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 1,447 indicators as of March 2023 in the World Bank Development Indicators database as a reference, a pre-selection was made of 1,072 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 146 WeMed indicators were chosen: 142 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:
by any indicator in the Thematic Pages: clicking on the name of the indicator in the statistical overview at the top of each page, information on definition and sources is popped up;
by any indicator in the Dashboard: in relation to the selected indicator, a indow opens containing information on definition, sources, methodology, notes and warnings, presence in policy-oriented information systems, links to sources;
for all indicators: the complete excel file can be downloaded; a shortened version with list, definitions and sources is in Annex 1 of this document.
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) World Bank Development Indicators, from 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) World Bank Development Indicators, from 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) World Bank Development Indicators, from 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) United Nations Population Division; b) Istat for Italy
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) World Bank Development Indicators, from: United Nations Population Division, National Statistical Offices, Eurostat; b) Istat for Italy
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 for Italy
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
The percentage of the population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis.
WHO
A statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015). Once the age-and-sex-specific prevalence rates from national surveys were compiled into a dataset, the model was fit to calculate trend estimates from the year 2000 to 2025. The model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. Depending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population.
Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population. Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution.
None
11
Prevalence of moderate or severe food insecurity in the population (%)
Population and Society
Other Social Issues
The percentage of people in the population who live in households classified as moderately or severely food insecure. A household is classified as moderately or severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to low quality diets and might have been forced to also reduce the quantity of food they would normally eat because of a lack of money or other resources.
FAO
Data are collected through a survey module in a questionnaire (Gallup World Poll).
Validity and reliability is evaluated as very high. Margin of error can vary from 0.5% to 10% of the value of the prevalence depending on the sample size.
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 ages 20-79 who have type 1 or type 2 diabetes. It is calculated by adjusting to a standard population age-structure.
International Diabetes Federation
Data come from a variety of sources such as peer-reviewed scientific papers, and national and regional health surveys. Official reports by international organisations, such as the World Health Organization (WHO), were also assessed for their quality that was defined in consensus with an international expert panel. Data sources that passed strict selection criteria were included in the data analysis.
People with undiagnosed diabetes are included in the total estimated number of people with diabetes for 2021.
SDG Goal 3, indicator 3.8.1
13
Gross intake ratio to the last grade of lower secondary general education, both sexes (%)
Population and Society
Other Social Issues
Total number of new entrants into the last grade of lower secondary general education, regardless of age, expressed as a percentage of the population at the intended entrance age to the last grade of or lower secondary general education.The intended entrance age to the last grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade.
UNESCO
Data come from Population censuses and household surveys which collect data on the highest level of education or grade completed by children and young people in a household, through self- or household-declaration. In the former case, each household member above a certain age reports his or her own level of educational attainment. In the latter case, one person, usually the head of the household or another reference person, indicates the highest grade and/or level of education completed by each member of the household. Administrative data from ministries of education on the structure of the education system (entrance ages and durations) are also needed. Surveys can serve as a source of data if they collect information for the age groups of concern. In addition to national surveys, international sample surveys, such as Demographic and Health Surveys (DHS, http://dhsprogram.com) or Multiple Indicator Cluster Surveys (MICS, http://mics.unicef.org), are another source. These surveys are designed to meet commonly agreed upon international data needs and aim to assure cross-national comparability, while also providing data for national policy purposes. These surveys are implemented on a regular basis in selected countries, on average every 3 to 5 years.
The number of new entrants in the last grade of the given level of education, regardless of age, is expressed as a percentage of the population of the intended entrance age to the last grade of that level of education. If data on new entrants are not collected directly, they can be calculated by subtracting the number of pupils repeating the last grade from total enrolment in the last grade. This is a gross measure and may therefore exceed 100% if there are large numbers of pupils who entered school either early or late and/or who have repeated earlier grades. The fact that the indicator can exceed 100% also makes it more difficult to interpret than the completion rate. Compared to the completion rate, the gross intake ratio to the last grade does not indicate how many children complete the last grade, only how many children enter that grade. If students in the last grade leave school before graduation, the gross intake ratio to the last grade overestimates completion. Data limitations preclude adjusting for students who drop out during the final year of lower secondary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual lower secondary completion rate.
None
14
Labor force participation rate, total (% of total population ages 15-64)
Population and Society
Labor Market
Percentage of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
National data on labour force participation rates may not be comparable owing to differences in concepts and methodologies. The single most important factor affecting data comparability is the data source. Labour force data obtained from population censuses are often based on a restricted number of questions on the economic characteristics of individuals, with little possibility of probing. The resulting data, therefore, are generally not consistent with 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. Establishment censuses and surveys can – by their nature – only provide data on the employed population, leaving out the unemployed and, in many countries, also excluding workers engaged in small establishments or in the informal economy who fall outside the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
15
Labor force participation rate for ages 15-24, total (%)
Population and Society
Labor Market
Percentage of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
National data on labour force participation rates may not be comparable owing to differences in concepts and methodologies. The single most important factor affecting data comparability is the data source. Labour force data obtained from population censuses are often based on a restricted number of questions on the economic characteristics of individuals, with little possibility of probing. The resulting data, therefore, are generally not consistent with 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. Establishment censuses and surveys can – by their nature – only provide data on the employed population, leaving out the unemployed and, in many countries, also excluding workers engaged in small establishments or in the informal economy who fall outside the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
16
Employment to population ratio, 15+, total (%)
Population and Society
Labor Market
Percentage of a country's population ages 15 years and over that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered 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 series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
17
Employment to population ratio, ages 15-24, total (%)
Population and Society
Labor Market
Percentage of a country's population ages 15-24 that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.
ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
18
Employment in agriculture (% of total employment)
Population and Society
Labor Market
Persons of working age engaged in the agricoltural sector to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with 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 series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Data presented by branch of economic activity is 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 utilized for the collection and reporting of statistics according to such activities. The original version of ISIC was adopted in 1948, and it has been revised four times since : in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of the ISIC Rev.3 was introduced in 2002 to account for substantial changes in many countries’ economic structure (ISIC Rev. 3.1). It is important to note that different versions of the ISIC can be used across countries, with countries moving to adopting the most recent version at different paces. 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 can 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 can limit the comparability of statistics on employment by economic activity between countries or over time. Comparability of employment statistics across countries is affected most significantly by variations in the definitions used for the employment figures. Differences may result from age coverage, such as the lower and upper age bounds for labour force activity. Estimates of employment are also likely to vary according to whether members of the armed forces are included. When the armed forces are included in the measure of employment they are usually allocated to the services sector. Therefore, in countries that do not include armed forces, the services sector tends to be understated in comparison with countries where they are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labour force survey have to do with slight national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members before for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment figures can vary for this reason alone. Also, changes in the level of employment can occur throughout the year, but this can be obscured when fewer observations are available.
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
19
Employment in industry (% of total employment)
Population and Society
Labor Market
Persons of working age engaged in the industrial sector to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with 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 series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Data presented by branch of economic activity is 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 utilized for the collection and reporting of statistics according to such activities. The original version of ISIC was adopted in 1948, and it has been revised four times since : in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of the ISIC Rev.3 was introduced in 2002 to account for substantial changes in many countries’ economic structure (ISIC Rev. 3.1). It is important to note that different versions of the ISIC can be used across countries, with countries moving to adopting the most recent version at different paces. 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 can 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 can limit the comparability of statistics on employment by economic activity between countries or over time. Comparability of employment statistics across countries is affected most significantly by variations in the definitions used for the employment figures. Differences may result from age coverage, such as the lower and upper age bounds for labour force activity. Estimates of employment are also likely to vary according to whether members of the armed forces are included. When the armed forces are included in the measure of employment they are usually allocated to the services sector. Therefore, in countries that do not include armed forces, the services sector tends to be understated in comparison with countries where they are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labour force survey have to do with slight national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members before for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment figures can vary for this reason alone. Also, changes in the level of employment can occur throughout the year, but this can be obscured when fewer observations are available.
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
20
Employment in services (% of total employment)
Population and Society
Labor Market
Persons of working age engaged in the tertiary sector to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with 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 series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Data presented by branch of economic activity is 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 utilized for the collection and reporting of statistics according to such activities. The original version of ISIC was adopted in 1948, and it has been revised four times since : in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of the ISIC Rev.3 was introduced in 2002 to account for substantial changes in many countries’ economic structure (ISIC Rev. 3.1). It is important to note that different versions of the ISIC can be used across countries, with countries moving to adopting the most recent version at different paces. 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 can 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 can limit the comparability of statistics on employment by economic activity between countries or over time. Comparability of employment statistics across countries is affected most significantly by variations in the definitions used for the employment figures. Differences may result from age coverage, such as the lower and upper age bounds for labour force activity. Estimates of employment are also likely to vary according to whether members of the armed forces are included. When the armed forces are included in the measure of employment they are usually allocated to the services sector. Therefore, in countries that do not include armed forces, the services sector tends to be understated in comparison with countries where they are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labour force survey have to do with slight national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members before for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment figures can vary for this reason alone. Also, changes in the level of employment can occur throughout the year, but this can be obscured when fewer observations are available.
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
21
Wage and salaried workers, total (% of total employment)
Population and Society
Labor Market
Persons who hold the type of jobs defined as 'paid employment jobs,' where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
22
Self-employed, total (% of total employment)
Population and Society
Labor Market
Persons who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a 'self-employment jobs.' i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include four sub-categories of employers, own-account workers, members of producers' cooperatives, and contributing family workers.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
23
Employers, total (% of total employment)
Population and Society
Labor Market
Persons who, working on their own account or with one or a few partners, hold the type of jobs defined as a 'self-employment jobs' i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
24
Vulnerable employment, total (% of total employment)
Population and Society
Labor Market
Contributing family workers and own-account workers as a percentage of total employment.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
25
Contributing family workers, total (% of total employment)
Population and Society
Labor Market
Persons who hold 'self-employment jobs' as own-account workers in a market-oriented establishment operated by a person living in the same household.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
26
Unemployment, total (% of total labor force)
Population and Society
Labor Market
Share of the labor force that is without work but available for and seeking employment.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labour market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programmes, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave. The overall unemployment rate for a country is a widely used measure of its unutilized labour supply. Unemployment rates by specific groups, defined by age, sex, occupation or industry, are also useful in identifying groups of workers and sectors most vulnerable to joblessness.
SDG Goal 8, indicator 8.5.2; ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
27
Unemployment, youth total (% of total labor force ages 15-24)
Population and Society
Labor Market
Share of the labor force ages 15-24 without work but available for and seeking employment.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labour market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programmes, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave. The overall unemployment rate for a country is a widely used measure of its unutilized labour supply. Unemployment rates by specific groups, defined by age, sex, occupation or industry, are also useful in identifying groups of workers and sectors most vulnerable to joblessness.
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
28
Fixed broadband subscriptions (per 100 people)
Population and Society
Other Social Issues
Share per 100 residents of fixed subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s.
a) International Telecommunication Union; b) World Bank Development Indicators for West Bank and Gaza
The data are collected directly from governments by means of annual questionnaires sent to the agency in-charge of telecommunications/ICT (regulator or ministry). The data are verified and harmonized to ensure international comparability and compliance with international standards as outlined in the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT and the Core list of ICT indicators developed by the Partnership on Measuring ICT for Development. Data are collected two times each year through questionnaires sent to governments. In April a short questionnaire is sent requesting data on key telecommunication/ICT indicators such as fixed-telephone subscriptions, mobile-cellular subscriptions, fixed-broadband subscriptions (total and by speed tiers), international bandwidth, mobile and fixed broadband traffic, and mobile population coverage (total, 3G, 4G and above). In September a long questionnaire is sent requesting data on all telecommunication/ICT indicators included in the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT. Data are validated and discrepancies clarified through communication with countries before they are disseminated.
Fixed broadband Internet includes cable modem, DSL, fibre and other fixed broadband technology (such as satellite broadband Internet, Ethernet LANs, fixed-wireless access, Wireless Local Area Network, WiMAX etc.). Subscribers with access to data communications (including the Internet) via mobile cellular networks are excluded. Advertised and real speeds can differ substantially. Because survey questions and definitions differ, the estimates may not be strictly comparable across countries. In some countries, regulatory authorities monitor the speed and quality of broadband services and oblige operators to provide accurate quality-of-service information to end users. Regional and global totals are calculated as unweighted sums of the country values. Regional and global penetration rates (per 100 inhabitants) are weighted averages of the country values weighted by the population of the countries/regions. Discrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is end of December of every year. A number of countries have fiscal years that end in March or June of every year.
