IndicatorName: All education staff compensation, tertiary (% of total expenditure in tertiary public institutions)

Statisticalconceptandmethodology:
All staff compensation, tertiary is calculated by dividing all staff compensation in public institutions of tertiary education by total expenditure (current and capital) in public institutions of tertiary education, and multiplying by 100. Aggregate data are based on World Bank estimates.

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).


Aggregationmethod: Median

IndicatorName: All education staff compensation, total (% of total expenditure in public institutions)

License_Type: CC BY-4.0

Longdefinition: All staff (teacher and non-teachers) compensation is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programs, and other allowances and benefits.

Periodicity: Annual

Source: UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed September 30, 2024. https://apiportal.uis.unesco.org/bdds.

Statisticalconceptandmethodology:
All staff compensation, total is calculated by dividing all staff compensation in public institutions of all levels of education by total expenditure (current and capital) in public institutions of all levels of education, and multiplying by 100. Aggregate data are based on World Bank estimates.

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).

Topic: Education: Inputs


IndicatorName: Government expenditure per student, primary (% of GDP per capita)

Longdefinition: Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita.

Source: UNESCO Institute for Statistics (http://uis.unesco.org/). Data as of February 2020.

Statisticalconceptandmethodology:
General government expenditure per student in primary education is calculated by dividing total government expenditure on primary education by the number of students at primary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.

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. Data on GDP per capita come from the World Bank.

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).


Developmentrelevance: The share of government expenditure for a specific education level allows an assessment of the priority a government assigns to a level of education relative to other levels. Enrolment and the relative costs per student between different levels of education should be also taken into account.

IndicatorName: Expenditure on primary education (% of government expenditure on education)

Limitationsandexceptions: Data disaggregated by level of education are estimates in some instances. It is often difficult to separate lower from upper secondary education expenditure, or pre-primary from primary.

Longdefinition: Expenditure on primary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments.

Statisticalconceptandmethodology:
The share of expenditure on primary education to total government expenditure on education is calculated by dividing government expenditure on primary education by total government expenditure on education (all levels combined), and multiplying by 100. Aggregate data are based on World Bank estimates.

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).


IndicatorName: Spending on teaching materials, primary (% of primary expenditure)

Longdefinition: Spending on teaching materials, primary is the percentage of public education expenditure for primary education that is spent on teaching materials. Current educational expenditure includes direct current expenditure on public educational institutions from all sources (public, private and international). Public expenditure includes government spending on educational institutions, education administration as well as subsidies for private entities (students/households and other private entities).


IndicatorName: Government expenditure per student, secondary (% of GDP per capita)

Statisticalconceptandmethodology:
General government expenditure per student in secondary education is calculated by dividing total government expenditure on secondary education by the number of students at secondary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.

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. Data on GDP per capita come from the World Bank.

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).


IndicatorName: Expenditure on secondary education (% of government expenditure on education)

Longdefinition: Expenditure on secondary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments.

Statisticalconceptandmethodology:
The share of expenditure on secondary education to total government expenditure on education is calculated by dividing government expenditure on secondary education by total government expenditure on education (all levels combined), and multiplying by 100. Aggregate data are based on World Bank estimates.

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).


IndicatorName: Spending on teaching materials, secondary (% of secondary expenditure)

Longdefinition: Spending on teaching materials, secondary is the percentage of public education expenditure for secondary education that is spent on teaching materials. Current educational expenditure includes direct current expenditure on public educational institutions from all sources (public, private and international). Public expenditure includes government spending on educational institutions, education administration as well as subsidies for private entities (students/households and other private entities).


IndicatorName: Teachers' salaries (% of current education expenditure)

Longdefinition: Teachers' salaries is the percentage of current education expenditure for teachers' salaries. Current educational expenditure includes direct current expenditure on public educational institutions from all sources (public, private and international). public expenditure includes government spending on educational institutions, education administration as well as subsidies for private entities (students/households and other private entities).


IndicatorName: Government expenditure per student, tertiary (% of GDP per capita)

Statisticalconceptandmethodology:
General government expenditure per student in tertiary education is calculated by dividing total government expenditure on tertiary education by the number of students at tertiary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.

