes wholesale and retail trade, restaurants and hotels, transport, warehousing and communications, financing, insurance, real estate and business services, as well as social and personal services, according to divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
The data presented by economic activity branch are based on the International Standard Industrial Classification of All Economic Activities (ISIC). Its main purpose is to provide a set of activity categories that can be used for the collection and reporting of statistics. The original version of ISIC was adopted in 1948 and has since been revised four times: in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of ISIC Rev. 3 was introduced in 2002 to take account of substantial changes in the economic structure of many countries (ISIC Rev. 3.1). It is important to note that countries may use different versions of ISIC, and that countries move to the adoption of the latest version at different times. A country may continue to use the previous version even after starting a new data series according to the latest version. Although these different classification systems may have an impact on comparability at detailed levels of economic activity, changes from one ISIC to another should not have a significant impact on the information for the three broad sectors presented in ILOSTAT. A number of factors may limit the comparability of employment statistics by economic activity between countries or over time. The comparability of employment statistics between countries is significantly affected by variations in the definitions used for employment data. Differences may arise from age coverage, such as lower and upper age limits for labour force activity. Employment estimates may also vary depending on whether members of the armed forces are included. When the armed forces are included in the measurement of employment, they are usually assigned to the service sector. Therefore, in countries that do not include the armed forces, the service sector tends to be underestimated compared to countries where they are included. Another area of measurement difference concerns the national treatment of particular groups of workers. The international definition of employment includes all persons who worked for at least one hour during the reference period. Workers may be paid or self-employed, even in less obvious forms of work, some of which are discussed in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeships or non-market production. Most exceptions to the coverage of all employed persons in a labour force survey have to do with minor national variations from the international recommendation applicable to alternative employment statuses. For example, some countries measure only paid employees, while others measure 'all employed persons', i.e. paid workers and business owners who receive remuneration based on company shares. Other possible variations to the rules for measuring total employment include hour limits (over one hour) imposed on family members who contribute before being included in employment. Comparisons can also be problematic when the frequency of data collection varies. The interval for collecting information can range from one month to 12 months in a year. Since seasonality of various kinds is undoubtedly present in all countries, employment data may vary for this reason alone. In addition, changes in the level of employment may occur during the year, but this may be obscured when fewer observations are available.
None
123
Employment to population ratio, ages 15-24, female (%) (modeled ILO estimate)
Gender Gaps
Labour and gender
Percentage of employed female population of a country in the age group 15-24 years. Employment is defined as persons of working age who, during a short reporting period, have been engaged in any activity of producing goods or providing services for remuneration or profit, whether they were at work during the reporting period (i.e. worked at a workplace for at least one hour) or were not at work due to temporary absence from a post or agreements on working time. The age between 15 and 24 is generally considered the reference for the young population.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for countries for which this procedure is possible. This procedure produces accurate, low-variance estimates, which is not surprising, given that LFPR is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
None
None
124
Employment to population ratio, ages 15-24, male (%) (modeled ILO estimate)
Gender Gaps
Labour and gender
Percentage of employed male population of a country in the age group 15-24 years. Employment is defined as persons of working age who, during a short reporting period, have been engaged in any activity of producing goods or providing services for remuneration or profit, whether they were at work during the reporting period (i.e. worked at a workplace for at least one hour) or were not at work due to temporary absence from a post or agreements on working time. The age between 15 and 24 is generally considered the reference for the young population.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for countries for which this procedure is possible. This procedure produces accurate, low-variance estimates, which is not surprising, given that LFPR is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
None
None
125
Employers, female (% of female employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Women who, working on their own account or with one or more partners, carry out jobs in which the salary depends directly on the profits deriving from the goods and services produced, and who, in this capacity, have hired, on a continuous basis, one or more people who work for them as employees.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
126
Employers, male (% of male employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Men who, working on their own account or with one or more partners, carry out jobs in which the salary depends directly on the profits deriving from the goods and services produced, and who, in this capacity, have hired, on a continuous basis, one or more people who work for them as employees.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
127