None
29
Individuals using the Internet (% of population)
Population and Society
Other Social Issues
Share per 100 residents of the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones.
a) International Telecommunication Union; b) World Bank Development Indicators for West Bank and Gaza
The data are collected directly from governments by means of annual questionnaires sent to the agency in-charge of telecommunications/ICT (regulator or ministry). The data are verified and harmonized to ensure international comparability and compliance with international standards as outlined in the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT and the Core list of ICT indicators developed by the Partnership on Measuring ICT for Development. Data are collected two times each year through questionnaires sent to governments. In April a short questionnaire is sent requesting data on key telecommunication/ICT indicators such as fixed-telephone subscriptions, mobile-cellular subscriptions, fixed-broadband subscriptions (total and by speed tiers), international bandwidth, mobile and fixed broadband traffic, and mobile population coverage (total, 3G, 4G and above). In September a long questionnaire is sent requesting data on all telecommunication/ICT indicators included in the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT. Data are validated and discrepancies clarified through communication with countries before they are disseminated.
Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure would be the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year.
SDG Goal 17, indicator 17.8.1
30
Mobile cellular subscriptions (per 100 people)
Population and Society
Other Social Issues
Share per 100 residents of the ubscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology.
World Bank Develpoment Indicators, from International Telecommunication Union
Data on mobile cellular subscribers are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators.Data for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports.
The indicator includes postpaid and prepaid subscriptions and includes analogue and digital cellular systems, using cellular technology, including number of pre-paid SIM cards active during the past three months. This includes both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services should also be excluded. This should include all mobile cellular subscriptions that offer voice communications.
None
31
Human Development Index (min=0, max=1)
Population and Society
Other Social Issues
Composite index which measures achievements in three key dimensions of human development: a long and healthy life, access to knowledge and a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.
United Nations Development Programme
It is a geometric mean of normalized indices, based upon the following indicators: a) Life expectancy at birth: UN/DESA (2022a). b) Expected years of schooling: CEDLAS and World Bank (2022), ICF Macro Demographic and Health Surveys (various years), UNESCO Institute for Statistics (2022) and United Nations Children’s Fund (UNICEF) Multiple Indicator Cluster Surveys (various years). c) Mean years of schooling: Barro and Lee (2018), ICF Macro Demographic and Health Surveys (var¬ious years), OECD (2022), UNESCO Institute for Statistics (2022) and UNICEF Multiple Indicator Cluster Surveys (various years). d) GNI per capita: IMF (2022), UN/DESA (2022b), United Nations Statistics Division (2022) and World Bank (2022).
None
None
32
Inequality-adjusted Human Development Index (min=0, max=1)
Population and Society
Other Social Issues
Composite index which adjusts the Human Development Index (HDI) for inequality in the distribution of each dimension across the population.
United Nations Development Programme
It is based on a distribution-sensitive class of composite indices proposed by Foster, Lopez-Calva and Szekely (2005), which draws on the Atkinson (1970) family of inequality measures. It is computed as a geometric mean of inequality-adjusted dimensional indices. Inequality in the distribution of HDI dimensions is estimated for: * Life expectancy, using data from complete life tables provided by UN/DESA (2022a). * Mean years of schooling, using household surveys data harmonized in international databases, including the Luxembourg Income Study, Eurostat’s European Union Survey of Income and Living Conditions, the World Bank’s International Income Distribution Database, ICF Macro’s Demographic and Health Surveys, United Nations Children’s Fund’s Multiple Indicators Cluster Surveys, the Center for Distributive, Labour and Social Studies and the World Bank’s Socio-Economic Database for Latin America and the Caribbean, the United Nations Educational, Scientific and Cultural Organization Institute for Statistics’ Educational Attainment Table and the United Nations University’s World Income Inequality Database. * Disposable household income or consumption per capita using the above listed databases and household surveys—and for some countries, income imputed based on an asset index matching methodology using household survey asset indices (Harttgen and Vollmer 2013). The asset index is provided in microdata from ICF Macro Demographic and Health Surveys and United Nations Children’s Fund Multiple Indicator Cluster Surveys.
None
None
33
GDP (US$ billion, constant 2015 prices)
Economy
Macroeconomics and Public Finance
GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 prices, expressed in U.S. dollars. Dollar figures for GDP are converted from domestic currencies using 2015 official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.
World Bank Development Indicators, from World Bank and OECD
Gross domestic product (GDP) represents the sum of value added by all its producers plus any product taxes and minus any subsidies not included in the value of the products. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either 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 valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices.
An economy's growth is measured by the change in the volume of its output or in the real incomes of its residents. The 2008 United Nations System of National Accounts (2008 SNA) offers three plausible indicators for calculating growth: the volume of gross domestic product (GDP), real gross domestic income, and real gross national income. The volume of GDP is the sum of value added, measured at constant prices, by households, government, and industries operating in the economy. GDP accounts for all domestic production, regardless of whether the income accrues to domestic or foreign institutions. Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently 'netted out' using equally crude and ad hoc approximations. Data for OECD countries are based on ISIC, revision 4.
None
34
GDP (US$ billion, current values)
Economy
Macroeconomics and Public Finance
GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.
World Bank Development Indicators, from World Bank and OECD
Gross domestic product (GDP) represents the sum of value added by all its producers plus any product taxes and minus any subsidies not included in the value of the products. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either 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 valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices.
An economy's growth is measured by the change in the volume of its output or in the real incomes of its residents. The 2008 United Nations System of National Accounts (2008 SNA) offers three plausible indicators for calculating growth: the volume of gross domestic product (GDP), real gross domestic income, and real gross national income. The volume of GDP is the sum of value added, measured at constant prices, by households, government, and industries operating in the economy. GDP accounts for all domestic production, regardless of whether the income accrues to domestic or foreign institutions. Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently 'netted out' using equally crude and ad hoc approximations. Data for OECD countries are based on ISIC, revision 4.
ENP-South Eurostat Data Browser: Area ' Economy and Finance'
35
GDP growth (annual %)
Economy
Macroeconomics and Public Finance
Annual percentage growth rate of GDP at market prices based on constant local currency.
World Bank Development Indicators, from World Bank and OECD
Gross domestic product (GDP) represents the sum of value added by all its producers plus any product taxes and minus any subsidies not included in the value of the products. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either 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 valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices.
An economy's growth is measured by the change in the volume of its output or in the real incomes of its residents. The 2008 United Nations System of National Accounts (2008 SNA) offers three plausible indicators for calculating growth: the volume of gross domestic product (GDP), real gross domestic income, and real gross national income. The volume of GDP is the sum of value added, measured at constant prices, by households, government, and industries operating in the economy. GDP accounts for all domestic production, regardless of whether the income accrues to domestic or foreign institutions. Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently 'netted out' using equally crude and ad hoc approximations. Data for OECD countries are based on ISIC, revision 4.
SDG Goal 8, indicator 8.1.1; ENP-South Eurostat Data Browser: Area ' Economy and Finance'
36
GDP per capita (constant 2015 US$)
Economy
Macroeconomics and Public Finance
Gross domestic product divided by midyear population.
World Bank Development Indicators, from World Bank and OECD
Gross domestic product (GDP) represents the sum of value added by all its producers plus any product taxes and minus any subsidies not included in the value of the products. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either 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 valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices.
An economy's growth is measured by the change in the volume of its output or in the real incomes of its residents. The 2008 United Nations System of National Accounts (2008 SNA) offers three plausible indicators for calculating growth: the volume of gross domestic product (GDP), real gross domestic income, and real gross national income. The volume of GDP is the sum of value added, measured at constant prices, by households, government, and industries operating in the economy. GDP accounts for all domestic production, regardless of whether the income accrues to domestic or foreign institutions. Among the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently 'netted out' using equally crude and ad hoc approximations. Data for OECD countries are based on ISIC, revision 4.
ENP-South Eurostat Data Browser: Area ' Economy and Finance'
37
GDP per capita, PPP (constant 2017 international $)
Economy
Macroeconomics and Public Finance
Gross domestic product converted to 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.
World Bank Development Indicators from World Bank International Comparison Program and Eurostat-OECD PPP Programme
PPP GDP is gross domestic product converted to 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 purchaser's prices is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.
There are still significant limitations on the availability of reliable data. Information on consistent series of output in both national currencies and purchasing power parity dollars is not easily available, especially in developing countries, because the definition, coverage, and methodology are not always consistent across countries. For example, countries employ different methodologies for estimating the missing values for the nonmarket service sectors and use different definitions of the informal sector. SDG indicator 8.2.1.
None
38
GDP per person employed (constant 2017 PPP $)
Economy
Macroeconomics and Public Finance
Gross domestic product (GDP) divided by total employment in the economy. Purchasing power parity (PPP) GDP is GDP converted to 2017 constant international dollars using PPP rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has in the United States.
World Bank Development Indicators, data from ILO, United Nations Population Division, Eurostat, OECD and World Bank
Estimates are based on employment, population, GDP and PPP data obtained from ILO, United Nations Population Division, Eurostat, OECD and World Bank.
For comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output in both national currencies and purchasing power parity dollars is not easily available, especially in developing countries, because the definition, coverage, and methodology are not always consistent across countries. For example, countries employ different methodologies for estimating the missing values for the nonmarket service sectors and use different definitions of the informal sector. SDG indicator 8.2.1.
None
39
Agriculture, forestry, and fishing, value added (% of total value added)
Economy
Macroeconomics and Public Finance
Agriculture, forestry, and fishing corresponds to ISIC divisions 1-3 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. 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 depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4.
WeMed estimates of data from World Bank Development Indicators
Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either 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 valuations exclude transport charges that are invoiced separately by producers.
Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently 'netted out' using equally crude and ad hoc approximations.Note: Data for OECD countries are based on ISIC, revision 4.
ENP-South Eurostat Data Browser: Area ' Economy and Finance'
40
Agriculture, forestry, and fishing, value added (annual % growth)
Economy
Macroeconomics and Public Finance
Annual growth rate for agricultural, forestry, and fishing value added based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. Agriculture corresponds to ISIC divisions 01-03 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. 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 depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4.
World Bank Development Indicators, from World Bank and OECD
Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either 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 valuations exclude transport charges that are invoiced separately by producers.
Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently 'netted out' using equally crude and ad hoc approximations.Note: Data for OECD countries are based on ISIC, revision 4.
None
41
Industry (including construction), value added (% of total value added)
Economy
Macroeconomics and Public Finance
Industry (including construction) corresponds to ISIC divisions 05-43 and includes manufacturing (ISIC divisions 10-33). It comprises value added in 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 making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4.
WeMed estimates of data from World Bank Development Indicators
Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either 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 valuations exclude transport charges that are invoiced separately by producers.
Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms. Data for OECD countries are based on ISIC, revision 4.
ENP-South Eurostat Data Browser: Area ' Economy and Finance'
42
Industry (including construction), value added (annual % growth)
Economy
Macroeconomics and Public Finance
Annual growth rate for industrial (including construction) value added based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. Industry corresponds to ISIC divisions 05-43 and includes manufacturing (ISIC divisions 10-33). It comprises value added in 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 making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4.
World Bank Development Indicators, from World Bank and OECD
Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either 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 valuations exclude transport charges that are invoiced separately by producers.
Ideally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms. Data for OECD countries are based on ISIC, revision 4.
None
43
Medium and high-tech manufacturing value added (% manufacturing value added)
Economy
Macroeconomics and Public Finance
Proportion of medium and high-tech industry value added in total value added of manufacturing
World Bank Development Indicators, from United Nations Industrial Development Organization (UNIDO)
Data are collected using General Industrial Statistics Questionnaire which is filled by NSOs and submitted to UNIDO annually. Data for OECD countries are obtained directly from OECD. Country data are also collected from official publications and official web-sites. Missing values at country level are imputed based on the methodology from Competitive Industrial Performance Report (UNIDO, 2017.