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. Data on GDP per capita come from the World Bank.

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).


IndicatorName: Expenditure on tertiary education (% of government expenditure on education)

Longdefinition: Expenditure on tertiary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments.

Statisticalconceptandmethodology:
The share of expenditure on tertiary education to total government expenditure on education is calculated by dividing government expenditure on tertiary education by total government expenditure on education (all levels combined), and multiplying by 100. Aggregate data are based on World Bank estimates.

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).


Developmentrelevance: The share of government expenditure devoted to education allows an assessment of the priority a government assigns to education relative to other public investments, as well as a government's commitment to investing in human capital development. Countries with younger populations may spend more on education in relation to other sector such as health or social security, and vice-versa.

IndicatorName: Government expenditure on education, total (% of government expenditure)

Limitationsandexceptions: Data on government expenditure on education may refer to spending by the ministry of education only (excluding spending on educational activities by other ministries). In addition, definitions and methods of data on total general government expenditure may differ across countries.

Longdefinition: General government expenditure on education (current, capital, and transfers) is expressed as a percentage of total general government expenditure on all sectors (including health, education, social services, etc.). It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments.

Statisticalconceptandmethodology:
Expenditure on education, total (% of government expenditure) is calculated by dividing total government expenditure on education by the total government expenditure on all sectors and multiplying by 100. Aggregate data are based on World Bank estimates.

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. Data on total general government expenditure were previously collected from countries through the annual questionnaire, but are from the International Monetary Fund's World Economic Outlook database since January 2014. Therefore, current data cannot be compared with data in earlier editions.

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).


Developmentrelevance: The percentage of government expenditure on education to GDP is useful to compare education expenditure between countries and/or over time in relation to the size of their economy; A high percentage to GDP suggests a high priority for education and a capacity of raising revenues for public spending. Note that government expenditure appears lower in some countries where the private sector and/or households have a large share in total funding for education.

Limitationsandexceptions: Data may refer to spending by the ministry of education only (excluding spending on educational activities by other ministries).

Longdefinition: General government expenditure on education (current, capital, and transfers) is expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments.

Statisticalconceptandmethodology:
Government expenditure on education, total (% of GDP) is calculated by dividing total government expenditure for all levels of education by the GDP, and multiplying by 100. Aggregate data are based on World Bank estimates.

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. GDP data come from the World Bank.

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).


IndicatorName: Public spending on education, total (% of GNI, UNESCO)

Longdefinition: Public expenditure on education consists of current and capital government spending on educational institutions (both public and private), education administration as well as subsidies for private entities (students/households and other privates entities).


IndicatorName: Average years of schooling, poorest quintile (ages 15-19, DHS/MICS)

Longdefinition: Average years of schooling by age group. Age 15-19. Quintile 1 (lowest) is the number of years of formal schooling received, on average, by the quintile 1 population of the given age group.

Topic: Education: Participation


IndicatorName: Average years of schooling, richest quintile (ages 15-19, DHS/MICS)

Longdefinition: Average years of schooling by age group. Age 15-19. Quintile 5 (highest) is the number of years of formal schooling received, on average, by the quintile 5 population of the given age group.


Developmentrelevance: Women‘s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women‘s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development.

IndicatorName: Women participating in the three decisions (own health care, major household purchases, and visiting family) (% of women age 15-49)

Longdefinition: Women participating in the three decisions (own health care, major household purchases, and visiting family) is the percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in all of the three decisions (own health care, large purchases and visits to family, relatives, and friends).

Source: Demographic and Health Surveys (DHS)

Statisticalconceptandmethodology: Women participating in the three decisions (own health care, major household purchases, and visiting family) is the number of currently married women aged 15-49 who say they alone or jointly have the final say in the three decisions, expressed as percentage of currently married women age 15-49 who have been interviewed and It’s derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly have the final say in the three decisions by total number of currently married women age 15-49 who have been interviewed.

