Chapter 2 Data collection and processing
2.1 Data collection
Data are collected from various international sources to obtain the best country coverage. The starting year is 2000 until the most recent available data point.
Data are sourced from following databases:
Date of finalizing data collection: 30-08-2023
Pillar 1
Pillar 1 includes a series of indicators on economic growth.
It includes 6 indicators, namely:
1.1: GDP per capita, PPP
Data source: World Bank
Data series code: “NY.GDP.PCAP.PP.KD”
Indicator series name: GDP per capita, PPP (constant 2017 international $)
Brief Description: GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the U.S. dollar has in the United States. GDP at purchaser’s prices is the sum of gross value added by all resident producers in the country plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2017 international dollars. It replaces the same series used in the past, with 2017 instead of 2011 as base year.
Data access: https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD
SDG linkages: SDG target 8.1: Sustain per capita economic growth in accordance with national circumstances and, in particular, at least 7 per cent gross domestic product growth per annum in the least developed countries
+ SDG indicator 8.1.1: Annual growth rate of real GDP per capita1.2: Adjusted net national income per capita
Data source: World Bank
Data series code: “NY.ADJ.NNTY.PC.KD”
Indicator series name: Adjusted net national income per capita (constant 2015 US$)
Brief Description: The indicator is developed by World Bank staff estimates based on sources and methods in World Bank’s “The Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium” (2011). The adjusted net national income complements gross national income (GNI) in assessing economic progress (Hamilton and Ley 2010) by providing a broader measure of national income that accounts for the depletion of natural resources. Adjusted net national income is calculated by subtracting from GNI a charge for the consumption of fixed capital (a calculation that yields net national income) and for the depletion of natural resources. The deduction for the depletion of natural resources, which covers net forest depletion, energy depletion, and mineral depletion, reflects the decline in asset values associated with the extraction and harvesting of natural resources. This is analogous to depreciation of fixed assets. Growth rates of adjusted net national income are computed from constant price series deflated using the gross national expenditure (formerly domestic absorption) deflator.
Data access: https://data.worldbank.org/indicator/NY.ADJ.NNTY.PC.KD
SDG linkages: SDG target 8.1: Sustain per capita economic growth in accordance with national circumstances and, in particular, at least 7 per cent gross domestic product growth per annum in the least developed countries
+ No specific indicator1.3: Labour productivity
Data source: World Bank
Data series code: “SL.GDP.PCAP.EM.KD”
Indicator series name: Labour productivity - GDP per person employed (constant 2017 PPP USD)
Brief Description: Description/footnote: GDP per person employed is 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. It replaces the same series used in the past, with 2017 instead of 2011 as base year. World Bank, World Development Indicators database. Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank.
Data access: https://data.worldbank.org/indicator/SL.GDP.PCAP.EM.KD
SDG linkages: SDG target 8.2: Achieve higher levels of economic productivity through diversification, technological upgrading and innovation, including through a focus on high-value added and labour-intensive sectors
+ SDG indicator 8.2.1: Annual growth rate of real GDP per employed person1.4: Employment to population ratio
Data source: World Bank
Data series code: “SL.EMP.TOTL.SP.ZS”
Indicator series name: Employment to population ratio, 15+, total (%) (modeled ILO estimate)
Brief Description: Employment to population ratio is the proportion of a country’s 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 and older are generally considered the working-age population.
The employment-to-population ratio indicates how efficiently an economy provides jobs for people who want to work. A high ratio means that a large proportion of the population is employed. But a lower employment-to-population ratio can be seen as a positive sign, especially for young people, if an increase in their education causes it.
Data access: https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.ZS
SDG linkages: SDG 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all
+ No specific indicator1.5: Electricity consumption
Data source: International Energy Agency (IEA)
Data series code: “ELEPOP”
Indicator series name: Electricity consumption/population (kWh per capita)
Brief Description: Electricity consumption equals domestic supply less losses. Electric power consumption per capita (kWh ) is the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants, divided by midyear population. Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. Electricity consumption is equivalent to production less power plants’ own use and transmission, distribution, and transformation losses less exports plus imports. It includes consumption by auxiliary stations, losses in transformers that are considered integral parts of those stations, and electricity produced by pumping installations. Where data are available, it covers electricity generated by primary sources of energy - coal, oil, gas, nuclear, hydro, geothermal, wind, tide and wave, and combustible renewables.