Conversion to USD or difference in ISIC combinations may cause discrepancy between national and international figures.
SDG Goal 9, indicator 9.b.1
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Services, value added (% of total value added)
Economy
Macroeconomics and Public Finance
Services correspond to ISIC divisions 45-99 and they include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. 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 depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4.
WeMed estimates of data from World Bank Development Indicators
Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either 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 valuations exclude transport charges that are invoiced separately by producers.
In the services industry the many self-employed workers and one-person businesses are sometimes difficult to locate, and they have little incentive to respond to surveys, let alone to report their full earnings. Compounding these problems are the many forms of economic activity that go unrecorded, including the work that women and children do for little or no pay.
ENP-South Eurostat Data Browser: Area ' Economy and Finance'
45
Services, value added (annual % growth)
Economy
Macroeconomics and Public Finance
Annual growth rate for value added in services based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. Services correspond to ISIC divisions 45-99. They include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. 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 depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4.
World Bank Development Indicators, from World Bank and OECD
Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either 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 valuations exclude transport charges that are invoiced separately by producers.
In the services industry the many self-employed workers and one-person businesses are sometimes difficult to locate, and they have little incentive to respond to surveys, let alone to report their full earnings. Compounding these problems are the many forms of economic activity that go unrecorded, including the work that women and children do for little or no pay.
None
46
General government final consumption expenditure (annual % growth)
Economy
Macroeconomics and Public Finance
Annual percentage growth of general government final consumption expenditure based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. General government final consumption expenditure (general government consumption) includes all government current expenditures for purchases of goods and services (including compensation of employees). It also includes most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation.
World Bank Development Indicators, from World Bank and OECD
Gross domestic product (GDP) represents the sum of value added by all its producers plus any product taxes and minus any subsidies not included in the value of the products. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either 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 valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices.
Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total. Measures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved). To obtain government consumption in constant prices, countries may deflate current values by applying a wage (price) index or extrapolate from the change in government employment. Neither technique captures improvements in productivity or changes in the quality of government services.
None
47
Household and NPISHs Final consumption expenditure (annual % growth)
Economy
Macroeconomics and Public Finance
Annual percentage growth of household and NPISHs final consumption expenditure based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. Household and NPISHs final consumption expenditure (formerly private consumption) is the market value of all goods and services, including durable products (such as cars, washing machines, and home computers), purchased by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also includes payments and fees to governments to obtain permits and licenses. This indicator includes the expenditures of nonprofit institutions serving households even when reported separately by the country.
World Bank Development Indicators, from World Bank and OECD
Gross domestic product (GDP) represents the sum of value added by all its producers plus any product taxes and minus any subsidies not included in the value of the products. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either 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 valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices.
Because policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total. Measures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved).
None
48
Social contributions (% of revenue)
Economy
Macroeconomics and Public Finance
Social contributions include social security contributions by employees, employers, and self-employed individuals, and other contributions whose source cannot be determined. They also include actual or imputed contributions to social insurance schemes operated by governments.
a) International Monetary Fund; b) World Bank Development Indicators for Lebanon
Data on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks. Government finance statistics are reported in local currency. Many countries report government finance data by fiscal year.
For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.
None
49
Tax revenue (% of GDP)
Economy
Macroeconomics and Public Finance
Tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social security contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue.
a) International Monetary Fund; b) World Bank Development Indicators for Lebanon, Jordan, West Bank and Gaza, Tunisia, Morocco
Data on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks. Government finance statistics are reported in local currency. Many countries report government finance data by fiscal year.
For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.
SDG Goal 17, indicator 17.1.1
50
Taxes on goods and services (% of revenue)
Economy
Macroeconomics and Public Finance
Taxes on goods and services include general sales and turnover or value added taxes, selective excises on goods, selective taxes on services, taxes on the use of goods or property, taxes on extraction and production of minerals, and profits of fiscal monopolies.
a) International Monetary Fund; b) World Bank Development Indicators for Lebanon, Jordan, West Bank and Gaza
Data on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks. Government finance statistics are reported in local currency. Many countries report government finance data by fiscal year.
For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.
None
51
Current government expenditure on goods and services (% of GDP)
Economy
Macroeconomics and Public Finance
Cash payments for operating activities of the government in providing goods and services. It includes compensation of employees (such as wages and salaries), interest and subsidies, grants, social benefits, and other expenses such as rent and dividends.
a) International Monetary Fund; b) World Bank Development Indicators for Lebanon, Jordan, West Bank and Gaza, Morocco
Data on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). The IMF's Government Finance Statistics Manual 2014, harmonized with the 2008 SNA, recommends an accrual accounting method, focusing on all economic events affecting assets, liabilities, revenues, and expenses, not just those represented by cash transactions. It accounts for all changes in stocks, so stock data at the end of an accounting period equal stock data at the beginning of the period plus flows over the period. The 1986 manual considered only debt stocks. Government finance statistics are reported in local currency. Many countries report government finance data by fiscal year.
For most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.
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 provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments.
World Bank Development Indicators, from World Bank and OECD
Gross domestic product (GDP) from the expenditure side is made up 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. Such expenditures are recorded in purchaser prices and include net taxes on products.
Data on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics.
ENP-South Eurostat Data Browser: Area 'International trade in goods'
53
Exports of goods and services (annual % growth)
Economy
International Relations
Annual growth rate of exports of goods and services based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. Exports of goods and services represent the value of all goods and other market services provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments.
World Bank Development Indicators, from World Bank and OECD
Gross domestic product (GDP) from the expenditure side is made up 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. Such expenditures are recorded in purchaser prices and include net taxes on products.
Data on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics.
None
54
Exports of goods and services (US$ billion, current values)
Economy
International Relations
Exports of goods and services represent the value of all goods and other market services provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments. Data are in current U.S. dollars.
World Bank Development Indicators, from World Bank and OECD
Gross domestic product (GDP) from the expenditure side is made up 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. Such expenditures are recorded in purchaser prices and include net taxes on products.
Data on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics.
ENP-South Eurostat Data Browser: Area 'International trade in goods'
55
Merchandise exports (US$ billion, current values)
Economy
International Relations
F.o.b. value of goods provided to the rest of the world.
a) World Trade Organization; b) World Bank Development Indicators for Serbia
Merchandise trade data are from customs reports of goods moving into or out of an economy or from reports of financial transactions related to merchandise trade recorded in the balance of payments.
Exports are recorded as the cost of the goods delivered to the frontier of the exporting country for shipment: free on board (FOB) values. Because of differences in timing and definitions, trade flow estimates from customs reports and balance of payments may differ. Several international agencies process trade data, each correcting unreported or misreported data, leading to other differences. Countries may report trade according to the general or special system of trade. Under the general system exports comprise outward-moving goods that are (a) goods wholly or partly produced in the country; (b) foreign goods, neither transformed nor declared for domestic consumption in the country, that move outward from customs storage; and (c) goods previously included as imports for domestic consumption but subsequently exported without transformation. Under the special system exports comprise categories a and c. In some compilations categories b and c are classified as re-exports. Because of differences in reporting practices, data on exports may not be fully comparable across economies. Data on exports of goods are derived from the same sources as data on imports. In principle, world exports and imports should be identical. Similarly, exports from an economy should equal the sum of imports by the rest of the world from that economy. But differences in timing and definitions result in discrepancies in reported values at all levels.
None
56
Agricultural raw materials exports (% of merchandise exports)
Economy
International Relations
Agricultural raw materials comprise SITC section 2 (crude materials except fuels) excluding divisions 22, 27 (crude fertilizers and minerals excluding coal, petroleum, and precious stones), and 28 (metalliferous ores and scrap).
a) UNCTAD; b) World Bank Development Indicators for Spain and Montenegro
Merchandise trade statistics consists of data as reported to the UN Comtrade and estimated data for missing reporters. When not reported, statistics on the total merchandise imports and exports of countries (or areas) are mainly derived from the International Financial Statistics (IFS) published monthly by the International Monetary Fund (IMF). They are supplemented with statistics from other sources such as national publications and websites and the United Nations Monthly Bulletin of Statistics Questionnaire.
Statistics by commodity for missing reporters are estimated either through the extrapolation of the statistics for the two adjacent years, or, if this is not possible, through the use of the statistics reported by the trading partners; i.e., mirror statistics. Mirror statistics are also used in cases in which the reported data must be adjusted due to partner distribution or confidential data. All estimates are reviewed and adjusted where necessary.The classification of commodity groups is based on the Standard International Trade Classification (SITC) revision 3.
None
57
Food exports (% of merchandise exports)
Economy
International Relations
Food comprises the commodities in SITC sections 0 (food and live animals), 1 (beverages and tobacco), and 4 (animal and vegetable oils and fats) and SITC division 22 (oil seeds, oil nuts, and oil kernels).
a) UNCTAD; b) World Bank Development Indicators for Spain and Montenegro
Merchandise trade statistics consists of data as reported to the UN Comtrade and estimated data for missing reporters. When not reported, statistics on the total merchandise imports and exports of countries (or areas) are mainly derived from the International Financial Statistics (IFS) published monthly by the International Monetary Fund (IMF). They are supplemented with statistics from other sources such as national publications and websites and the United Nations Monthly Bulletin of Statistics Questionnaire.
Statistics by commodity for missing reporters are estimated either through the extrapolation of the statistics for the two adjacent years, or, if this is not possible, through the use of the statistics reported by the trading partners; i.e., mirror statistics. Mirror statistics are also used in cases in which the reported data must be adjusted due to partner distribution or confidential data. All estimates are reviewed and adjusted where necessary.The classification of commodity groups is based on the Standard International Trade Classification (SITC) revision 3.
None
58
Fuel exports (% of merchandise exports)
Economy
International Relations
Fuels comprise the commodities in SITC section 3 (mineral fuels, lubricants and related materials).
a) UNCTAD; b) World Bank Development Indicators for Spain and Montenegro
Merchandise trade statistics consists of data as reported to the UN Comtrade and estimated data for missing reporters. When not reported, statistics on the total merchandise imports and exports of countries (or areas) are mainly derived from the International Financial Statistics (IFS) published monthly by the International Monetary Fund (IMF). They are supplemented with statistics from other sources such as national publications and websites and the United Nations Monthly Bulletin of Statistics Questionnaire.
Statistics by commodity for missing reporters are estimated either through the extrapolation of the statistics for the two adjacent years, or, if this is not possible, through the use of the statistics reported by the trading partners; i.e., mirror statistics. Mirror statistics are also used in cases in which the reported data must be adjusted due to partner distribution or confidential data. All estimates are reviewed and adjusted where necessary.The classification of commodity groups is based on the Standard International Trade Classification (SITC) revision 3.
None
59
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 merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments.
World Bank Development Indicators, from World Bank and OECD
Gross domestic product (GDP) from the expenditure side is made up 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. Such expenditures are recorded in purchaser prices and include net taxes on products.
Data on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics.
None
60
Imports of goods and services (annual % growth)
Economy
International Relations
Annual growth rate of imports of goods and services based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars.
World Bank Development Indicators, from World Bank and OECD
Gross domestic product (GDP) from the expenditure side is made up 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. Such expenditures are recorded in purchaser prices and include net taxes on products.
Data on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics.
None
61
Imports of goods and services (US$ billion, current values)
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 merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments. Data are in current U.S. dollars.
World Bank Development Indicators, from World Bank and OECD
Gross domestic product (GDP) from the expenditure side is made up 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. Such expenditures are recorded in purchaser prices and include net taxes on products.
Data on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics.
None
62
Merchandise imports (US$ billion, current values)
Economy
International Relations
C.i.f. value of goods received from the rest of the world.
a) World Trade Organization; b) World Bank Development Indicators for Serbia
Merchandise trade data are from customs reports of goods moving into or out of an economy or from reports of financial transactions related to merchandise trade recorded in the balance of payments.