Self-employed, female (% of female employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Women who, working on their own account or with one or more members or in cooperatives, carry out jobs in which the salary depends directly on the profits deriving from the goods and services produced. The self-employed comprise four subcategories: employers, self-employed workers, members of producer cooperatives and family workers.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
128
Self-employed, male (% of male employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Men who, working on their own account or with one or more members or in cooperatives, carry out jobs in which the salary depends directly on the profits deriving from the goods and services produced. The self-employed comprise four subcategories: employers, self-employed workers, members of producer cooperatives and family workers.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
129
Wage and salaried workers, female (% of female employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Women who perform the type of work defined as "subordinate work", in which the employed have explicit (written or oral) or implicit employment contracts that give them a basic wage that does not depend directly on the income of the unit for which they work.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
130
Wage and salaried workers, male (% of male employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Men who perform the type of work defined as "subordinate work", in which the employed have explicit (written or oral) or implicit employment contracts that give them a basic wage that does not depend directly on the income of the unit for which they work.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
131
Vulnerable employment, female (% of female employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Female family workers and self-employed workers as a percentage of total employment.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
132
Vulnerable employment, male (% of male employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Family workers and own-account workers as a percentage of total employment.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
133
Contributing family workers, female (% of female employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Women who are "self-employed" as self-employed workers in a market-oriented enterprise run by a related person living in the same household.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
134
Contributing family workers, male (% of male employment) (modeled ILO estimate)
Gender Gaps
Labour and gender
Men who are "self-employed" as workers on their own account in a 3market-oriented enterprise run by a related person living in the same household.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. Linear interpolation is used to fill in missing data for countries for which this procedure is possible. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
135
Unemployment, female (% of female labor force) (modeled ILO estimate)
Gender Gaps
Labour and gender
Share of the female workforce that does not have a job but is available and looking for a job.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for countries for which this procedure is possible. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
The unemployed include all persons of working age who: (a) were not in employment during the reference period, i.e. were not in paid employment or self-employment; (b) were currently available for employment, i.e. were available for paid employment or self-employment during the reference period; (c) were looking for a job, i.e. had taken specific actions in a certain recent period to seek paid employment or self-employment. Future start-ups, i.e. people who have not looked for work but have a future interest in the labour market (have made arrangements for a future start of work), as well as participants in vocational training or retraining programmes as part of employment promotion programmes, who were not "employed" on this basis, are also counted as unemployed. they were not "currently available" and did not "look for work" because they had a job offer to start within a short subsequent period, generally no longer than three months. The unemployed also include "unemployed" people who have migrated abroad to work for pay or profit, but who were still waiting for the opportunity to leave. A country's overall unemployment rate is a widely used measure of unused labor supply. Unemployment rates for specific groups, defined by age, gender, occupation or industry, are also useful for identifying the groups of workers and sectors most vulnerable to unemployment.
SDG Goal 8, indicator 8.5.2; ENP-South Eurostat Data Browser: Population and Social Conditions Area
136
Unemployment, male (% of male labor force) (modeled ILO estimate)
Gender Gaps
Labour and gender
Share of the male workforce that does not have a job but is available and looking for a job.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for countries for which this procedure is possible. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
The unemployed include all persons of working age who: (a) were not in employment during the reference period, i.e. were not in paid employment or self-employment; (b) were currently available for employment, i.e. were available for paid employment or self-employment during the reference period; (c) were looking for a job, i.e. had taken specific actions in a certain recent period to seek paid employment or self-employment. Future start-ups, i.e. people who have not looked for work but have a future interest in the labour market (have made arrangements for a future start of work), as well as participants in vocational training or retraining programmes as part of employment promotion programmes, who were not "employed" on this basis, are also counted as unemployed. they were not "currently available" and did not "look for work" because they had a job offer to start within a short subsequent period, generally no longer than three months. The unemployed also include "unemployed" people who have migrated abroad to work for pay or profit, but who were still waiting for the opportunity to leave. A country's overall unemployment rate is a widely used measure of unused labor supply. Unemployment rates for specific groups, defined by age, gender, occupation or industry, are also useful for identifying the groups of workers and sectors most vulnerable to unemployment.