Metadata:
General information: https://www.iea.org/data-and-statistics/data-product/world-energy-balances
Database documentation: https://iea.blob.core.windows.net/assets/08e3af5c-a438-4e50-b44d-1419cb7ff309/WORLDBAL_Documentation.pdf
More information: https://www.iea.org/data-and-statistics/data-product/electricity-information#electricityheat-supply-and-consumption-oecd-and-selected-countries
Data access:
The download requires a subscription to their data portal (not free of charge):
https://doi.org/10.1787/enestats-data-enFull access for UNCTAD staff: https://go.openathens.net/redirector/un.org?url=https://www.oecd-ilibrary.org/
SDG linkages: SDG 7: Ensure access to affordable, reliable, sustainable and modern energy for all
+ No specific indicator1.6: Export of goods and services
Data source: World Bank
Data series code: “NE.EXP.GNFS.ZS”
Indicator series name: Export of goods and services (% of GDP)
Brief Description: 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 originally sourced from the World Bank national accounts data, and OECD National Accounts data files. Some of the data are the results of national estimates, some other come from actual records.
Metadata: https://databank.worldbank.org/metadataglossary/world-development-indicators/series/NE.EXP.GNFS.ZS
Data access: https://data.worldbank.org/indicator/NE.EXP.GNFS.ZS
SDG linkages: SDG target 17.11: Significantly increase the exports of developing countries, in particular with a view to doubling the least developed countries’ share of global exports by 2020
+ SDG indicator 17.11.1: Developing countries’ and least developed countries’ share of global exportsPillar 2
The second pillar is dedicated to living conditions indicators.
It includes 7 indicators, namely:
2.1: Logistics Performance Index
Data source: World Bank
Data series code: “LP.LPI.OVRL.XQ”
Indicator series name: Logistics performance index: Overall (1=low to 5=high)
Brief Description: The Logistics Performance Index overall score reflects 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. Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent’s country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report.
Metadata: https://databank.worldbank.org/metadataglossary/world-development-indicators/series/LP.LPI.OVRL.XQ
Data access: https://data.worldbank.org/indicator/LP.LPI.OVRL.XQ
SDG linkages:
SDG target 9.1: Develop quality, reliable, sustainable and resilient infrastructure, including regional and transborder infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all
+ SDG indicator 9.1.2: Passenger and freight volumes, by mode of transportSDG target 17.11: Significantly increase the exports of developing countries, in particular with a view to doubling the least developed countries’ share of global exports by 2020
+ SDG indicator 17.11.1: Developing countries’ and least developed countries’ share of global exportsThis could be extended for more SDG linkages.
2.2: Fixed Internet broadband subscriptions
Data source: Global SDG Indicators Database
Data series code: “IT_NET_BBND”
Indicator series name: Fixed broadband subscriptions per 100 inhabitants, by speed (per 100 inhabitants)
Brief Description: Fixed broadband subscriptions refer to subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s. It includes cable modem, DSL, fibre-to-the-home/building, other fixed -broadband subscriptions, satellite broadband and terrestrial fixed wireless broadband. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks.
Metadata: https://unstats.un.org/sdgs/metadata/files/Metadata-17-06-01.pdf
Data access: https://unstats.un.org/sdgs/dataportal/database
Disaggregation: None
SDG linkages:
SDG target 17.6: Enhance North-South, South-South and triangular regional and international cooperation on and access to science, technology and innovation and enhance knowledge-sharing on mutually agreed terms, including through improved coordination among existing mechanisms, in particular at the United Nations level, and through a global technology facilitation mechanism
+ SDG indicator 17.6.1: Fixed Internet broadband subscriptions per 100 inhabitants, by speed2.3: Child mortality
Data source: Global SDG Indicators Database
Data series code: “SH_DYN_MORT”
Indicator series name: Under-five mortality rate (deaths per 1,000 live births)
Brief Description: The under-five mortality rate is the probability of a child born in a specific year or period dying before reaching the age of 5 years, if subject to age-specific mortality rates of that period, expressed as deaths per 1000 live births. The under-five mortality rate as defined here is, strictly speaking, not a rate (i.e. the number of deaths divided by the number of population at risk during a certain period of time), but a probability of death derived from a life table and expressed as a rate per 1000 live births.