Topic: Gender: Public life & decision making


Generalcomments: This is the Sustainable Development Goal indicator 5.6.1[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Women making their own informed decisions regarding sexual relations, contraceptive use and reproductive health care  (% of women age 15-49)

Limitationsandexceptions: Current estimates of the indicator are based on currently married or in union women of reproductive age (15-49 years old) who are using any type of contraception.  In the current Demographic and Health Surveys (DHS),  the question on decision-making on use of contraception is only asked to women who are currently using contraception. Because the questions on decision- making on sexual relations and health care are restricted to women (15-49) currently married or in union, the denominator for Indicator 5.6.1 is women 15-49, who are currently married or in union and currently using contraception.  However, agreement has been reached with Macro/ICF for upcoming DHS surveys to ask the question on decision on use of contraception to all married/ in union women aged 15-49 years, whether they are currently using any contraception or not. The DHS model questionnaire for Phase 7 already includes the question on decision-making for women who are not currently using any contraception.

Longdefinition: Proportion of women ages 15-49 years (married or in union) who make their own decision on all three selected areas i.e. can say no to sexual intercourse with their husband or partner if they do not want; decide on use of contraception; and decide on their own health care. Only women who provide a “yes” answer to all three components are considered as women who “make her own decisions regarding sexual and reproductive”.

Source: Demographic and Health Surveys compiled by United Nations Population Fund. Retrieved on February 14, 2023, from the SDG Global database API (https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html).

Statisticalconceptandmethodology: Numerator of the indicator is number of married or in union women ages 15-49 who have been interviewed and satisfy all three empowerment criteria: 1)who can say "no" to sex; and 2)for whom the decision on contraception is not mainly made by the husband/partner; and 3) for whom decision on health care for themselves ins not usually made by the husband/partner or someone else.  Denominator of the indicator is the total number of women ages 15-49 who are married or in union and who have been interviewed.


Developmentrelevance:
Changes in the occupational distribution of an economy can be used to identify and analyse stages of development. In the textbook case of economic development, when labour flows from agriculture to the industrial and services sectors, these flows will be visible in the occupational distribution as well. The share of skilled agricultural and fishery workers will decrease, while rising educational attainment levels are likely to be reflected in a decreasing share of elementary occupations and rising shares of high-skilled occupational groups such as professionals and technicians.

In developed economies, which already have relatively well-educated labour forces, increases in the shares of high-skilled occupational groups are associated with the advance of the knowledge economy and additional changes in the structure of economies. Furthermore, shifts within occupational groups may be equally important. For example, the growing importance of information and communication technology (ICT) has resulted in a proliferation of ICT-related jobs. Similarly, aging of populations in many advanced economies has led to an expansion of the number of health professionals and health associate professionals.

The breakdown of the indicator by sex allows for an analysis of gender segregation of employment. Division of labour markets on the basis of sex is one of the most pervasive characteristics of labour markets around the world, which is reflected in differentials in occupational distributions between men and women. Such differentials can be analysed at detailed levels of the occupational classification,1but even at the most aggregated level, large differences by sex are evident.

Despite much progress in recent decades, gender inequalities remain pervasive in many dimensions of life - worldwide. But while disparities exist throughout the world, they are most prevalent in developing countries. Gender inequalities in the allocation of such resources as education, health care, nutrition, and political voice matter because of the strong association with well-being, productivity, and economic growth. These patterns of inequality begin at an early age, with boys routinely receiving a larger share of education and health spending than do girls, for example.

The female share of high-skilled occupations such as legislators, senior officials, and managers indicates gender segregation of employment. Women are vastly underrepresented in decision-making positions , although there is some evidence of recent improvement.

Limitationsandexceptions:
Most of the information derives from labour force surveys. In a limited number of countries the information is derived from other household surveys, population censuses, and official estimates. There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.

Countries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.

Longdefinition: Female legislators, senior officials and managers (% of total) refers to the share of legislators, senior officials and managers who are female.

Source: ILO Key Indicators of the Labour Market (KILM).