SDG Goal 8, indicator 8.5.2; ENP-South Eurostat Data Browser: Population and Social Conditions Area
137
Proportion of seats held by women in national parliaments (%)
Gender Gaps
Other Gender Issues
Percentage of parliamentary seats in the single or lower House held by women.
Inter-Parliamentary Union (IPU)
The data are provided by national parliaments and updated after an election or parliamentary renewal. National parliaments also transmit their data to the Inter-Parliamentary Union (IPU) at least once a year and when the numbers change significantly. IPU member parliaments provide information on changes and updates to the IPU Secretariat. After each general election or renewal, a questionnaire is sent to parliaments to request the latest available data. If there is no response, other methods are used to obtain the information, such as the electoral management body, parliamentary websites or internet searches. Additional information gathered from other sources is regularly checked with Parliament. The data are updated on a monthly basis, up to the last day of the month.
The number of countries covered varies with the suspension or dissolution of parliaments. As of February 1, 2016, 193 countries are included. It can be difficult to obtain information on the results of by-elections and replacements due to death or resignation. These changes are ad hoc events that are harder to keep track of. By-elections, for example, are often not announced internationally like general elections. The data exclude the number and percentage of women in the upper houses of Parliament. Information is available on the website of the Inter-Parliamentary Union (IPU) at https://data.ipu.org/women-ranking. Parliaments vary greatly in their internal workings and procedures, but they typically legislate, oversee government, and represent voters. In terms of measuring women's contribution to political decision-making, this indicator may not be sufficient because some women may face obstacles in carrying out their parliamentary mandate fully and efficiently.
SDG Goal 5 and Goal 16, indicator 5.5.1/16.7.1
138
Women Business and the Law Index Score (scale 1-100)
Gender Gaps
Other Gender Issues
Composite index that measures the effect of laws and regulations on women's economic opportunities. The overall scores are calculated by comparing the average score of each index (Mobility, Workplace, Salary, Marriage, Parenthood, Entrepreneurship, Assets and Pension) to 100, which is the maximum score.
World Bank
The data are collected with standardized questionnaires to ensure comparability between the various economies. The questionnaires are administered to more than 2,000 respondents who are experts in family, labour and criminal law, including lawyers, judges, academics and members of civil society organisations working on gender issues. Respondents provide answers to questionnaires and references to relevant laws and regulations. The Women, Business and the Law team collects the texts of these codified sources of national law – constitutions, codes, laws, statutes, rules, regulations and procedures – and verifies the accuracy of the responses to the questionnaire. Thirty-five points are scored through eight indicators of four or five binary questions, with each indicator representing a different stage of a woman's career. Indicator-level scores are obtained by calculating the unweighted average of the questions within that indicator and scaling the result to 100. The overall scores are then calculated by taking the average of the questions of that indicator. Overall scores are then calculated by taking the average of each indicator, with 100 being the maximum possible score.
The methodology of Women, Business and the Law has limitations that must be considered when interpreting the data. All eight indicators are based on standardised assumptions to ensure comparability across economies. Comparability is one of the strengths of the data, but assumptions can also be limitations, as they may not capture all the restrictions or represent all the particularities of a country. A woman is assumed to reside in the main economic city, but in federal economies, laws affecting women may vary by state or province. Even in non-federal economies, rural and small-town women may face more restrictive local legislation. Such restrictions are not considered by Women, Business and the Law, unless they are also present in the main economic city.