Metadata: https://unstats.un.org/sdgs/metadata/files/Metadata-03-02-01.pdf
Data access: https://unstats.un.org/sdgs/dataportal/database
Disaggregation: Sex: Both sexes
+ X.Sex.=="BOTHSEX"SDG linkages:
SDG target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births
+ SDG indicator 3.2.1: Under‑5 mortality rate2.4: Safe water
Data source: Global SDG Indicators Database
Data series code: “SGH_H2O_SAFE”
Indicator series name: Proportion of population using safely managed drinking water services, by urban/rural (%)
Brief Description: The proportion of the population using safely managed drinking water services is defined as the proportion of population using an improved drinking water source which is accessible on premises, available when needed and free from faecal and priority chemical contamination. ‘Improved’ drinking water sources include: piped supplies, boreholes and tubewells, protected dug wells, protected springs, rainwater, water kiosks, and packaged and delivered water. WHO/UNICEF Joint Monitoring Program for Water Supply, Sanitation and Hygiene has established international standards for classification of drinking water facilities and service levels to benchmark and compare progress across countries (see washdata.org).
Metadata: https://unstats.un.org/sdgs/metadata/files/Metadata-06-01-01.pdf
Data access: https://unstats.un.org/sdgs/dataportal/database
Disaggregation: Location: All areas (for some countries only ruban/rural are available, such as China)
+ X.Location.=="ALLAREA"SDG linkages:
SDG target 6.1: By 2030, achieve universal and equitable access to safe and affordable drinking water for all
+ SDG indicator 6.1.1: Proportion of population using safely managed drinking water services2.5: School enrollment (secondary)
Data source: World Bank
Data series code: “SE.SEC.ENRR”
Indicator series name: School enrollment, secondary (% gross)
Brief Description: Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.
Metadata: https://databank.worldbank.org/metadataglossary/world-development-indicators/series/SE.SEC.ENRR
Data access: https://data.worldbank.org/indicator/SE.SEC.ENRR
SDG linkages:
SDG 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all
+ No specific indicator2.6: Universal health coverage
Data source: Global SDG Indicators Database
Data series code: “SH_ACS_UNHC”
Indicator series name: Universal health coverage (UHC) service coverage index
Brief Description: The access to universal health is an indicator developed by the World Health Organisation (WHO). Coverage of essential health services is defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access, among the general and the most disadvantaged population.
Metadata: https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf
Data access: https://unstats.un.org/sdgs/dataportal/database
Disaggregation: None
SDG linkages:
SDG target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all
+ SDG indicator 3.8.1: : Coverage of essential health services 2.7: Access to financial services
Data source: Global SDG Indicators Database
Data series code: “FB_BNK_ACCSS”
Indicator series name: Proportion of adults (15 years and older) with an account at a financial institution or mobile-money-service provider, by sex (% of adults aged 15 years and older)
Brief Description: The percentage of adults (ages 15+) who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months.
Metadata: https://unstats.un.org/sdgs/metadata/files/Metadata-08-10-02.pdf
Data access: https://unstats.un.org/sdgs/dataportal/database
Disaggregation: Sex: Both sexes, Age: 15 years old and over, Location: All areas, Education level: Total or no breakdown by education level, Quantile: Total (national average) or no breakdown
+ X.Sex.=="BOTHSEX"
+ X.Age.=="15+"
+ X.Location.=="ALLAREA"
+ X.Education.level.=="_T"
+ X.Quantile.=="_T"SDG linkages:
SDG target 8.10: Strengthen the capacity of domestic financial institutions to encourage and expand access to banking, insurance and financial services for all
+ SDG indicator 8.10.2: Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service providerPillar 3
The third pillar is dedicated to equality indicators.
It includes 10 indicators, namely:
3.1: Gini index
Data source: World Bank
Data series code: “SI.POV.GINI”
Indicator series name: Gini index
Brief Description: Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.
Metadata: https://databank.worldbank.org/metadataglossary/world-development-indicators/series/SI.POV.GINI
Data access: https://data.worldbank.org/indicator/SI.POV.GINI
SDG linkages:
SDG 10: Reduce inequality within and among countries
+ No specific indicator3.2: Poverty headcount ratio
Data source: World Bank
Data series code: “SI.POV.LMIC”
Indicator series name: Poverty headcount ratio at $3.65 a day (2017 PPP) (% of population)
Brief Description: Poverty headcount ratio at $3.65 a day is the percentage of the population living on less than $3.65 a day at 2017 international prices. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $2.15 as the international poverty line using the 2017 PPP. The $3.65 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $6.85 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.