Statisticalconceptandmethodology:
The indicator for employment by occupation classifies jobs into major groups, with the groups defined by the classification that is used. Most internationally comparable data currently available are classified according to the International Standard Classification of Occupations, 1988 (ISCO-88), with the following major groups (1) Legislators, senior officials and managers; (2) Professionals; (3) Technicians and associate professionals; (4) Clerks; (5) Service workers and shop and market sales workers; (6) Skilled agricultural and fishery workers; (7) Craft and related trades workers; (8) Plant and machine operators and assemblers; (9) Elementary occupations; and (10) Armed forces.

A job is defined as a set of tasks and duties performed, or meant to be performed, by one person, including for an employer or in self-employment. An occupation is defined as a set of jobs whose main tasks and duties are characterised by a high degree of similarity.2Occupational classifications categorize all jobs into groups, which are hierarchically structured in a number of levels.

The ten major groups in ISCO-88 and the most recent revision, ISCO-08, are associated with four broad skill levels. These levels are defined in relation to the levels of education specified in the International Standard Classification of Education (ISCED). The use of ISCED categories to assist in defining the four skill levels does not imply that the skills necessary to perform the tasks and duties of a given job can be acquired only through formal education. The skills may be, and often are, acquired through (informal) training and experience. In addition, it should be emphasized that the focus in both ISCO-88 and ISCO-08 is on the skills required to carry out the tasks and duties of an occupation, and not on whether a worker employed in a particular occupation is more or less skilled than another worker in the same occupation.

Employment is defined as persons above a specified age who performed any work at all, in the reference period, for pay or profit (or pay in kind), or were temporarily absent from a job for such reasons as illness, maternity or parental leave, holiday, training or industrial dispute. Unpaid family workers who work for at least one hour should be included in the count of employment, although many countries use a higher hour limit in their definition.
Data on employment are drawn from a variety of sources including labor force surveys, household surveys, official estimates, and censuses. In a very few cases and only where other types of sources are not available, information is derived from insurance records and establishment surveys Employment data include both full-time and part-time workers.


Aggregationmethod: Weighted average

Developmentrelevance:
Despite much progress in recent decades, gender inequalities remain pervasive in many dimensions of life - worldwide. But while disparities exist throughout the world, they are most prevalent in developing countries. Gender inequalities in the allocation of such resources as education, health care, nutrition, and political voice matter because of the strong association with well-being, productivity, and economic growth. These patterns of inequality begin at an early age, with boys routinely receiving a larger share of education and health spending than do girls, for example.

Women are vastly underrepresented in decision-making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.

A strong and vibrant democracy is possible only when parliament is fully inclusive of the population it represents. Parliaments cannot consider themselves inclusive, however, until they can boast the full participation of women. This is not just about women's right to equality and their contribution to the conduct of public affairs, but also about using women's resources and potential to determine political and development priorities that benefit societies and the global community.

Generalcomments:
Relevance to gender indicator: Women are vastly underrepresented in decision making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.

This is the Sustainable Development Goal indicator 5.5.1 (a). [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Proportion of seats held by women in national parliaments (%)

Limitationsandexceptions:
The number of countries covered varies with suspensions or dissolutions of parliaments. There can be difficulties in obtaining information on by-election results and replacements due to death or resignation. These changes are ad hoc events which are more difficult to keep track of. By-elections, for instance, are often not announced internationally as general elections are. 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.

The data is compiled by the Inter-Parliamentary Union on the basis of information provided by National Parliaments. The percentages do not take into account the case of parliaments for which no data was available at that date. Information is available in all countries where a national legislature exists and therefore does not include parliaments that have been dissolved or suspended for an indefinite period.

Longdefinition: Women in parliaments are the percentage of parliamentary seats in a single or lower chamber held by women.

Source: Inter-Parliamentary Union (IPU) (www.ipu.org).  For the year of 1998, the data is as of August 10, 1998.

Statisticalconceptandmethodology:
The proportion of seats held by women in national parliaments is the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats; it is derived by dividing the total number of seats occupied by women by the total number of seats in parliament.

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 may also be filled by nomination, appointment, indirect election, rotation of members and by-election. Seats refer to the number of parliamentary mandates, or the number of members of parliament.