None
139
Indice di sviluppo di genere
Gender Gaps
Other Gender Issues
Composite index that measures gender inequalities in three fundamental dimensions of human development: health, measured by the life expectancy at birth of women and men; education, measured by the years of schooling expected by women and men for children and the average years of schooling of women and men for adults aged 25 and over; the control of economic resources, measured by the estimated income from work of women and men. It is calculated as the ratio of women's Human Development Index (HDI) to men's.
United Nations Development Programme (UNDP)
It is a geometric mean of the normalised indices and ratios between female and male values, 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 for adults aged 25 and over: Barro and Lee (2018), ICF Macro Demographic and Health Surveys (various years), OECD (2022), UNESCO Institute for Statistics (2022) and UNICEF Multiple Indicator Cluster Surveys (various years). d) Estimates of labour income made by the Human Development Report based on the female and male shares of the economically active population, the ratio of female to male wages in all sectors and the gross national income at purchasing power parity (PPP) in 2017, as well as the female and male shares of the ILO source population (2022), IMF (2022), UNDESA (2022a), United Nations Statistics Division (2022) and World Bank (2022).
None
None
140
Indice di uguaglianza di genere
Gender Gaps
Other Gender Issues
Composite index that measures gender disadvantage in three dimensions: reproductive health, empowerment and labour market, for the largest number of countries where data of reasonable quality are available. It shows the loss of potential human development due to the inequality between female and male achievement in these dimensions. It ranges from 0, where women and men perform equally, to 1, where one of the two sexes performs the worst possible in all dimensions measured.
United Nations Development Programme (UNDP)
The values are calculated using the association-sensitive measure of inequality suggested by Seth (2009), which implies that the index is based on the general average of general means of different orders: the first aggregation is that of a geometric mean between dimensions; Separately calculated averages for women and men are then aggregated using a harmonic average between the genders. The index is based on the following indicators and sources: a) maternal mortality rate: WHO, UNICEF, UNFPA, World Bank Group, United Nations Population Division (2019); b) adolescent birth rate): UN/DESA (2022a); (c) share of parliamentary seats held by each sex: IPU (2022); d) population with some level of secondary education (: Barro and Lee (2018), ICF Macro Demographic and Health Surveys (various years), OECD (2022), UNESCO Institute for Statistics (2022) and United Nations Children's Fund Multiple Indicator Cluster Surveys (various years); e) labour force participation rate: ILO (2022).
Generally, the index assumes values below 1, but can also reach values above 1, in countries where the gaps between the female and male condition are overcome as a whole.
None
141
Employment to population ratio, 15+, total (%) (modeled ILO estimate)
Population and Society
Labor Market
Percentage of a country's employed population in the age group 15 years and over. Employment is defined as persons of working age who, during a short reporting period, have been engaged in any activity of producing goods or providing services for remuneration or profit, whether they were at work during the reporting period (i.e. worked at a workplace for at least one hour) or were not at work due to temporary absence from a post or agreements on working time. The age of 15 and above is generally considered the reference for the working-age population.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for countries for which this procedure is possible. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
142
Employment to population ratio, 15+, female (%) (modeled ILO estimate)
Gender Gaps
Labour and gender
Percentage of a country's female population in the age group 15 years and over. Employment is defined as persons of working age who, during a short reporting period, have been engaged in any activity of producing goods or providing services for remuneration or profit, whether they were at work during the reporting period (i.e. worked at a workplace for at least one hour) or were not at work due to temporary absence from a post or agreements on working time. The age of 15 and over is generally considered the reference for the working-age population.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for countries for which this procedure is possible. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
143
Employment to population ratio, 15+, male (%) (modeled ILO estimate)
Gender Gaps
Labour and gender
Percentage of the employed male population of a country in the age group 15 years and over. Employment is defined as persons of working age who, during a short reporting period, have been engaged in any activity of producing goods or providing services for remuneration or profit, whether they were at work during the reporting period (i.e. worked at a workplace for at least one hour) or were not at work due to temporary absence from a post or agreements on working time. The age of 15 and over is generally considered the reference for the working-age population.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for countries for which this procedure is possible. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
None
144
Agricultural land (sq. km)
Environment and Natural Resources
Agriculture
Area devoted to arable land, permanent crops and permanent pastures. Arable land includes land defined by FAO as temporary cropland (dual crop areas are counted only once), temporary meadows for mowing or grazing, land planted with vegetable gardens or gardens, and temporarily fallow land. Land abandoned due to itinerant cultivation is excluded. Permanent crop land is grown with crops that occupy the land for long periods and do not need to be replanted after each harvest. This category includes land planted with flowering shrubs, fruit trees, walnuts and vines, but excludes land planted with timber trees. Permanent pastures are land that is used for five or more years for fodder, including natural and cultivated crops.