The value of imports is generally recorded as the cost of the goods when purchased by the importer plus the cost of transport and insurance to the frontier of the importing country - the cost, insurance, and freight (c.i.f.) value, corresponding to the landed cost at the point of entry of foreign goods into the country. A few countries collect import data on a free on board (f.o.b.) basis and adjust them for freight and insurance costs. Because of differences in timing and definitions, trade flow estimates from customs reports and balance of payments may differ. Several international agencies process trade data, each correcting unreported or misreported data, leading to other differences. Countries may report trade according to the general or special system of trade. Under the general system imports include goods imported for domestic consumption and imports into bonded warehouses and free trade zones. Under the special system imports comprise goods imported for domestic consumption (including transformation and repair) and withdrawals for domestic consumption from bonded warehouses and free trade zones. Goods transported through a country en route to another are excluded. Data on imports of goods are derived from the same sources as data on exports. In principle, world exports and imports should be identical. Similarly, exports from an economy should equal the sum of imports by the rest of the world from that economy. But differences in timing and definitions result in discrepancies in reported values at all levels.
None
63
Agricultural raw materials imports (% of merchandise imports)
Economy
International Relations
Agricultural raw materials comprise SITC section 2 (crude materials except fuels) excluding divisions 22, 27 (crude fertilizers and minerals excluding coal, petroleum, and precious stones), and 28 (metalliferous ores and scrap).
a) UNCTAD; b) World Bank Development Indicators for Spain and Montenegro
Merchandise trade statistics consists of data as reported to the UN Comtrade and estimated data for missing reporters. When not reported, statistics on the total merchandise imports and exports of countries (or areas) are mainly derived from the International Financial Statistics (IFS) published monthly by the International Monetary Fund (IMF). They are supplemented with statistics from other sources such as national publications and websites and the United Nations Monthly Bulletin of Statistics Questionnaire.
Statistics by commodity for missing reporters are estimated either through the extrapolation of the statistics for the two adjacent years, or, if this is not possible, through the use of the statistics reported by the trading partners; i.e., mirror statistics. Mirror statistics are also used in cases in which the reported data must be adjusted due to partner distribution or confidential data. All estimates are reviewed and adjusted where necessary.The classification of commodity groups is based on the Standard International Trade Classification (SITC) revision 3.
None
64
Food imports (% of merchandise imports)
Economy
International Relations
Food comprises the commodities in SITC sections 0 (food and live animals), 1 (beverages and tobacco), and 4 (animal and vegetable oils and fats) and SITC division 22 (oil seeds, oil nuts, and oil kernels).
a) UNCTAD; b) World Bank Development Indicators for Spain and Montenegro
Merchandise trade statistics consists of data as reported to the UN Comtrade and estimated data for missing reporters. When not reported, statistics on the total merchandise imports and exports of countries (or areas) are mainly derived from the International Financial Statistics (IFS) published monthly by the International Monetary Fund (IMF). They are supplemented with statistics from other sources such as national publications and websites and the United Nations Monthly Bulletin of Statistics Questionnaire.
Statistics by commodity for missing reporters are estimated either through the extrapolation of the statistics for the two adjacent years, or, if this is not possible, through the use of the statistics reported by the trading partners; i.e., mirror statistics. Mirror statistics are also used in cases in which the reported data must be adjusted due to partner distribution or confidential data. All estimates are reviewed and adjusted where necessary.The classification of commodity groups is based on the Standard International Trade Classification (SITC) revision 3.
None
65
Fuel imports (% of merchandise imports)
Economy
International Relations
Fuels comprise the commodities in SITC section 3 (mineral fuels, lubricants and related materials).
a) UNCTAD; b) World Bank Development Indicators for Spain and Montenegro
Merchandise trade statistics consists of data as reported to the UN Comtrade and estimated data for missing reporters. When not reported, statistics on the total merchandise imports and exports of countries (or areas) are mainly derived from the International Financial Statistics (IFS) published monthly by the International Monetary Fund (IMF). They are supplemented with statistics from other sources such as national publications and websites and the United Nations Monthly Bulletin of Statistics Questionnaire.
Statistics by commodity for missing reporters are estimated either through the extrapolation of the statistics for the two adjacent years, or, if this is not possible, through the use of the statistics reported by the trading partners; i.e., mirror statistics. Mirror statistics are also used in cases in which the reported data must be adjusted due to partner distribution or confidential data. All estimates are reviewed and adjusted where necessary.The classification of commodity groups is based on the Standard International Trade Classification (SITC) revision 3.
None
66
Foreign direct investment, net inflows (% of GDP)
Economy
International Relations
Net inflows of investment are new investment inflows less disinvestment in the reporting economy from foreign investors, in order to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments.
a) International Monetary Fund; b) World Bank Development Indicators for Kosovo, Bosnia and Herzegovina, Montenegro, Syria, Lebanon, Jordan, West Bank and Gaza, Egypt, Libya, Tunisia, Algeria
Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Equity flows comprise foreign direct investment (FDI) and portfolio equity. Foreign direct investment (FDI) data are supplemented by the 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 (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity 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 because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated. Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6).
ENP-South Eurostat Data Browser: Area 'Tourism'
67
Foreign direct investment, net outflows (% of GDP)
Economy
International Relations
Net outflows of investment are new investment outflows less disinvestment from the reporting economy to the rest of the world, in order to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments.
a) International Monetary Fund; b) World Bank Development Indicators for Kosovo, Bosnia and Herzegovina, Montenegro, Lebanon, Jordan, West Bank and Gaza, Egypt, Libya, Tunisia, Algeria
Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Equity flows comprise foreign direct investment (FDI) and portfolio equity. Foreign direct investment (FDI) data are supplemented by the 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 (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. FDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity 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 because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated. Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6).
ENP-South Eurostat Data Browser: Area ' Economy and Finance'
68
International tourism, number of arrivals (thousands)
Economy
Other Economic Issues
International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they have their usual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead.
a) UN Tourism; b) World Bank Development Indicators for Portugal, Slovenia, Serbia, Bosnia and Herzegovina, Montenegro, North Macedonia, Lebanon, West Bank and Gaza, Tunisia
Data are obtained from different sources: administrative records (immigration, traffic counts, and other possible types of controls), border surveys or a mix of them. If data are obtained from accommodation surveys, the number of guests is used as estimate of arrival figures; consequently, in this case, breakdowns by regions ,main purpose of the trip, modes of transport used or forms oforganization of the trip are based on complementary visitor surveys.
For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries. The data on inbound tourists refer to the number of arrivals, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival.
ENP-South Eurostat Data Browser: Area ' Economy and Finance'
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International tourism, number of departures (thousands)
Economy
Other Economic Issues
International outbound tourists are the number of departures that people make from their country of usual residence to any other country for any purpose other than a remunerated activity in the country visited. The data on outbound tourists does not refer to the number of people traveling; thus a person who makes several trips from a country during a given period is counted each time as a new departure.
a) UN Tourism for Spain, France, Italy, Slovenia, Croatia, Malta, Cyprus, Albania; b) World Bank Development Indicators from World Tourism Organisation for other countries
Data are collected using one of three methods, or a combination of these to determine the flows of outbound visitors: using an entry/departure card; a specific survey at the border, or observing them from household surveys because they belong to resident households. In the latter case, the information on outbound trips is usually collected at the same time as that on domestic trips.
The data refer to international tourism, where the traveler's country of residence differs from the visiting country. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people travelling.
ENP-South Eurostat Data Browser: Area 'Tourism'
70
High-technology exports (% of manufactured exports)
Economy
Other Economic Issues
Exports of products with high R&D intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery, as a percentage of manifactured exports.
a) UNCTAD; b) World Bank Development Indicators for Spain and Montenegro
The methodology for determining high-technology exports was developed by the Organisation for Economic Co-operation and Development in collaboration with Eurostat. It takes a 'product approach' (rather than a 'sectoral approach') based on R&D intensity (expenditure divided by total sales) for groups of products from Germany, Italy, Japan, the Netherlands, Sweden, and the United States. The original high-tech products classification is based on SITC Rev. 3 and is taken from Table 4 of Annex 2 of the 1997 working paper of Thomas Hatzichronouglou, OECD. It is based on the importance of expenditures on research and development relative to the gross output and value added of different types of industries that produce goods for export. It is a four-way classification of exports: high, medium-high, medium-low and low-technology. Examples of high-technology industries are aircraft, computers, and pharmaceuticals; medium-high-technology includes motor vehicles, electrical equipment and most chemicals; medium-low-technology includes rubber, plastics, basic metals and ship construction; low-technology industries include food processing, textiles, clothing and footwear.
Because industrial sectors specializing in a few high-technology products may also produce low-technology products, the product approach is more appropriate for international trade. The method takes only R&D intensity into account, but other characteristics of high technology are also important, such as knowhow, scientific personnel, and technology embodied in patents. Considering these characteristics would yield a different list (see Hatzichronoglou 1997).
None
71
ICT goods exports (% of total goods exports)
Economy
Other Economic Issues
Information and communication technology goods imports - including computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous) - as a percentage of goods imports.
UNCTAD
Data are reported by national authorities with product details coded according to the World Customs Organization (WCO) Harmonized Commodity Description and Coding System (HS) 1992, HS 1996, HS 2002, HS 2007, HS 2012, HS 2017 or HS 2022, depending on the individual reporting economy and year. In cases where multiple HS classifications are available for a given economy and year, preference is given to the latest HS edition. The data are downloaded from UN Comtrade and aggregated into ICT product groups by UNCTAD.
None
None
72
ICT goods imports (% total goods imports)
Economy
Other Economic Issues
Information and communication technology goods exports - including computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous) - as a percentage of goods exports.
UNCTAD
Data are reported by national authorities with product details coded according to the World Customs Organization (WCO) Harmonized Commodity Description and Coding System (HS) 1992, HS 1996, HS 2002, HS 2007, HS 2012, HS 2017 or HS 2022, depending on the individual reporting economy and year. In cases where multiple HS classifications are available for a given economy and year, preference is given to the latest HS edition. The data are downloaded from UN Comtrade and aggregated into ICT product groups by UNCTAD.
None
None
73
Patent applications, residents and non residents (per 1.000,000 inhabitants)
Economy
Other Economic Issues
Worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention: a product or process that provides a new way of doing something or offers a new technical solution to a problem. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.
WeMed estimates from World Intellectual Property Organization (WIPO)
Intellectual property (IP) data are based primarily on WIPO’s annual IP statistics surveys and on data compiled by WIPO in processing international applications/registrations through the Patent Cooperation Treaty (PCT) and the Madrid and Hague Systems. WIPO’s long-established and regular IP survey covers patents, utility models, trademarks, industrial designs and plant varieties. It consists of 27 questionnaires, all of which are available in Arabic, Chinese, English, French, Russian and Spanish at www.wipo.int/ipstats/en/data_collection/questionnaire. In 2017, WIPO started to collect data on geographical indications) through an annual survey. This simple questionnaire seeks to collect data on geographical indications in force broken down by legal means of protection (e.g., sui generis systems, trademarks, international agreements, and so on) and products types (e.g., wines and spirits, agricultural products, and so on).
Continuous efforts are being made to improve the quality and availability of IP statistics and to gather data from as many IP offices and countries as possible.
None
74
Commercial bank branches (per 100,000 adults)
Economy
Other Economic Issues
Ratio (per 100 persons ages 15 and older) of retail locations of resident commercial banks and other resident banks that function as commercial banks that provide financial services to customers and are physically separated from the main office but not organized as legally separated subsidiaries.
International Monetary Fund
Data are collected by the Financial Access Survey (FAS), based on administrative data collected by central banks and other financial regulators as reported to the IMF. The Financial Access Survey (FAS), launched in 2009, is a supply-side dataset on access to and use of financial services aimed at supporting policymakers to measure and monitor financial inclusion and benchmark progress against peers.
The banking sector includes monetary authorities (the central bank) and deposit money banks, as well as other financial corporations where data are available (including institutions that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies.
None
75
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, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises.
a) International Monetary Fund; b) World Bank Development Indicators for Cyprus
Data are collected by the Financial Access Survey (FAS), based on administrative data collected by central banks and other financial regulators as reported to the IMF. The Financial Access Survey (FAS), launched in 2009, is a supply-side dataset on access to and use of financial services aimed at supporting policymakers to measure and monitor financial inclusion and benchmark progress against peers.
The financial corporations include monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies.