Metadata: https://databank.worldbank.org/metadataglossary/world-development-indicators/series/SI.POV.LMIC
Data access: https://data.worldbank.org/indicator/SI.POV.LMIC
SDG linkages:
SDG target 1.1: By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day
+ SDG indicator 1.1.1: Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)3.3: School enrolment, secondary (gross), gender parity index (GPI)
Data source: World Bank
Data series code: “SE.ENR.SECO.FM.ZS”
Indicator series name: School enrolment, secondary (gross), gender parity index (GPI)
Brief Description: Gender parity index for gross enrollment ratio in secondary education is the ratio of girls to boys enrolled at secondary level in public and private schools. The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women.
This indicator is calculated by dividing female gross enrollment ratio in secondary education by male gross enrollment ratio in secondary education. Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).
Data access: https://data.worldbank.org/indicator/SE.ENR.SECO.FM.ZS
SDG linkages:
SDG 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all
+ No specific indicator3.4: Ratio of female to male employment-to-population ratio
Data source: World Bank
Data series code: “SL.EMP.TOTL.SP.ZS”
+ Female: "SL.EMP.TOTL.SP.FE.ZS", Male: "SL.EMP.TOTL.SP.MA.ZS"Indicator series name: Ratio of female to male employment-to-population ratio (%) (modeled ILO estimate)
+ Employment to population ratio, 15+, female (%) (modeled ILO estimate)
+ Employment to population ratio, 15+, male (%) (modeled ILO estimate) Brief Description: Employment to population ratio is the proportion of a country’s 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 and older are generally considered the working-age population. The employment-to-population ratio indicates how efficiently an economy provides jobs for people who want to work. A high ratio means that a large proportion of the population is employed. But a lower employment-to-population ratio can be seen as a positive sign, especially for young people, if an increase in their education causes it.
Ratio of female to male employment-to-population ratio is calculated by dividing the employment-to-population ratio among women, by the corresponding rate for men.
Metadata:
https://databank.worldbank.org/metadataglossary/world-development-indicators/series/SL.EMP.TOTL.SP.FE.ZS https://databank.worldbank.org/metadataglossary/world-development-indicators/series/SL.EMP.TOTL.SP.MA.ZS
Data access:
https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.FE.ZS https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.MA.ZS
SDG linkages:
SDG 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all
+ No specific indicator3.5: Ratio of youth to adult employment-to-population ratio
Data source: ILOSTAT
Data series code: “EMP_2WAP”
+ Female: "AGE_YTHADULT_Y15-24", Male: "AGE_YTHADULT_YGE25"Indicator series name: Ratio of youth to adult employment-to-population ratio (modeled ILO estimate)
+ Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)
+ Employment to population ratio, 25+, total (%) (modeled ILO estimate) Brief Description: Employment to population ratio is the proportion of a country’s 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 and older are generally considered the working-age population. The employment-to-population ratio indicates how efficiently an economy provides jobs for people who want to work. A high ratio means that a large proportion of the population is employed. But a lower employment-to-population ratio can be seen as a positive sign, especially for young people, if an increase in their education causes it.
Ratio of youth to adult employment-to-population ratio is calculated by dividing the employment-to-population ratio among young people (15-24 years old), by the corresponding rate for adults (25 years and older).
Metadata: https://ilostat.ilo.org/resources/concepts-and-definitions/description-labour-force-statistics/
Data access:
https://www.ilo.org/shinyapps/bulkexplorer59/?lang=en&id=EMP_2WAP_SEX_AGE_RT_A
SDG linkages:
SDG 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all
+ No specific indicator3.6: Gender parity in the number of seats held by women and men in national parliaments
Data source: Global SDG Indicators Database
Data series code: “SG_GEN_PARL_PAR”
+ SDG series code: "SG_GEN_PARL"Indicator series name: Proportion of seats held by women in national parliaments (% of total number of seats)
Brief Description: The proportion of seats held by women in national parliaments, currently as of 1 January of reporting year, is currently measured as the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats. National parliaments can be bicameral or unicameral. This indicator covers the single chamber in unicameral parliaments and the lower chamber in bicameral parliaments. It does not cover the upper chamber of bicameral parliaments. Seats are usually won by members in general parliamentary elections. Seats refer to the number of parliamentary mandates or the number of members of parliament.
Gender parity in the number of seats held by women and men in national parliaments is calculated as a ratio between the number of seats held by women and men.