Food and Agriculture Organization (FAO)
Data are collected through the FAO Questionnaire on Land Use, Irrigation and Agricultural Practices, based on the FAO Land Use Classification.
FAO's classification of land use is aligned with the United Nations System of Environmental and Economic Accounting (SEEA), the United Nations Framework for the Development of Environmental Statistics (FDES) and the World Census of Agriculture. It is also consistent with the Intergovernmental Panel on Climate Change Land Use Classes for Countries' Relations to the United Nations Framework Convention on Climate Change (UNFCCC). A mapping of the FAO, SEEA, World Census of Agriculture and IPCC classifications is provided in the FAO questionnaire.
None
145
Annual freshwater withdrawals, total (billion cubic meters)
Environment and Natural Resources
Environment, climate, and territory
Annual freshwater withdrawals refer to total water withdrawals, not counting evaporative losses from storage basins. The withdrawals also include water from desalination plants in countries where they represent a significant source.
Food and Agriculture Organization (FAO)
The data is based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). Data on freshwater resources are based on estimates of runoff into rivers and groundwater recharge.
Withdrawals can exceed 100% of total renewable resources when extraction from non-renewable aquifers or desalination plants is considerable or when there is significant water reuse. Levies for agriculture and industry are the total levies for irrigation and livestock farming and for direct industrial use (including levies for cooling thermal power plants). Domestic withdrawals include drinking water, municipal use or supply, and use for utilities, commercial establishments, and homes. These estimates are based on different sources and refer to different years, so comparisons between countries should be made with caution. Because data is collected intermittently, it can hide significant variations in total renewable water resources from one year to the next. In addition, the data does not distinguish between seasonal and geographical variations in water availability within countries. Data for small countries and those in arid and semi-arid areas are less reliable than those for larger countries and those with more rainfall. Caution should also be exercised when comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and accuracy, which requires caution in interpreting the data, particularly for water-strapped countries, especially in the Middle East and North Africa.
None
146
Level of water stress: freshwater withdrawal as a proportion of available freshwater resources
Environment and Natural Resources
Environment, climate, and territory
Ratio of total freshwater withdrawn from all major sectors to total renewable freshwater resources, after taking into account environmental water requirements. The main sectors, defined by ISIC standards, include agriculture, forestry and fisheries, manufacturing, electrical industry and services. This indicator is also known as water withdrawal intensity.