IndicatorName: Nonpregnant and nonnursing women can do the same jobs as men (1=yes; 0=no)

Longdefinition: Non-pregnant and non-nursing women can do the same jobs as men indicates whether there are specific jobs that women explicitly or implicitly cannot perform except in limited circumstances. Both partial and full restrictions on women’s work are counted as restrictions. For example, if women are only allowed to work in certain jobs within the mining industry, e.g., as health care professionals within mines but not as miners, this is a restriction.

Source: World Bank: Women, Business and the Law.

Topic: Gender: Participation & access


IndicatorName: Law prohibits or invalidates child or early marriage (1=yes; 0=no)

Longdefinition: Law prohibits or invalidates child or early marriage is whether there are provisions that prevent the marriage of girls, boys, or both before they reach the legal age of marriage or the age of marriage with consent, including, for example, a prohibition on registering the marriage or provisions stating that such a marriage is null and void.


Developmentrelevance: The knowledge and analysis provided by Women, Business and the Law make a strong economic case for laws that empower women. Better performance in the areas measured by the Women, Business and the Law index is associated with more women in the labor force and with higher income and improved development outcomes. Equality before the law and of economic opportunity are not only wise social policy but also good economic policy. The equal participation of women and men will give every economy a chance to achieve its potential. Given the economic significance of women's empowerment, the ultimate goal of Women, Business and the Law is to encourage governments to reform laws that hold women back from working and doing business.

Generalcomments: For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/).  For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases.

IndicatorName: Law mandates equal remuneration for females and males for work of equal value (1=yes; 0=no)

Longdefinition: The indicator measures whether there is a law that obligates employers to pay equal remuneration to male and female employees who do work of equal value. “Remuneration” refers to the ordinary, basic or minimum wage or salary and any additional emoluments payable directly or indirectly, whether in cash or in kind, by the employer to the worker and arising out of the worker’s employment. “Work of equal value” refers not only to the same or similar jobs but also to different jobs of the same value.

Notesfromoriginalsource: This is one of the 35 scored indicators.

Source: World Bank: Women, Business and the Law. https://wbl.worldbank.org/

Statisticalconceptandmethodology: Women, Business and the Law tracks progress toward legal equality between men and women in 190 economies. 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.


Generalcomments:
1. For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/).  For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases.
2. The 2024 Women, Business and the Law (WBL) report has introduced two distinct datasets, labeled as 1.0 and 2.0. The WBL data in the Gender database is based on the dataset 1.0.  This dataset maintains consistency with the indicators used in previous WBL reports from 2020 to 2023. In contrast, the WBL 2.0 dataset includes new areas of childcare and safety. For those interested in exploring the WBL 2.0 dataset, it is available on the WBL website at https://wbl.worldbank.org.

IndicatorName: Women Business and the Law Index Score (scale 1-100)

Limitationsandexceptions: 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. The woman has reached the legal age of majority and is capable of making decisions as an adult, is in good health and has no criminal record. She is a lawful citizen of the economy being examined, and she works as a cashier in the food retail sector in a supermarket or grocery store that has 60 employees. She is a cisgender, heterosexual woman in a monogamous first marriage registered with the appropriate authorities (de facto marriages and customary unions are not measured), she is of the same religion as her husband, and is in a marriage under the rules of the default marital property regime, or the most common regime for that jurisdiction, which will not change during the course of the marriage. She is not a member of a union, unless membership is mandatory. Membership is considered mandatory when collective bargaining agreements cover more than 50 percent of the workforce in the food retail sector and when they apply to individuals who were not party to the original collective bargaining agreement. Where personal law prescribes different rights and obligations for different groups of women, the data focus on the most populous group, which may mean that restrictions that apply only to minority populations are missed. Women, Business and the Law focuses solely on the ways in which the formal legal and regulatory environment determines whether women can work or open their own businesses. The data set is constructed using laws and regulations that are codified (de jure) and currently in force, therefore implementation of laws (de facto) is not measured. The data looks only at laws that apply to the private sector. These assumptions can limit the representativeness of the data for the entire population in each country. Finally, Women, Business and the Law recognizes that the laws it measures do not apply to all women in the same way. Women face intersectional forms of discrimination based on gender, sex, sexuality, race, gender identity, religion, family status, ethnicity, nationality, disability, and a myriad of other grounds. Women, Business and the Law therefore encourages readers to interpret the data in conjunction with other available research.