Metadata: https://unstats.un.org/sdgs/metadata/files/Metadata-05-05-01a.pdf
Data access: https://unstats.un.org/sdgs/dataportal/database
Disaggregation: None
SDG linkages:
SDG target 5.5: Ensure women’s full and effective participation and equal opportunities for leadership at all levels of decision-making in political, economic and public life
+ SDG indicator 5.5.1: Proportion of seats held by women in (a) national parliaments and (b) local governments3.7: Ratio of female to male labour force participation rate
Data source: World Bank
Data series code: “SL.TLF.CACT.FM.ZS”
Indicator series name: Ratio of female to male labour force participation rate (%) (modeled ILO estimate)
Brief Description: Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.
The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.
Metadata:
Data access:
https://data.worldbank.org/indicator/SL.TLF.CACT.FM.ZS
SDG linkages:
SDG 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all
+ No specific indicator3.8: Ratio of female age of first marriage to male age of first marriage
Data source: UNDESA Population Division - Would Marriage Database
Data series code: “AFMR”
Indicator series name: Ratio of female age of first marriage to male age of first marriage
Brief Description: The singulate mean age at marriage (SMAM) is the mean age at first marriage among persons who ever marry by a certain age limit, usually before the age of 50 years. It measures the average number of years lived as single or “never married” by a hypothetical cohort of individuals for which the proportions never married at each age are the same as those observed at a moment in time for a given population.
Compilation issues: Additional sources of data include survey programmes, such as the Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS), Pan-Arab Project for Child Development Surveys (PAPCHILD), Pan-Arab Project for Family Health Survey (PAPFAM) as well as national surveys. Tabulations for several countries are based on census microdata samples provided by National Statistical Offices to the Integrated Public Use Microdata Series, International (Minnesota Population Center, 2019)6 and survey microdata provided by the DHS and MICS. In order to maximize the availability of data on consensual unions and a wider range of age groups, two series of data on marital status are presented from the Demographic and Health Surveys and the Multiple Indicator Cluster Surveys: marital status data generated from the individual questionnaire for men and women of reproductive age that include consensual unions as a separate data point, and marital status data generated from the household questionnaire that pertain to age groups up to 75 years and over. Data generated from the household questionnaire are differentiated by designating them as such in the Data Source field.
In the case of DHS data, as available, two values are presented from the same survey: 1) The survey raw data (data as they were collected, without any structural changes). 2) Survey data made available via STATcompiler, a Web-based tool provided by DHS that allows users to build customized tables for DHS countries based on hundreds of indicators. The original country raw data is converted into a standardized format allowing easy comparison among countries or different DHS phases in the same country. The values from both sources may differ slightly due to the conversion of the raw data into the standardized format provided by the STATcompiler.
Note: Next release is expected in the first half of 2024.
The recommended priority on data sources is: DHS -> MICS -> Census
Metadata:
Data access:
https://population.un.org/MarriageData/Index.html#/home
SDG linkages:
SDG target 5.3: Eliminate all harmful practices, such as child, early and forced marriage and female genital mutilation
+ SDG indicator 5.3.1: Proportion of women aged 20–24 years who were married or in a union before age 15 and before age 18Pillar 4
The fourth pillar is dedicated to environmental indicators.
It includes 4 indicators, namely:
4.1: Carbon dioxide (CO\(_2\)) emissions per value added
Data source: Global SDG Indicators Database
Data series code: “EN_ATM_CO2GDP”
Indicator series name: Carbon dioxide emissions per unit of GDP PPP (kilogrammes of CO2 per constant 2017 United States dollars)
Brief Description: The indicator CO\(_2\) emissions per unit of value added represents the amount of emissions from fuel combustion produced by an economic activity, per unit of economic output. When computed for the whole economy, it combines effects of the average carbon intensity of the energy mix (linked to the shares of the various fossil fuels in the total); of the structure of an economy (linked to the relative weight of more or less energy-intensive sectors); of the average efficiency in the use of energy.
CO\(_2\) emissions per unit of value added is an indicator computed as ratio between CO\(_2\) emissions from fuel combustion and the value added of associated economic activities. The total intensity of the economy is defined as the ratio of total CO2 emissions from fuel combustion and per unit of GDP.