Food and Agriculture Organization (FAO)
Total freshwater withdrawal is the volume of freshwater extracted from the source (rivers, lakes, aquifers) for agriculture, industries, and municipalities. It is estimated at the national level for the following three main sectors: agriculture, municipalities (including domestic water withdrawal) and industries. Freshwater withdrawal includes primary freshwater (not previously withdrawn), secondary freshwater (previously withdrawn and returned to rivers and aquifers, such as wastewater and agricultural drainage water), and fossil groundwater. It does not include unconventional water, i.e. the direct use of treated wastewater, the direct use of agricultural drainage water and desalinated water. Total freshwater withdrawal is generally calculated as the sum of total water withdrawal per sector minus direct wastewater use, direct agricultural drainage water use, and desalinated water use. The actual total renewable water resources of a country or region are defined as the sum of internal renewable water resources and external renewable water resources, also expressed in km3/year. The indicator is calculated by dividing total water withdrawal by total actual renewable water resources minus environmental requirements and is expressed in percentage points. Total renewable water resources are expressed as the sum of internal and external renewable water resources. The terms "water resources" and "water withdrawals" are here understood as freshwater resources and freshwater withdrawals. Inland renewable water resources are defined as the long-term average annual flow of rivers and groundwater recharge for a given country, generated by endogenous rainfall. External renewable water resources refer to the flows of water entering the country, taking into account the amount of flows reserved for upstream and downstream countries through agreements or treaties. Environmental water requirements (Env.) are the amounts of water needed to sustain freshwater and estuarine ecosystems. Water quality and the ecosystem services that derive from it are excluded from this formulation, which is limited to water volumes. This does not mean that quality and support for societies dependent on environmental flows are not important and should not be taken into account. The methods of calculation of the Env. They are highly variable and range from global estimates to comprehensive assessments for waterways. Water volumes can be expressed in the same units of measurement as the total freshwater withdrawal and therefore as percentages of the available water resources.
Water abstraction as a percentage of water resources is a good indicator of pressure on limited water resources, one of the most important natural resources. However, it only partially addresses issues related to sustainable water management. Additional indicators that capture the multiple dimensions of water management would combine data on water demand management, behavioural changes related to water use, and the availability of adequate infrastructure, and would measure progress in increasing the efficiency and sustainability of water use, particularly in relation to population and economic growth. Furthermore, they recognise the different climatic contexts that influence water use in countries, particularly in agriculture, which is the main user of water. The assessment of sustainability is also linked to the critical thresholds set for this indicator, and there is no universal consensus on such a threshold. Water withdrawal trends show relatively slow patterns of change. Usually, three to five years is the minimum frequency required to detect significant changes, as the indicator is unlikely to show significant variations from one year to the next. Estimating water withdrawal by sector is the main limitation to calculating the indicator. Few countries regularly publish data on water use by sector. Renewable water resources include all surface and groundwater resources that are available on an annual basis, without considering the capacity to collect and use this resource. Exploitable water resources, which refer to the volume of surface or groundwater available with a 90% frequency, are significantly lower than renewable water resources, but there is no universal method for assessing these exploitable water resources. There is no universally agreed method for calculating freshwater inflows originating outside a country's borders. Nor is there a standard method for accounting for return flows, i.e. the portion of water withdrawn from the source that returns to the river system after use. In countries where return flows represent a substantial part of water abstraction, the indicator tends to underestimate available water and therefore overestimate the level of water stress. Other limitations affecting the interpretation of the water stress indicator are: difficulty in obtaining accurate, complete and up-to-date data; potentially wide variation in sub-national data; failure to consider seasonal variations in water resources; failure to consider distribution among water uses; failure to consider water quality and suitability for use. The indicator may exceed 100% when water abstraction includes secondary fresh water (water previously abstracted and returned to the system), non-renewable water (fossil groundwater), when annual groundwater abstraction exceeds annual recharge (over-abstraction), or when water abstraction includes some or all of the water set aside for environmental water requirements. Some of these issues can be resolved by disaggregating the index at the hydrological unit level and distinguishing between different sectors of use. However, given the complexity of water flows, both within and between countries, care must be taken to avoid double counting.
None
147
Solar photovoltaic, Electricity Installed Capacity MW
None
None
Installed electrical capacity from photovoltaic plants, expressed in megawatts. It measures the maximum power that can be delivered by solar systems connected to the grid.
International Renewable Energy Agency (IRENASTAT)
Energy capacity data represent the maximum net generation capacity of electrical systems and other installations used to produce energy. For most countries and technologies, these data refer to installed and grid-connected capacity at the end of the calendar year.
Installed electrical capacity from onshore wind farms, expressed in megawatts. It indicates the maximum power that can be produced by wind farms located on the earth's territory, connected to the electricity grid.