None
76
Domestic credit to private sector by banks (% of GDP)
Economy
Other Economic Issues
Percentage of GDP of domestic credit to private sector by banks refers to financial resources provided to the private sector by other depository corporations (deposit taking corporations except central banks), such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises.
a) International Monetary Fund; b) World Bank Development Indicators for Cyprus and Lebanon
Data are collected by the Financial Access Survey (FAS), based on administrative data collected by central banks and other financial regulators as reported to the IMF. The Financial Access Survey (FAS), launched in 2009, is a supply-side dataset on access to and use of financial services aimed at supporting policymakers to measure and monitor financial inclusion and benchmark progress against peers.
The banking sector includes monetary authorities (the central bank) and deposit money banks, as well as other financial corporations where data are available (including institutions that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies.
None
77
Air transport, freight (million ton-km)
Environment and Natural Resources
Infrastructure, Transport and Energy
Volume of freight, express, and diplomatic bags carried on each flight stage (operation of an aircraft from takeoff to its next landing), measured in metric tons times kilometers traveled.
World Bank Development Indicators from International Civil Aviation Organization
The air transport data represent the total (international and domestic) scheduled traffic carried by the air carriers registered in a country. Countries submit air transport data to International Civil Aviation Organization (ICAO) on the basis of standard instructions and definitions issued by ICAO. In many cases, however, the data include estimates by ICAO for nonreporting carriers. Where possible, these estimates are based on previous submissions supplemented by information published by the air carriers, such as flight schedules.
None
ENP-South Eurostat Data Browser: Area ' Transport'
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Air transport, passengers carried
Environment and Natural Resources
Infrastructure, Transport and Energy
Domestic and international aircraft passengers of air carriers registered in the country.
World Bank Development Indicators from International Civil Aviation Organization
The air transport data represent the total (international and domestic) scheduled traffic carried by the air carriers registered in a country. Countries submit air transport data to International Civil Aviation Organization (ICAO) on the basis of standard instructions and definitions issued by ICAO. In many cases, however, the data include estimates by ICAO for nonreporting carriers. Where possible, these estimates are based on previous submissions supplemented by information published by the air carriers, such as flight schedules.
The data cover the 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 nonscheduled. Thus recent increases shown for 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 shown separately as domestic and all other traffic as international. 'Foreign' cabotage traffic (i.e. traffic carried between city-pairs in a State other than the one where the reporting carrier has its principal place of business) is shown as international traffic. A technical stop does not result in any flight stage being classified differently than would have been the case had the technical stop not been made. For countries with few air carriers or only one, the addition or discontinuation of a home-based air carrier may cause significant changes in air traffic. Data for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been 'internationalized.'
ENP-South Eurostat Data Browser: Area ' Transport'
79
Liner shipping connectivity index (medium value in 2023.I = 100)
Environment and Natural Resources
Infrastructure, Transport and Energy
Composite index which measures an economy’s position within global liner shipping networks. It is calculated from the number of ship calls, the container handling capacity of ports, the number of services and companies, the size of the largest ship, and the number of countries connected through direct liner shipping services. For each year, the value of the fourth quarter is considered.
UNCTAD
The composite index is calculated from the number of ship calls, the container handling capacity of ports, the number of regular liner services, the number of companies, the size of the largest ship, and the number of countries connected through direct liner shipping services. For each component, the country’s value is divided by the average value for the component in Q1 2023 and then is calculated the average of the six components for the country. The average across components for a given country and quarter is then multiplied by 100. The result is an average LSCI of 100 in Q1 2023. This scaling was applied in March 2024 for the whole series (from Q1 2006). This is a change from the previous calculation methodology where components and index were scaled to the maximum value in Q1 2006.
None
None
80
Logistics performance index: Overall (1=low to 5=high)
Environment and Natural Resources
Infrastructure, Transport and Energy
Composite index which measures perceptions of a country's logistics based on the efficiency of customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance.
World Bank
The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistic performance index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions. The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators.
None
None
81
Renewable energy consumption (% of total final energy consumption)
Environment and Natural Resources
Infrastructure, Transport and Energy
Share of renewables energy in total final energy consumption.Renewable energy includes hydro, solid biofuels, liquid biofuels, biogases, wind, solar, geothermal, tide/wave/oceans 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'
Data are derived from the International Energy Agency (IEA) World energy balances, with additional information at https://www.iea.org/reports/sdg7-data-and-projections; and from the United Nations Energy Statistics Database (http://data.un.org/Explorer.aspx?d=EDATA); both providing a breakdown of national energy flows by products over time. The indicator is calculated as the ratio of final energy consumption from renewables after allocation of electricity and heat (AFECREN) to total final energy consumption (TFEC), calculated from the flows in the energy balances. Final consumption of electricity and heat is allocated to renewables based on the share of the gross generation coming from renewable sources. In practice, this occurs by calculating the percentage of electricity and heat produced by each renewable source, multiplying the final energy consumption of electricity and heat by those shares, and then allocating the resulting quantities to each renewable energy source’s final consumption. For instance, if total final consumption table reports 150 TJ for biogas energy, while total final consumption of electricity is 400 TJ and heat 100 TJ, and the share of biogas in total electricity output is 10 percent and 5 percent in heat, the total reported number for biogas consumption will be 195 TJ (150 TJ+400TJ*10%+100TJ*5%). As a result of this method, it is implicitly assumed that energy losses between energy supplied and energy consumed are proportional to their shares in production across all technologies. Total final energy consumption represents the sum of the energy from all sources used in the industry, transport, buildings, and other sectors of final consumption. It is derived from the national energy balance and excludes quantities used for non-energy purposes.
None
SDG Goal 7, indicator 7.2.1
82
Surface area (sq. km)
Environment and Natural Resources
Environment and Territory
Country's total area, including areas under inland bodies of water 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
Data from FAO are collected through a Questionnaire on Land Use, Irrigation and Agricultural Practices, based on the FAO Land Use classification. In other cases, data are drawn by national statistical offices.
Changes in the total area of a country from one year to the next may be due to updating or revision of data rather than an actual change.
ENP-South Eurostat Data Browser: Area 'Environment and Energy'
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Forest area (% of land area)
Environment and Natural Resources
Environment and Territory
Percentage over the land area of the forest area, that is land under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, excluding tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens.
a) FAO; b) World Bank Development Indicators for Egypt, Libya, Algeria
Data are collected through a FAO Questionnaire on Land Use, Irrigation and Agricultural Practices, based on the FAO Land Use classification.
The FAO Land Use classification is aligned with the UN System of Environmental and Economic Accounting (SEEA); the UN Framework for the Development of Environmental Statistics (FDES); and the World Census of Agriculture. It is furthermore consistent with the land use classes of the Intergovernmental Panel on Climate Change for country reporting to the UN Framework Convention on Climate Change (UNFCCC). A mapping between 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: Area 'Environment and Energy'
84
Rural population (% of total population)
Environment and Natural Resources
Environment and Territory
Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.
World Bank Development Indicators from United Nations Population Division
Rural population is calculated as the difference between the total population and the urban population(midyear nonurban population). The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data.
Aggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries. Because the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population.
None
85
Population in the largest city (% of urban population)
Environment and Natural Resources
Environment and Territory
Percentage of a country's urban population living in that country's largest metropolitan area.
World Bank Development Indicators from United Nations Population Division
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 urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data.
A metropolitan area includes the urban area, and its satellite cities plus intervening rural land that is socio-economically connected to the urban core city, typically by employment ties through commuting, with the urban core city being the primary labor market. According to the United Nations' definition, a metropolitan area includes both the 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 transport, road linkages, commuting facilities etc.). Because the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Countries differ in the way they classify population as 'urban' or 'rural.' The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage.
None
86
Urban population (% of total population)
Environment and Natural Resources
Environment and Territory
Urban population refers to people living in urban areas as defined by national statistical offices.
a) United Nations Population Division; b) World Bank Development Indicators for West Bank and Gaza
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 urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data.
Most countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. Because the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Countries differ in the way they classify population as 'urban' or 'rural.' The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage.
None
87
Marine protected areas (% of territorial waters)
Environment and Natural Resources
Environment and Territory
Percentage over the territorial waters of marine protected areas, that are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--reserved by law or other effective means to protect part or all of the enclosed environment.
World Bank Development Indicators from United Nations Environment World Conservation Monitoring Centre (UNEP-WCMC)
This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon). A GIS analysis is used to calculate terrestrial and marine protection.
The International Union for Conservation of Nature (IUCN) defines a protected area as 'a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values'. Designating an area as protected does not mean that protection is in force. And for small countries that have only protected areas smaller than 1,000 hectares, the size limit in the definition leads to an underestimate of protected areas. Nationally protected areas are defined using the six IUCN management categories for areas of at least 1,000 hectares: scientific reserves and strict nature reserves with limited public access; national parks of national or international significance 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); and areas managed mainly for the sustainable use of natural systems to ensure long-term protection and maintenance of biological diversity.
SDG Goal 14, indicator 14.5.1
88
Terrestrial protected areas (% of total land area)
Environment and Natural Resources
Environment and Territory
Percentage over the total land area of terrestrial protected areas, that are areas totally or partially protected areas of at least 1,000 hectares that are 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 mainly for sustainable use. Marine areas, unclassified areas, littoral (intertidal) areas, and sites protected under local or provincial law are excluded.
World Bank Development Indicators from United Nations Environment World Conservation Monitoring Centre (UNEP-WCMC)
This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon). A GIS analysis is used to calculate terrestrial and marine protection.
The International Union for Conservation of Nature (IUCN) defines a protected area as 'a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values'. Designating an area as protected does not mean that protection is in force. And for small countries that have only protected areas smaller than 1,000 hectares, the size limit in the definition leads to an underestimate of protected areas. Nationally protected areas are defined using the six IUCN management categories for areas of at least 1,000 hectares: scientific reserves and strict nature reserves with limited public access; national parks of national or international significance 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); and areas managed mainly for the sustainable use of natural systems to ensure long-term protection and maintenance of biological diversity.
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89
Annual freshwater withdrawals, total (billion cubic meters)
Environment and Natural Resources
Environment and Territory
Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source.
FAO
The data are 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). The data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater.
Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. The data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall. Caution should also be used in 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 precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.
None
90
Level of water stress: freshwater withdrawal as a proportion of available freshwater resources
Environment and Natural Resources
Environment and Territory
Ratio between total freshwater withdrawn by all major sectors and total renewable freshwater resources, after taking into account environmental water requirements. Main sectors, as defined by ISIC standards, include agriculture; forestry and fishing; manufacturing; electricity industry; and services. This indicator is also known as water withdrawal intensity.
FAO
Total freshwater withdrawal is the volume of freshwater extracted from its source (rivers, lakes, aquifers) for agriculture, industries and municipalities. It is estimated at the country level for the following three main sectors: agriculture, municipalities (including domestic water withdrawal) and industries. Freshwater withdrawal includes primary freshwater (not withdrawn before), secondary freshwater (previously withdrawn and returned to rivers and groundwater, such as discharged wastewater and agricultural drainage water) and fossil groundwater. It does not include non-conventional water, i.e. direct use of treated wastewater, direct use of agricultural drainage water and desalinated water. Total freshwater withdrawal is in general calculated as being the sum of total water withdrawal by sector minus direct use of wastewater, direct use of agricultural drainage water and use of desalinated water. The total actual renewable water resources for a country or region are defined as the sum of internal renewable water resources and the external renewable water resources, also expressed in km3/year. The indicator is computed by dividing total water withdrawal by total actual renewable water resources minus environmental requirements and expressed in percentage points. Total renewable freshwater resources are expressed as the sum of internal and external renewable water resources. The terms “water resources” and “water withdrawal” are understood here as freshwater resources and freshwater withdrawal. Internal renewable water resources are defined as the long-term average annual flow of rivers and recharge of groundwater for a given country generated from endogenous precipitation. External renewable water resources refer to the flows of water entering the country, taking into consideration the quantity of flows reserved to upstream and downstream countries through agreements or treaties. Environmental water requirements (Env.) are the quantities of water required to sustain freshwater and estuarine ecosystems. Water quality and also the resulting ecosystem services are excluded from this formulation which is confined to water volumes. This does not imply that quality and the support to societies which are dependent on environmental flows are not important and should not be taken care of. Methods of computation of Env. are extremely variable and range from global estimates to comprehensive assessments for river reaches. Water volumes can be expressed in the same units as the total freshwater withdrawal, and then as percentages of the available water resources.