Longdefinition: The index 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.

Statisticalconceptandmethodology: Women, Business and the Law tracks progress toward legal equality between men and women in 190 economies. 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 violence against women legislation, 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.

Topic: Employment and Time Use


IndicatorName: Law mandates paid or unpaid maternity leave (1=yes; 0=no)

Longdefinition: Law mandates paid or unpaid maternity leave is whether there is a law mandating paid or unpaid maternity leave available only to the mother. Provisions for circumstantial leave by which an employee is entitled to a certain number of days of paid leave (usually fewer than five days) upon the birth of a child are considered paternity leave; even if the law is gender-neutral, such leave is not considered maternity leave if the law covers maternity leave elsewhere.


IndicatorName: Law prohibits discrimination in employment based on gender (1=yes; 0=no)

Longdefinition: The indicator measures whether the law generally prevents or penalizes gender-based discrimination in employment. Laws that mandate equal treatment or equality between women and men in employment are also counted for this question. It is not considered whether the laws only prohibit discrimination in one aspect of employment, such as pay or dismissal.


IndicatorName: There is legislation specifically addressing domestic violence (1=yes; 0=no)

Longdefinition: The indicator measures whether there is legislation addressing domestic violence that includes criminal sanctions or provides for protection orders for domestic violence, or the legislation addresses "harassment" that clearly leads to physical or mental harm in the context of domestic violence.


IndicatorName: Mothers are guaranteed an equivalent position after maternity leave (1=yes; 0=no)

Longdefinition: Mothers are guaranteed an equivalent position after maternity leave is whether employers of women returning from maternity leave are legally obligated to provide them with an equivalent position after maternity leave. It takes into account paid and unpaid maternity leave and captures whether the employer has a legal obligation to reinstate the returning employee in an equivalent or better position and salary than the employee had pre-leave. Where the maternity leave regime explicitly states that the employee may not be indefinitely replaced, the answer is assumed to be “Yes.” Where the maternity leave regime explicitly establishes a suspension of the employee’s contract, the answer is assumed to be “Yes.” In economies that also have parental leave and the law guarantees return after the leave to the same or an equivalent position paid at the same rate but is silent on guaranteeing the same position after maternity leave, the answer is “Yes.” The answer is “N/A” if no paid or unpaid maternity leave is available.


IndicatorName: Nondiscrimination clause mentions gender in the constitution (1=yes; 0=no)

Longdefinition: Nondiscrimination clause mentions gender in the constitution is whether there is a nondiscrimination clause in the constitution which mentions gender. For the answer to be “Yes,” the constitution must use either the word discrimination or the word nondiscrimination or even when there is a “clawback” provision granting exceptions to the nondiscrimination clause for certain areas of the law, such as inheritance, family and customary law. The answer is “No” if there is no nondiscrimination provision, or the nondiscrimination language is present in the preamble but not in an article of the constitution, or there is a provision that merely stipulates that the sexes are equal, or the sexes have equal rights and obligations. The answer is "N/A" if there is no nondiscrimination provision.


Developmentrelevance: Women often spend disproportionately more time on unpaid domestic and care work than men.  This unequal division of responsibilities is correlated with gender differences in economic opportunities, includign low female labor force participation, occupational sex segregation, and earnings diffrentials.  The need for a gender balance  in the distribution of unpaid domestic and care work has been increasingly recognized and the Sustainable Development Goals address the issue in the target 5.4.

Generalcomments: This is the Sustainable Development Goal indicator 5.4.1[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day)

Limitationsandexceptions: Data may not be strictly comparable across countries as the methods and sampling involved for data collection may differ.

Longdefinition: The average time women spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others.