Metadata: https://unstats.un.org/sdgs/metadata/files/Metadata-09-04-01.pdf
Data access: https://unstats.un.org/sdgs/dataportal/database
Disaggregation: None
SDG linkages:
SDG target 9.4: By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies and industrial processes, with all countries taking action in accordance with their respective capabilities
+ SDG indicator 9.4.1: CO2 emission per unit of value added4.2: Energy intensity
Data source: Global SDG Indicators Database
Data series code: “EG_EGY_PRIM”
Indicator series name: Energy intensity level of primary energy (Megajoules per constant 2017 purchasing power parity GDP)
Brief Description: Energy intensity is an indication of how much energy is used to produce one unit of economic output. It is an inverse proxy of the efficiency with which an economy is able to use energy to produce economic output. A lower ratio indicates that less energy is used to produce one unit of output, so decreasing trends indicate progress.
Energy intensity is only an imperfect proxy for energy efficiency. It can be affected by a number of factors, such as climate, structure of the economy, nature of economic activities etc. that are not necessarily linked to pure efficiency. For better assessment of energy efficiency progress, more disaggregated data are needed, such as those at the sectoral and end-use level.
This indicator is based on the development of comprehensive energy statistics across supply and demand for all energy sources – statistics used to produce the energy balance. Once the energy balance is developed, the indicator can be obtained by dividing total energy supply over GDP.
Metadata: https://unstats.un.org/sdgs/metadata/files/Metadata-09-04-01.pdf
Data access: https://unstats.un.org/sdgs/dataportal/database
Disaggregation: None
SDG linkages:
SDG target 7.3: By 2030, double the global rate of improvement in energy efficiency
+ SDG indicator 7.3.1: Energy intensity measured in terms of primary energy and GDP4.3: Water use efficiency
Data source: Global SDG Indicators Database
Data series code: “ER_H2O_WUEYST”
Indicator series name: Change in Water Use Efficiency over time (United States dollars per cubic meter)
Brief Description: The indicator “Change in Water Use Efficiency over Time” tracks the value added in US dollars per volume of water used in cubic meters, by a given economic activity over time. It considers water use by all economic activities, with a focus on agriculture, industry and the service sector. The indicator allows countries to assess to what extent their economic growth depends on the use of their water resources. Regional differences in climate and water availability must be considered in the interpretation of this indicator, in particular for agriculture.
The rationale behind this indicator consists in providing information on the efficiency of the economic and social usage of water resources, i.e., value added generated by the use of water in the main sectors of the economy, and distribution network losses.
Metadata: https://unstats.un.org/sdgs/metadata/files/metadata-06-04-01.pdf
Data access: https://unstats.un.org/sdgs/dataportal/database
Disaggregation: Activity: Total
+ X.Activity.=="TOTAL"SDG linkages:
SDG target 6.4: By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity
+ SDG indicator 6.4.1: Change in water-use efficiency over time4.4: Terrestrial protected areas (% total land area)
Data source: Global SDG Indicators Database
Data series code: “ER_PTD_TERR”
Indicator series name: Terrestrial biodiversity area as % total protected areas
Brief Description: The indicator “Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type” shows temporal trends in the mean percentage of each important site for terrestrial and freshwater biodiversity (i.e., those that contribute significantly to the global persistence of biodiversity) that is covered by designated protected areas and Other Effective Area-based Conservation Measures (OECMs). Protected areas, as defined by the IUCN, are clearly defined geographical spaces, 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.
This indicator is calculated from data derived from a spatial overlap between digital polygons for protected areas from the World Database on Protected Areas (UNEP-WCMC & IUCN 2020), digital polygons for Other Effective Area-based Conservation Measures from the World Database on OECMs and digital polygons for terrestrial and freshwater Key Biodiversity Areas (from the World Database of Key Biodiversity Areas, including Important Bird and Biodiversity Areas, Alliance for Zero Extinction sites, and other Key Biodiversity Areas).