Water withdrawal 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 the issues related to sustainable water management. Supplementary indicators that capture the multiple dimensions of water management would combine data on water demand management, behavioural changes with regard to water use and the availability of appropriate infrastructure, and measure progress in increasing the efficiency and sustainability of water use, in particular in relation to population and economic growth. They would also recognize the different climatic environments that affect water use in countries, in particular in agriculture, which is the main user of water. Sustainability assessment is also linked to the ritical thresholds fixed for this indicator and there is no universal consensus on such threshold. Trends in water withdrawal show relatively slow patterns of change. Usually, three-five years are a minimum frequency to be able to detect significant changes, as it is unlikely that the indicator would show meaningful variations from one year to the other. Estimation of water withdrawal by sector is the main limitation to the computation of the indicator. Few countries actually publish water use data on a regular basis by sector. Renewable water resources include all surface water and groundwater resources that are available on a yearly basis without consideration of the capacity to harvest and use this resource. Exploitable water resources, which refer to the volume of surface water or groundwater that is available with an occurrence of 90% of the time, are considerably less than renewable water resources, but no universal method exists to assess such exploitable water resources. There is no universally agreed method for the computation of incoming freshwater flows originating outside of a country's borders. Nor is there any standard method to account for return flows, the part of the water withdrawn from its source and which flows back to the river system after use. In countries where return flow represents a substantial part of water withdrawal, the indicator tends to underestimate available water and therefore overestimate the level of water stress. Other limitations that affect the interpretation of the water stress indicator include: difficulty to obtain accurate, complete and up-to-date data; potentially large variation of sub-national data; lack of account of seasonal variations in water resources; lack of consideration to the distribution among water uses; lack of consideration of water quality and its suitability for use; and the indicator can be higher than 100 per cent when water withdrawal includes secondary freshwater (water withdrawn previously and returned to the system), non-renewable water (fossil groundwater), when annual groundwater withdrawal is higher than annual replenishment (over-abstraction) or when water withdrawal includes part or all of the water set aside for environmental water requirements. Some of these issues can be solved through disaggregation of the index at the level of hydrological units and by distinguishing between different use sectors. However, due to the complexity of water flows, both within a country and between countries, care should be taken not to double-count.
None
91
Agricultural land (sq. km)
Environment and Natural Resources
Agriculture
Land area that is arable, under permanent crops and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops.
FAO
Data are collected through a FAO Questionnaire on Land Use, Irrigation and Agricultural Practices, based on the FAO Land Use classification.
The FAO Land Use classification is aligned with the UN System of Environmental and Economic Accounting (SEEA); the UN Framework for the Development of Environmental Statistics (FDES); and the World Census of Agriculture. It is furthermore consistent with the land use classes of the Intergovernmental Panel on Climate Change for country reporting to the UN Framework Convention on Climate Change (UNFCCC). A mapping between the FAO, SEEA, World Census of Agriculture and IPCC classifications is provided in the FAO Questionnaire.
None
92
Agricultural land (% of land area)
Environment and Natural Resources
Agriculture
Share of land area that is arable, under permanent crops and under permanent pastures.
FAO
Data are collected through a FAO Questionnaire on Land Use, Irrigation and Agricultural Practices, based on the FAO Land Use classification.
The FAO Land Use classification is aligned with the UN System of Environmental and Economic Accounting (SEEA); the UN Framework for the Development of Environmental Statistics (FDES); and the World Census of Agriculture. It is furthermore consistent with the land use classes of the Intergovernmental Panel on Climate Change for country reporting to the UN Framework Convention on Climate Change (UNFCCC). A mapping between the FAO, SEEA, World Census of Agriculture and IPCC classifications is provided in the FAO Questionnaire. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops.
SDG Goal 15, indicator 15.1.1; ENP-South Eurostat Data Browser: Area 'Agriculture and fisheries'
93
Arable land (% of land area)
Environment and Natural Resources
Agriculture
Share of land area that is arable land: land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.
FAO
Data collected through a FAO Questionnaire on Land Use, Irrigation and Agricultural Practices, based on the FAO Land Use classification.
The FAO Land Use classification is aligned with the UN System of Environmental and Economic Accounting (SEEA); the UN Framework for the Development of Environmental Statistics (FDES); and the World Census of Agriculture. It is furthermore consistent with the land use classes of the Intergovernmental Panel on Climate Change for country reporting to the UN Framework Convention on Climate Change (UNFCCC). A mapping between the FAO, SEEA, World Census of Agriculture and IPCC classifications is provided in the FAO Questionnaire.
None
94
Arable land (hectares per 1.000 persons)
Environment and Natural Resources
Agriculture
Arable land (hectares per 1.000 persons). it includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.
FAO
Data collected through a FAO Questionnaire on Land Use, Irrigation and Agricultural Practices, based on the FAO Land Use classification.
The FAO Land Use classification is aligned with the UN System of Environmental and Economic Accounting (SEEA); the UN Framework for the Development of Environmental Statistics (FDES); and the World Census of Agriculture. It is furthermore consistent with the land use classes of the Intergovernmental Panel on Climate Change for country reporting to the UN Framework Convention on Climate Change (UNFCCC). A mapping between 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: Area 'Agriculture and fisheries'
95
Permanent cropland (% of land area)
Environment and Natural Resources
Agriculture
Share of land area that is permanent cropland: land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber.
FAO
Data collected through a FAO Questionnaire on Land Use, Irrigation and Agricultural Practices, based on the FAO Land Use classification.
The FAO Land Use classification is aligned with the UN System of Environmental and Economic Accounting (SEEA); the UN Framework for the Development of Environmental Statistics (FDES); and the World Census of Agriculture. It is furthermore consistent with the land use classes of the Intergovernmental Panel on Climate Change for country reporting to the UN Framework Convention on Climate Change (UNFCCC). A mapping between 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: Area 'Agriculture and fisheries'
96
Livestock production index (2014-2016 = 100)
Environment and Natural Resources
Agriculture
Vaue of livestock production for each year relative to the base period 2014-2016. It index includes meat and milk from all sources, dairy products such as cheese, and eggs, honey, raw silk, wool, and hides and skins.
FAO
The index is based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional and world levels are calculated by the Laspeyres formula. Production quantities of each commodity are weighted by 2014-2016 average international commodity prices and summed 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 the FAO indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting, once in the production data and once with the crops or livestock produced from them. Deductions for seed (in the case of eggs, for hatching) and for livestock and poultry feed apply to both domestically produced and imported commodities. They cover only primary agricultural products destined to animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feed items such as bran, oilcakes, meals and molasses have been completely excluded from the calculations at all stages. It should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group 'crops' and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group 'livestock products'. For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed. Indices which take into account deductions for feed and seed are referred to as ''net''. Indices calculated without any deductions for feed and seed are referred to as ''gross'. The 'international commodity prices' are used in order to avoid the use of exchange rates for obtaining 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 a Geary-Khamis formula for the agricultural sector. This method assigns a single 'price' to each commodity. For example, one metric ton of wheat has the same price regardless of the country where it was produced. The currency unit in which the prices are expressed has no influence on the indices published. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops.
None
None
97
Crop production index (2014-2016 = 100)
Environment and Natural Resources
Agriculture
Vaue of agricultural production for each year relative to the base period 2014-2016. It includes all crops except fodder crops.
FAO
The index is based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional and world levels are calculated by the Laspeyres formula. Production quantities of each commodity are weighted by 2014-2016 average international commodity prices and summed 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 the FAO indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting, once in the production data and once with the crops or livestock produced from them. Deductions for seed (in the case of eggs, for hatching) and for livestock and poultry feed apply to both domestically produced and imported commodities. They cover only primary agricultural products destined to animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feed items such as bran, oilcakes, meals and molasses have been completely excluded from the calculations at all stages. It should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group 'crops' and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group 'livestock products'. For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed. Indices which take into account deductions for feed and seed are referred to as ''net''. Indices calculated without any deductions for feed and seed are referred to as ''gross'. The 'international commodity prices' are used in order to avoid the use of exchange rates for obtaining 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 a Geary-Khamis formula** for the agricultural sector. This method assigns a single 'price' to each commodity. For example, one metric ton of wheat has the same price regardless of the country where it was produced. The currency unit in which the prices are expressed has no influence on the indices published. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops.
None
None
98
Food production index (2014-2016 = 100)
Environment and Natural Resources
Agriculture
Vaue of food production for each year relative to the base period 2014-2016. It covers food crops that are considered edible and that contain nutrients. Coffee and tea are excluded because, although edible, they have no nutritive value.
FAO
The index is based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional and world levels are calculated by the Laspeyres formula. Production quantities of each commodity are weighted by 2014-2016 average international commodity prices and summed 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 the FAO indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting, once in the production data and once with the crops or livestock produced from them. Deductions for seed (in the case of eggs, for hatching) and for livestock and poultry feed apply to both domestically produced and imported commodities. They cover only primary agricultural products destined to animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feed items such as bran, oilcakes, meals and molasses have been completely excluded from the calculations at all stages. It should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group 'crops' and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group 'livestock products'. For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed. Indices which take into account deductions for feed and seed are referred to as ''net''. Indices calculated without any deductions for feed and seed are referred to as ''gross'. The 'international commodity prices' are used in order to avoid the use of exchange rates for obtaining 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 a Geary-Khamis formula for the agricultural sector. This method assigns a single 'price' to each commodity. For example, one metric ton of wheat has the same price regardless of the country where it was produced. The currency unit in which the prices are expressed has no influence on the indices published. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops.
None
None
99
Fertilizer consumption (kilograms per hectare of arable land)
Environment and Natural Resources
Agriculture
Quantity of plant nutrients used per unit of arable land. Fertilizer products cover nitrogenous, potash, and phosphate fertilizers (including ground rock phosphate). Traditional nutrients--animal and plant manures--are not included.
World Bank Development Indicators from FAO
Fertilizer consumption measures the quantity of plant nutrients, and is calculated as production plus imports minus exports. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops.
The FAO has revised the time series for fertilizer consumption and irrigation for 2002 onward. In the previous release, the data were based on total consumption of fertilizers, but the data in the recent release are based on the nutrients in fertilizers. Some countries compile fertilizer data on a calendar year basis, while others compile on a crop year basis (July-June). Previous editions of this indicator, Fertilizer consumption (100 grams per hectare of arable land), reported data on a crop year basis, but this edition uses the calendar year, as adopted by the FAO. The data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops.
SDG Goal 2, indicator 2.4.1
100
Agricultural methane emissions (thousand metric tons of CO2 equivalent)
Environment and Natural Resources
Agriculture
Methane emissions from animals, animal waste, rice production, agricultural waste burning (nonenergy, on-site), and savanna burning.
a) World Resources Institute; b) World Bank Development Indicators for Syria
To estimate emissions, the countries that are Parties to the Climate Change Convention (UNFCCC) use complex, state-of-the-art methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC). Methane emissions result largely from agricultural activities, industrial production landfills and wastewater treatment, and other sources such as tropical forest and other vegetation fires.
The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared. A kilogram of methane is 21 times as effective at trapping heat in the earth's atmosphere as a kilogram of carbon dioxide within 100 years.
None
101
Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)
Environment and Natural Resources
Agriculture
Nitrous oxide emissions produced through fertilizer use (synthetic and animal manure), animal waste management, agricultural waste burning (nonenergy, on-site), and savanna burning.
a) World Resources Institute; b) World Bank Development Indicators for Syria
Agricultural nitrous oxide emissions are emissions produced through fertilizer use (synthetic and animal manure), animal waste management, agricultural waste burning (nonenergy, on-site), and savannah burning. IPCC category 4 = Agriculture. Expressed in CO2 equivalent using the GWP100 metric of the Second Assessment Report of IPCC and include N2O (GWP100=310).
Nitrous oxide is a powerful greenhouse gas, with an estimated atmospheric lifetime of 114 years, compared with 12 years for methane. The per kilogram global warming potential of nitrous oxide is nearly 310 times that of carbon dioxide within 100 years. The emissions are usually expressed in carbon dioxide equivalents using the global warming potential, which allows the effective contributions of different gases to be compared.