International Renewable Energy Agency (IRENASTAT)
Energy capacity data represent the maximum net generation capacity of electrical systems and other installations used to produce energy. For most countries and technologies, these data refer to installed and grid-connected capacity at the end of the calendar year.
Installed electrical capacity from hydroelectric plants, expressed in megawatts. It represents the maximum power that can be generated by exploiting water energy, through renewable plants connected to the grid.
International Renewable Energy Agency (IRENASTAT)
Energy capacity data represent the maximum net generation capacity of electrical systems and other installations used to produce energy. For most countries and technologies, these data refer to installed and grid-connected capacity at the end of the calendar year.
None
None
150
Oil, Electricity Installed Capacity MW
None
None
Installed electrical capacity of generation plants fuelled by fuel oil or petroleum derivatives, expressed in megawatts.
International Renewable Energy Agency (IRENASTAT)
Energy capacity data represent the maximum net generation capacity of electrical systems and other installations used to produce energy. For most countries and technologies, these data refer to installed and grid-connected capacity at the end of the calendar year.
None
None
151
Natural gas, Electricity Installed Capacity MW
None
None
Installed electrical capacity of natural gas-fired generation plants, expressed in megawatts. Indicates the power available from gas systems connected to the grid.
International Renewable Energy Agency (IRENASTAT)
Energy capacity data represent the maximum net generation capacity of electrical systems and other installations used to produce energy. For most countries and technologies, these data refer to installed and grid-connected capacity at the end of the calendar year.
None
None
152
Renewable energy share of electricity capacity and generation (%)
None
None
Percentage share of renewable energy in installed electricity capacity and/or actual electricity production. It indicates how much renewable sources contribute to the national energy structure.
International Renewable Energy Agency (IRENASTAT)
Energy capacity data represent the maximum net generation capacity of electrical systems and other installations used to produce energy. For most countries and technologies, these data refer to installed and grid-connected capacity at the end of the calendar year.
Passengers transported by rail represent the product of the number of passengers transported and the kilometres they travelled.
World Bank
Passenger kilometres are calculated by multiplying the railway distance travelled between origin and destination by the number of passengers making that journey. The variable refers to all passengers, regardless of the fare paid, including those who travel for free, but excluding train staff. The number of passengers is counted as the number of passenger journeys, where a journey is the movement from a place of origin to a place of destination using the railway, and can be composed of a single leg or several consecutive legs, provided that they are made by the same means of transport, so that the destination of one leg coincides with the origin of the next. A trip is considered to have ended in the event of an overnight stay, or, for convenience, when there is a change of mode of transport or company. Passenger kilometres therefore represent the total distance travelled by all passengers: for example, one person travelling 20 km contributes 20 passenger-kilometres, while four people travelling 20 km each contribute 80 passenger-kilometres.
None
None
154
Rail lines (total route-km)
None
None
The total length of the railway network operating in the country, whether used for passenger transport, freight transport, or both.
World Bank
Railway lines represent the length of the rail network available for rail service, regardless of the number of parallel tracks. Only routes open to public passenger and freight transport are included, while private railways dedicated to specific uses (e.g. transport of resources) are excluded. The lines are classified by gauge: N (standard gauge 1,435 m), L (broad gauge, indicated exactly), E (narrow gauge, indicated exactly). The length of the network is calculated considering the active sections, including those present in the capital expenditure accounts, while only the sections that are permanently out of use, i.e. no longer maintained in operational conditions, are excluded. Temporarily inactive routes continue to be counted. The length is measured in the center of the sections, from the center of the passenger or service buildings of the stations indicated as independent points of departure or arrival. If the rail network boundary falls on an open section, the measure extends up to that point. When multiple lines converge and use the same main section, it is counted only once, except in cases where there are separate tracks normally assigned to the individual lines, which are then counted separately. In the case of parallel tracks (e.g. service tracks), only the length of the main line is considered. The lines operating only in certain periods of the year (seasonal lines) are in any case included in the year-end measure.