Metadata: https://unstats.un.org/sdgs/metadata/files/Metadata-15-01-02.pdf
Data access: https://unstats.un.org/sdgs/dataportal/database
Disaggregation: Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas [15.1.2] with code ER_PTD_TERR
SDG linkages:
SDG target 15.1: By 2020, ensure the conservation, restoration and sustainable use of terrestrial and inland freshwater ecosystems and their services, in particular forests, wetlands, mountains and drylands, in line with obligations under international agreements
+ SDG indicator 15.1.2: Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem typeOther indicators used
Following data series are downloaded for the process of IGI compilation:
- Overview indicators: Population, GDP
- Indicators used as proxies for interpolation:
- Indicators to fill missing data series: Gini index, CO\(_2\) emissions per unit of GDP
Population
Data source: UN DESA Population Division - World Population Prospects
Data series code: “POP”
Indicator series name: Population
Metadata: https://population.un.org/wpp/
Data access: https://population.un.org/wpp/
GDP
Data source: National Accounts - Analysis of Main Aggregates (AMA)
Data series code: “GDP_USD2015”
Indicator series name: GDP
Brief description: GDP at constant 2015 US dollars
Metadata: https://unstats.un.org/unsd/snaama/
Data access: https://unstats.un.org/unsd/snaama/
Electricity net consumption (EIA)
Data source: U.S. Energy Information Administration (EIA)
Data series code: None - data used to fill the gaps
Indicator series name: Electricity net consumption (billion kWh)
Brief description: The database includes values on electricity net consumption (billion kWh). Per capita values can be calculated by using population from WPP.
Total electric power consumption = total net electricity generation + electricity imports - electricity exports - electricity transmission and distribution losses.
Data are reported as net consumption, not gross consumption. Net consumption excludes the energy consumed by the generating units.
Metadata: https://www.eia.gov/electricity/
People using at least basic drinking water services
Data source: World Bank
Data series code: “SH.H2O.BASW.ZS”
Indicator series name: People using at least basic drinking water services (% of population)
Brief description: The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.
Data access: https://data.worldbank.org/indicator/SH.H2O.BASW.ZS
Gini index (WIID)
Data source: World Income Inequality Database (WIID)
Data series code: None - data used to fill the gaps
Indicator series name: Gini index
Brief description: Gaps in Gini index can be filled by using data from the World Income Inequality Database.
Metadata: https://www.wider.unu.edu/sites/default/files/WIID/PDF/WIID-User-Guide-30JUN2022.pdf
Data access: http://www.wider.unu.edu/project/world-income-inequality-database-wiid
CO\(_2\) emissions per unit of GDP (EDGAR)
Data source: EDGAR - Emissions Database for Global Atmospheric Research
Data series code: None - data used to fill the gaps
Indicator series name: fossil_CO2_per_GDP_by_country
Brief description: Gaps in CO\(_2\) emissions per unit of GDP can be filled by using data from the EDGAR. These data how a very high correlation with the principal data source.
In this report fossil CO2 emissions include sources from fossil fuel use (combustion, flaring), industrial processes (cement, steel, chemicals and urea) and product use. Please note that in the analysis presented in this report no short cycle carbon CO\(_2\) is included in any sector.
EDGARv7.0 includes IEA CO\(_2\) emissions (IEA Energy Balances, 2020) for 1990-2019 from fossil fuel consumption and they are extended (FT approach) up to 2021 using BP statistics keeping the same sectoral breakdown. Updates up to 2021 for cement, lime, ammonia and ferroalloys production are based on USGS statistics, urea production and consumption are based on IFA statistics, associated gas used from flaring from GGFR/NOAA, steel production from world steel and cement clinker production from UNFCCC (2022).
CO\(_2\) emissions and removals from the LULUCF sector by EU27 countries and world macro-regions covering the time period 1990-2021 are presented. The LULUCF sector includes: Forest Land (Rossi et al., in prep.), Deforestation, Drainage of Organic Soils, Other Land use and their conversion (Grassi et al., 2022).
LULUCF emissions for the year 2021 are a copy of the previous year values. Compared to previous LULUCF versions, wildfire emissions are also included using the GWIS estimates (Global Wildfire Information System).
Metadata: https://edgar.jrc.ec.europa.eu/report_2022
Data access: https://edgar.jrc.ec.europa.eu/booklet/EDGARv7.0_FT2021_fossil_CO2_booklet_2022.xlsx
Note: New report and data series released on 8 September 2023. Data links and names can change.
Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)
Data source: World Bank
Data series code: “ER.GDP.FWTL.M3.KD”
Indicator series name: Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)
Brief description: Water productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country’s sectorial activities and natural resource endowments. GDP data are from World Bank’s national accounts files. Water withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants).
The indicator “Efficiency of water use (water productivity)” is unavailable for some countries. However, a similar indicator “Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)” is available for almost all countries. Thus, missing data for the preferred indicator were populated using a regression model based on the highly correlated (0.815) variable on water productivity as the auxiliary variable.
Data access: https://data.worldbank.org/indicator/ER.GDP.FWTL.M3.KD