None
102
Population, female
Gender Gaps
Population and Gender
Population by gender is based on the de facto definition of population, which counts all female 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. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality.
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
103
Population, male
Gender Gaps
Population and Gender
Population by gender is based on the de facto definition of population, which counts all female 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. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality.
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
104
Population, female (% of total population)
Gender Gaps
Population and Gender
The percentage of the population that is female (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. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality.
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
105
Population ages 0-14, female (% of female population)
Gender Gaps
Population and Gender
Female population between the ages 0 to 14 as a percentage of the total female population at the 1st January of each year.
a) World Bank Development Indicators, from 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'
106
Population ages 0-14, male (% of male population)
Gender Gaps
Population and Gender
Male population between the ages 0 to 14 as a percentage of the total male population at the 1st January of each year.
a) World Bank Development Indicators, from 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
None
107
Population ages 65 and above, female (% of female population)
Gender Gaps
Population and Gender
Female population 65 years of age or older as a percentage of the total female population at the 1st January of each year.
a) World Bank Development Indicators, from 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
None
108
Population ages 65 and above, male (% of male population)
Gender Gaps
Population and Gender
Male population ages 65 and above as a percentage of the total population at the 1st January of each year.
a) World Bank Development Indicators, from 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
None
109
Life expectancy at birth, female (years)
Gender Gaps
Population and Gender
Number of years a female newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.
a) World Bank Development Indicators, from: United Nations Population Division, National Statistical Offices, Eurostat; b) Istat for Italy
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'
110
Life expectancy at birth, male (years)
Gender Gaps
Population and Gender
Number of years a male newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.
a) World Bank Development Indicators, from: United Nations Population Division, National Statistical Offices, Eurostat; b) Istat for Italy
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'
111
Mortality rate, infant, female (per 1,000 live births)
Gender Gaps
Population and Gender
Number of female infants dying before reaching one year of age, per 1,000 female live births in a given year.
a) United Nations Inter-agency Group for Child Mortality Estimation; b) Istat for Italy
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
None
112
Mortality rate, infant, male (per 1,000 live births)
Gender Gaps
Population and Gender
Number of male infants dying before reaching one year of age, per 1,000 male live births in a given year.
a) United Nations Inter-agency Group for Child Mortality Estimation; b) Istat for Italy
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
None
113
Prevalence of current tobacco use, females (% of female adults)
Gender Gaps
Other Gender Issues
The percentage of the female population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population.
WHO
A statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015). Once the age-and-sex-specific prevalence rates from national surveys were compiled into a dataset, the model was fit to calculate trend estimates from the year 2000 to 2025. The model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. Depending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population.
Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population. Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution.
None
114
Prevalence of current tobacco use, males (% of male adults)
Gender Gaps
Other Gender Issues
The percentage of the male population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population.
WHO
A statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015). Once the age-and-sex-specific prevalence rates from national surveys were compiled into a dataset, the model was fit to calculate trend estimates from the year 2000 to 2025. The model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. Depending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population.
Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population. Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution.
None
115
Gross intake ratio to the last grade of lower secondary general education, female (%)
Gender Gaps
Other Gender Issues
Number of new female entrants into the last grade of primary education or lower secondary general education, regardless of age, expressed as a percentage of the female population at the intended entrance age to the last grade of primary education or lower secondary general education.The intended entrance age to the last grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade.
UNESCO
Data come from Population censuses and household surveys which collect data on the highest level of education or grade completed by children and young people in a household, through self- or household-declaration. In the former case, each household member above a certain age reports his or her own level of educational attainment. In the latter case, one person, usually the head of the household or another reference person, indicates the highest grade and/or level of education completed by each member of the household. Administrative data from ministries of education on the structure of the education system (entrance ages and durations) are also needed. Surveys can serve as a source of data if they collect information for the age groups of concern. In addition to national surveys, international sample surveys, such as Demographic and Health Surveys (DHS, http://dhsprogram.com) or Multiple Indicator Cluster Surveys (MICS, http://mics.unicef.org), are another source. These surveys are designed to meet commonly agreed upon international data needs and aim to assure cross-national comparability, while also providing data for national policy purposes. These surveys are implemented on a regular basis in selected countries, on average every 3 to 5 years.
The number of new entrants in the last grade of the given level of education, regardless of age, is expressed as a percentage of the population of the intended entrance age to the last grade of that level of education. If data on new entrants are not collected directly, they can be calculated by subtracting the number of pupils repeating the last grade from total enrolment in the last grade. This is a gross measure and may therefore exceed 100% if there are large numbers of pupils who entered school either early or late and/or who have repeated earlier grades. The fact that the indicator can exceed 100% also makes it more difficult to interpret than the completion rate. Compared to the completion rate, the gross intake ratio to the last grade does not indicate how many children complete the last grade, only how many children enter that grade. If students in the last grade leave school before graduation, the gross intake ratio to the last grade overestimates completion. Data limitations preclude adjusting for students who drop out during the final year of lower secondary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual lower secondary completion rate.
None
116
Gross intake ratio to the last grade of lower secondary general education, male (%)
Gender Gaps
Other Gender Issues
Number of new male entrants into the last grade of primary education or lower secondary general education, regardless of age, expressed as a percentage of the female population at the intended entrance age to the last grade of primary education or lower secondary general education.The intended entrance age to the last grade is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade.
UNESCO
Data come from Population censuses and household surveys which collect data on the highest level of education or grade completed by children and young people in a household, through self- or household-declaration. In the former case, each household member above a certain age reports his or her own level of educational attainment. In the latter case, one person, usually the head of the household or another reference person, indicates the highest grade and/or level of education completed by each member of the household. Administrative data from ministries of education on the structure of the education system (entrance ages and durations) are also needed. Surveys can serve as a source of data if they collect information for the age groups of concern. In addition to national surveys, international sample surveys, such as Demographic and Health Surveys (DHS, http://dhsprogram.com) or Multiple Indicator Cluster Surveys (MICS, http://mics.unicef.org), are another source. These surveys are designed to meet commonly agreed upon international data needs and aim to assure cross-national comparability, while also providing data for national policy purposes. These surveys are implemented on a regular basis in selected countries, on average every 3 to 5 years.
The number of new entrants in the last grade of the given level of education, regardless of age, is expressed as a percentage of the population of the intended entrance age to the last grade of that level of education. If data on new entrants are not collected directly, they can be calculated by subtracting the number of pupils repeating the last grade from total enrolment in the last grade. This is a gross measure and may therefore exceed 100% if there are large numbers of pupils who entered school either early or late and/or who have repeated earlier grades. The fact that the indicator can exceed 100% also makes it more difficult to interpret than the completion rate. Compared to the completion rate, the gross intake ratio to the last grade does not indicate how many children complete the last grade, only how many children enter that grade. If students in the last grade leave school before graduation, the gross intake ratio to the last grade overestimates completion. Data limitations preclude adjusting for students who drop out during the final year of lower secondary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual lower secondary completion rate.
None
117
Labor force participation rate, female (% of female population ages 15-64)
Gender Gaps
Work and Gender
Percentage of the female population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
National data on labour force participation rates may not be comparable owing to differences in concepts and methodologies. The single most important factor affecting data comparability is the data source. Labour force data obtained from population censuses are often based on a restricted number of questions on the economic characteristics of individuals, with little possibility of probing. The resulting data, therefore, are generally not consistent with 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. Establishment censuses and surveys can – by their nature – only provide data on the employed population, leaving out the unemployed and, in many countries, also excluding workers engaged in small establishments or in the informal economy who fall outside the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
118
Labor force participation rate, male (% of male population ages 15-64)
Gender Gaps
Work and Gender
Percentage of the male population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
National data on labour force participation rates may not be comparable owing to differences in concepts and methodologies. The single most important factor affecting data comparability is the data source. Labour force data obtained from population censuses are often based on a restricted number of questions on the economic characteristics of individuals, with little possibility of probing. The resulting data, therefore, are generally not consistent with 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. Establishment censuses and surveys can – by their nature – only provide data on the employed population, leaving out the unemployed and, in many countries, also excluding workers engaged in small establishments or in the informal economy who fall outside the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
119
Labor force participation rate for ages 15-24, female (%)
Gender Gaps
Work and Gender
Percentage of the female population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
National data on labour force participation rates may not be comparable owing to differences in concepts and methodologies. The single most important factor affecting data comparability is the data source. Labour force data obtained from population censuses are often based on a restricted number of questions on the economic characteristics of individuals, with little possibility of probing. The resulting data, therefore, are generally not consistent with 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. Establishment censuses and surveys can – by their nature – only provide data on the employed population, leaving out the unemployed and, in many countries, also excluding workers engaged in small establishments or in the informal economy who fall outside the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
120
Labor force participation rate for ages 15-24, male (%)
Gender Gaps
Work and Gender
Percentage of the male population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
National data on labour force participation rates may not be comparable owing to differences in concepts and methodologies. The single most important factor affecting data comparability is the data source. Labour force data obtained from population censuses are often based on a restricted number of questions on the economic characteristics of individuals, with little possibility of probing. The resulting data, therefore, are generally not consistent with 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. Establishment censuses and surveys can – by their nature – only provide data on the employed population, leaving out the unemployed and, in many countries, also excluding workers engaged in small establishments or in the informal economy who fall outside the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
121
Employment to population ratio, 15+, female (%)
Gender Gaps
Work and Gender
Percentage of a country's female population ages 15 years and over that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered 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 series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
122
Employment to population ratio, 15+, male (%)
Gender Gaps
Work and Gender
Percentage of a country's population ages 15 years and over that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered 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 series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
123
Employment to population ratio, ages 15-24, female (%)
Gender Gaps
Work and Gender
Percentage of a country's female population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
124
Employment to population ratio, ages 15-24, male (%)
Gender Gaps
Work and Gender
Percentage of a country's male population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
125
Employment in agriculture, female (% of female employment)
Gender Gaps
Work and Gender
Women of working age engaged in the agricoltural sector to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with 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 series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Data presented by branch of economic activity is 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 utilized for the collection and reporting of statistics according to such activities. The original version of ISIC was adopted in 1948, and it has been revised four times since : in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of the ISIC Rev.3 was introduced in 2002 to account for substantial changes in many countries’ economic structure (ISIC Rev. 3.1). It is important to note that different versions of the ISIC can be used across countries, with countries moving to adopting the most recent version at different paces. 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 can 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 can limit the comparability of statistics on employment by economic activity between countries or over time. Comparability of employment statistics across countries is affected most significantly by variations in the definitions used for the employment figures. Differences may result from age coverage, such as the lower and upper age bounds for labour force activity. Estimates of employment are also likely to vary according to whether members of the armed forces are included. When the armed forces are included in the measure of employment they are usually allocated to the services sector. Therefore, in countries that do not include armed forces, the services sector tends to be understated in comparison with countries where they are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labour force survey have to do with slight national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members before for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment figures can vary for this reason alone. Also, changes in the level of employment can occur throughout the year, but this can be obscured when fewer observations are available.
None
126
Employment in agriculture, male (% of male employment)
Gender Gaps
Work and Gender
Men of working age engaged in the agricoltural sector to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with 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 series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Data presented by branch of economic activity is 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 utilized for the collection and reporting of statistics according to such activities. The original version of ISIC was adopted in 1948, and it has been revised four times since : in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of the ISIC Rev.3 was introduced in 2002 to account for substantial changes in many countries’ economic structure (ISIC Rev. 3.1). It is important to note that different versions of the ISIC can be used across countries, with countries moving to adopting the most recent version at different paces. 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 can 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 can limit the comparability of statistics on employment by economic activity between countries or over time. Comparability of employment statistics across countries is affected most significantly by variations in the definitions used for the employment figures. Differences may result from age coverage, such as the lower and upper age bounds for labour force activity. Estimates of employment are also likely to vary according to whether members of the armed forces are included. When the armed forces are included in the measure of employment they are usually allocated to the services sector. Therefore, in countries that do not include armed forces, the services sector tends to be understated in comparison with countries where they are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labour force survey have to do with slight national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members before for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment figures can vary for this reason alone. Also, changes in the level of employment can occur throughout the year, but this can be obscured when fewer observations are available.