None
None
155
Container port traffic (TEU: 20 foot equivalent units)
None
None
Port container traffic measures the flow of containers from land to sea transport modes, and vice versa, in twenty-foot equivalent units (TEUs), a standard-size container. Data refer to coastal shipping as well as international journeys. Transshipment traffic is counted as two lifts at the intermediate port (once to off-load and again as an outbound lift) and includes empty units.
World Bank
The TEU (Twenty-foot Equivalent Unit) is the international standard unit used to measure container capacity and port traffic volume, and corresponds to a 20-foot container length, which is the minimum reference size. Containers can vary in length (from 20 to over 50 feet), but the TEU is used as a conversion unit: for example, two 20-foot containers are equivalent to a FEU (Forty-foot Equivalent Unit). Container ship capacity and port handling are commonly expressed in TEUs.
None
None
156
Mean annual air temperature at 2 m above the surface of land
Environment and Natural Resources
Environment, climate, and territory
Average annual air temperature at 2 m above the ground surface.
Wemed elaborations on Copernicus Climate Data Store, ERA5-Land data.
ERA5-Land is a climate reanalysis dataset that provides a consistent and detailed view of the evolution of land-related variables (such as soil temperature, soil moisture, precipitation, snow, evaporation, etc.) over several decades. The average annual temperature is calculated by aggregating the monthly average data from a global dataset structured on a regular grid of latitude and longitude. The country-specific data is obtained, in turn, through the spatial aggregation (arithmetic mean) of the grid values included in the respective national territory.
The spatial resolution of the regular grid is about 9 km (0.1° x 0.1°)
None
157
Total precipitation
Environment and Natural Resources
Environment, climate, and territory
Annual cumulative precipitation
Wemed elaborations on Copernicus Climate Data Store, ERA5-Land data.
ERA5-Land is a climate reanalysis dataset that provides a consistent and detailed view of the evolution of land-related variables (such as soil temperature, soil moisture, precipitation, snow, evaporation, etc.) over several decades. Total annual precipitation is calculated by aggregating the monthly cumulative data from a global dataset structured on a regular grid of latitude and longitude. The country-specific data is obtained, in turn, through the spatial aggregation (arithmetic mean) of the grid values included in the respective national territory.
The spatial resolution of the regular grid is about 9 km (0.1° x 0.1°)
None
158
Total Evaporation
Environment and Natural Resources
Environment, climate, and territory
Accumulated amount of water evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into water vapor in the air above.
Wemed elaborations on Copernicus Climate Data Store, ERA5-Land data.
ERA5-Land is a climate reanalysis dataset that provides a consistent and detailed view of the evolution of land-related variables (such as soil temperature, soil moisture, precipitation, snow, evaporation, etc.) over several decades. The total annual evaporation is calculated by aggregating the cumulative monthly data of a global dataset structured on a regular grid of latitude and longitude. The country-specific data is obtained, in turn, through the spatial aggregation (arithmetic mean) of the grid values included in the respective national territory. The convention provides that downward flows are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.
The spatial resolution of the regular grid is about 9 km (0.1° x 0.1°)
None
159
Share of youth not in education, employment or training, total (% of youth population) (modeled ILO estimate)
Population and Society
Labor Market
Young people not in employment, employment or training, total (% of youth population) (ILO modelled estimate)
World Bank elaborations on International Labour Organization (ILO) data
The NEET rate = (young people – young people in employment – young people not in employment but in education or training) / young people x 100. It is important to note that young people who are simultaneously employed and in education or training should not be counted twice when subtracted from the total number of young people. The formula can also be expressed as: NEET rate = [(Young unemployed + Young people out of the labour force) – (Young unemployed people in education or training + Young people out of the labour force in education or training)] / Young people x 100. Young people who are not in education are those who are not enrolled in school or in a formal training programme (e.g. vocational training). For the purposes of this indicator, young people are defined as all people aged between 15 and 24 (inclusive).