None
127
Employment in industry, female (% of female employment)
Gender Gaps
Work and Gender
Women of working age who engaged in the industrial sector to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with 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 series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Data presented by branch of economic activity is 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 utilized for the collection and reporting of statistics according to such activities. The original version of ISIC was adopted in 1948, and it has been revised four times since : in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of the ISIC Rev.3 was introduced in 2002 to account for substantial changes in many countries’ economic structure (ISIC Rev. 3.1). It is important to note that different versions of the ISIC can be used across countries, with countries moving to adopting the most recent version at different paces. 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 can 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 can limit the comparability of statistics on employment by economic activity between countries or over time. Comparability of employment statistics across countries is affected most significantly by variations in the definitions used for the employment figures. Differences may result from age coverage, such as the lower and upper age bounds for labour force activity. Estimates of employment are also likely to vary according to whether members of the armed forces are included. When the armed forces are included in the measure of employment they are usually allocated to the services sector. Therefore, in countries that do not include armed forces, the services sector tends to be understated in comparison with countries where they are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labour force survey have to do with slight national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members before for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment figures can vary for this reason alone. Also, changes in the level of employment can occur throughout the year, but this can be obscured when fewer observations are available.
None
128
Employment in industry, male (% of male employment)
Gender Gaps
Work and Gender
Men of working age who engaged in the industrial sector to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with 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 series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Data presented by branch of economic activity is 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 utilized for the collection and reporting of statistics according to such activities. The original version of ISIC was adopted in 1948, and it has been revised four times since : in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of the ISIC Rev.3 was introduced in 2002 to account for substantial changes in many countries’ economic structure (ISIC Rev. 3.1). It is important to note that different versions of the ISIC can be used across countries, with countries moving to adopting the most recent version at different paces. 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 can 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 can limit the comparability of statistics on employment by economic activity between countries or over time. Comparability of employment statistics across countries is affected most significantly by variations in the definitions used for the employment figures. Differences may result from age coverage, such as the lower and upper age bounds for labour force activity. Estimates of employment are also likely to vary according to whether members of the armed forces are included. When the armed forces are included in the measure of employment they are usually allocated to the services sector. Therefore, in countries that do not include armed forces, the services sector tends to be understated in comparison with countries where they are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labour force survey have to do with slight national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members before for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment figures can vary for this reason alone. Also, changes in the level of employment can occur throughout the year, but this can be obscured when fewer observations are available.
None
129
Employment in services, female (% of female employment)
Gender Gaps
Work and Gender
Women of working age engaged in the tertiary sector to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with 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 series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Data presented by branch of economic activity is 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 utilized for the collection and reporting of statistics according to such activities. The original version of ISIC was adopted in 1948, and it has been revised four times since : in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of the ISIC Rev.3 was introduced in 2002 to account for substantial changes in many countries’ economic structure (ISIC Rev. 3.1). It is important to note that different versions of the ISIC can be used across countries, with countries moving to adopting the most recent version at different paces. 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 can 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 can limit the comparability of statistics on employment by economic activity between countries or over time. Comparability of employment statistics across countries is affected most significantly by variations in the definitions used for the employment figures. Differences may result from age coverage, such as the lower and upper age bounds for labour force activity. Estimates of employment are also likely to vary according to whether members of the armed forces are included. When the armed forces are included in the measure of employment they are usually allocated to the services sector. Therefore, in countries that do not include armed forces, the services sector tends to be understated in comparison with countries where they are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labour force survey have to do with slight national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members before for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment figures can vary for this reason alone. Also, changes in the level of employment can occur throughout the year, but this can be obscured when fewer observations are available.
None
130
Employment in services, male (% of male employment)
Gender Gaps
Work and Gender
Men of working age engaged in the tertiary sector to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with 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 series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Data presented by branch of economic activity is 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 utilized for the collection and reporting of statistics according to such activities. The original version of ISIC was adopted in 1948, and it has been revised four times since : in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of the ISIC Rev.3 was introduced in 2002 to account for substantial changes in many countries’ economic structure (ISIC Rev. 3.1). It is important to note that different versions of the ISIC can be used across countries, with countries moving to adopting the most recent version at different paces. 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 can 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 can limit the comparability of statistics on employment by economic activity between countries or over time. Comparability of employment statistics across countries is affected most significantly by variations in the definitions used for the employment figures. Differences may result from age coverage, such as the lower and upper age bounds for labour force activity. Estimates of employment are also likely to vary according to whether members of the armed forces are included. When the armed forces are included in the measure of employment they are usually allocated to the services sector. Therefore, in countries that do not include armed forces, the services sector tends to be understated in comparison with countries where they are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labour force survey have to do with slight national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members before for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment figures can vary for this reason alone. Also, changes in the level of employment can occur throughout the year, but this can be obscured when fewer observations are available.
None
131
Wage and salaried workers, female (% of female employment)
Gender Gaps
Work and Gender
Persons who hold the type of jobs defined as 'paid employment jobs,' where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
132
Wage and salaried workers, male (% of male employment)
Gender Gaps
Work and Gender
Persons who hold the type of jobs defined as 'paid employment jobs,' where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
133
Self-employed, female (% of female employment)
Gender Gaps
Work and Gender
Persons who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a 'self-employment jobs.' i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include four sub-categories of employers, own-account workers, members of producers' cooperatives, and contributing family workers.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
134
Self-employed, male (% of male employment)
Gender Gaps
Work and Gender
Persons who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a 'self-employment jobs.' i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include four sub-categories of employers, own-account workers, members of producers' cooperatives, and contributing family workers.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
135
Employers, female (% of female employment)
Gender Gaps
Work and Gender
Persons who, working on their own account or with one or a few partners, hold the type of jobs defined as a 'self-employment jobs' i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
136
Employers, male (% of male employment)
Gender Gaps
Work and Gender
Persons who, working on their own account or with one or a few partners, hold the type of jobs defined as a 'self-employment jobs' i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
137
Vulnerable employment, female (% of female employment)
Gender Gaps
Work and Gender
Contributing family workers and own-account workers as a percentage of total employment.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
138
Vulnerable employment, male (% of male employment)
Gender Gaps
Work and Gender
Contributing family workers and own-account workers as a percentage of total employment.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
139
Contributing family workers, female (% of female employment)
Gender Gaps
Work and Gender
Persons who hold 'self-employment jobs' as own-account workers in a market-oriented establishment operated by a related person living in the same household.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
140
Contributing family workers, male (% of male employment)
Gender Gaps
Work and Gender
Persons who hold 'self-employment jobs' as own-account workers in a market-oriented establishment operated by a related person living in the same household.
ILO Modelled Estimates (ILOEST)
Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year.
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, 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 modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) 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. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
None
141
Unemployment, female (% of female labor force)
Gender Gaps
Work and Gender
Share of the female labor force that is without work but available for and seeking employment.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labour market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programmes, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave. The overall unemployment rate for a country is a widely used measure of its unutilized labour supply. Unemployment rates by specific groups, defined by age, sex, occupation or industry, are also useful in identifying groups of workers and sectors most vulnerable to joblessness.
SDG Goal 8, indicator 8.5.2; ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
142
Unemployment, male (% of male labor force)
Gender Gaps
Work and Gender
Share of the male force that is without work but available for and seeking employment.
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labour market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programmes, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave. The overall unemployment rate for a country is a widely used measure of its unutilized labour supply. Unemployment rates by specific groups, defined by age, sex, occupation or industry, are also useful in identifying groups of workers and sectors most vulnerable to joblessness.
SDG Goal 8, indicator 8.5.2; ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
143
Proportion of seats held by women in national parliaments (%)
Gender Gaps
Other Gender Issues
Percentage of parliamentary seats in a single or lower chamber held by women.
Inter-Parliamentary Union
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 dispatched to parliaments to solicit the latest available data. If no response is provided, other methods are used to obtain the information, such as from the electoral management body, parliamentary websites, or Internet searches. Additional information gathered from other sources is regularly crosschecked with parliament. Data are updated on a monthly basis, up to the last day of the month.
The number of countries covered varies with suspensions or dissolutions of parliaments. As of 1 February 2016, 193 countries are included. There can be difficulties in obtaining information on by-election results and replacements due to death or resignation. These changes are ad hoc events that are more difficult to keep track of. By-elections, for instance, are often not announced internationally as general elections are. The data excludes the numbers and percentages of women in the upper chambers of parliament. The information is available on the Inter-Parliamentary Union (IPU) website at https://data.ipu.org/women-ranking. Parliaments vary considerably in their internal workings and procedures, however, generally legislate, oversee government and represent the electorate. In terms of measuring women’s contribution to political decision-making, this indicator may not be sufficient because some women may face obstacles in fully and efficiently carrying out their parliamentary mandate.
SDG Goal 5 and Goal 17, indicator 5.5.1/16.7.1
144
Women Business and the Law Index (scale 1-100)
Gender Gaps
Other Gender Issues
Composite index which measures how laws and regulations affect women’s economic opportunity. Overall scores are calculated by taking the average score of each index (Mobility, Workplace, Pay, Marriage, Parenthood, Entrepreneurship, Assets and Pension), with 100 representing the highest possible score.
World Bank
Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 2,000 respondents with expertise in family, labor, and criminal law, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the 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 checks questionnaire responses for accuracy. Thirty-five data points are scored across eight indicators of four or five binary questions, with each indicator representing a different phase 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. Overall scores are then calculated by taking the average of each indicator, with 100 representing the highest possible score.
The Women, Business and the Law methodology has limitations that should be considered when interpreting the data. All eight indicators are based on standardized assumptions to ensure comparability across economies. Comparability is one of the strengths of the data, but the assumptions can also be limitations as they may not capture all restrictions or represent all particularities in a country. It is assumed that the woman resides in the economy's main business city. In federal economies, laws affecting women can vary by state or province. Even in nonfederal economies, women in rural areas and small towns could face more restrictive local legislation. Such restrictions are not captured by Women, Business and the Law unless they are also found in the main business city.
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145
Gender Development Index (min=0, max=1)
Gender Gaps
Other Gender Issues
Composite index which measures gender inequalities in achievement in three basic dimensions of human development: health, measured by female and male life expectancy at birth; education, measured by female and male expected years of schooling for children and female and male mean years of schooling for adults ages 25 years and older; and command over economic resources, measured by female and male estimated earned income. It is calculated as the ratio of women's Human Development Index (HDI) to men's value.
United Nations Development Programme
It is a geometric mean of normalized indices and ratio between female and male values, based upon the following indicators: a) Life expectancy at birth: UN/DESA (2022a).b) Expected years of schooling: CEDLAS and World Bank (2022), ICF Macro Demographic and Health Surveys (various years), UNESCO Institute for Statistics (2022) and United Nations Children’s Fund (UNICEF) Multiple Indicator Cluster Surveys (various years). c) Mean years of schooling for adults ages 25 and older: 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) Estimated earned income: Human Development Report Office estimates based on female and male shares of the economically active population, the ratio of the female to male wage in all sectors and gross national income in 2017 purchasing power parity (PPP) terms, and female and male shares of population from ILO (2022), IMF (2022), UN/DESA (2022a), United Nations Statistics Division (2022) and World Bank (2022).
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146
Gender Inequality Index (min=0, max=1)
Gender Gaps
Other Gender Issues
Composite index which measures gender-based disadvantage in three dimensions—reproductive health, empowerment and the labour market—for as many countries as data of reasonable quality allow. It shows the loss in potential human development due to inequality between female and male achievements in these dimensions. It ranges from 0, where women and men fare equally, to 1, where one gender fares as poorly as possible in all measured dimensions.
United Nations Development Programme
Values are computed using the association-sensitive inequality measure suggested by Seth (2009), which implies that the index is based on the general mean of general means of different orders—the first aggregation is by a geometric mean across dimensions; these means, calculated separately for women and men, are then aggregated using a harmonic mean across genders. The index is based upon the following indicators and sources: a) maternal mortality ratio : WHO, UNICEF, UNFPA, World Bank Group and 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 at least some 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 takes values below 1, but it can also reach values above 1, in countries where the gaps between the status of women and men are closed overall.