Source: National statistical offices or national database and publications compiled by United Nations Statistics Division.  The data were downloaded on April 4, 2024, from the Global SDG  API: https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html

Statisticalconceptandmethodology: Proportion of time spent on unpaid domestic and care work is calculated by dividing the daily average number of hours spent on unpaid domestic and care work by 24 hours.  Data presented for this indicator are expressed as a proportion of time in a day. Weekly data is averaged over seven days of the week to obtain the daily average time.


IndicatorName: Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day)

Longdefinition: The average time men spend on household provision of services for own consumption.  Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others.

Source: National statistical offices or national database and publications compiled by United Nations Statistics Division. The data were downloaded on April 4, 2024, from the Global SDG  API: https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html


Developmentrelevance: Violence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children

Generalcomments: This is the Sustainable Development Goal indicator 5.2.1[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Proportion of women subjected to physical and/or sexual violence in the last 12 months (% of ever-partnered women ages 15-49)

Limitationsandexceptions: Collecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting.

Longdefinition: Proportion of women subjected to physical and/or sexual violence in the last 12 months is the percentage of ever partnered women age 15-49 who are subjected to physical violence, sexual violence or both by a current or former intimate partner in the last 12 months.

Source: United Nations Statistics Division (UNSD)

Topic: Gender: Health


IndicatorName: Women who believe a husband is justified in beating his wife when she argues with him (%)

Longdefinition: Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she argues with him.

Source: Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other surveys


IndicatorName: Women who believe a husband is justified in beating his wife when she burns the food (%)

Longdefinition: Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she burns the food.


IndicatorName: Women who believe a husband is justified in beating his wife when she goes out without telling him (%)

Longdefinition: Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she goes out without telling him.


IndicatorName: Spousal physical or sexual violence in last 12 months (%)

Longdefinition: Spousal physical or sexual violence is the percentage of ever-married women aged 15-49 who have experienced physical or sexual violence in the last 12 months committed by their husband or partner.

Source: Demographic and Health Surveys (DHS).


IndicatorName: Women who believe a husband is justified in beating his wife when she neglects the children (%)

Longdefinition: Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she neglects the children.


IndicatorName: Women who believe a husband is justified in beating his wife (any of five reasons) (%)

Longdefinition: Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner for any of the following five reasons: argues with him; refuses to have sex; burns the food; goes out without telling him; or when she neglects the children.


IndicatorName: Women who believe a husband is justified in beating his wife when she refuses sex with him (%)

Longdefinition: Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she refuses sex with him.


Generalcomments: Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data.

IndicatorName: Problems in accessing health care (any of the specified problems) (% of women): Q1 (lowest)

Longdefinition: Problems in accessing health care: Percentage of women who report they have big problems in accessing health care for themselves when they are sick, by type of problem. The types of problem specified are; knowing where to go for treatment, getting permission to go for treatment, getting money for treatment, distance to health facility, having to take transport, not wanting to go alone, and concern there may not be a female provider.

Source: Household Surveys (DHS, MICS)

Topic: Quintile: Health


IndicatorName: Problems in accessing health care (any of the specified problems) (% of women): Q5 (highest)


IndicatorName: Inpatient admission rate (% of population )

Longdefinition: Inpatient admission rate is the percentage of the population admitted to hospitals during a year.

Source: WHO, OECD and supplemented by country data.

Topic: Health: Health systems


Developmentrelevance: Acoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year.   Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions.

Generalcomments: This is a sex-disaggregated indicator for Sustainable Development Goal 3.5.2 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Total alcohol consumption per capita, female (liters of pure alcohol, projected estimates, female 15+ years of age)

Longdefinition: Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption.

Source: World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).

Statisticalconceptandmethodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries.

Topic: Health: Risk factors


Generalcomments: This is the Sustainable Development Goal indicator 3.5.2[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)


IndicatorName: Total alcohol consumption per capita, male (liters of pure alcohol, projected estimates, male 15+ years of age)


IndicatorName: Prevalence of anemia among women of reproductive age (% of women ages 15-49)

Longdefinition: Prevalence of anemia among women of reproductive age refers to the combined prevalence of both non-pregnant with haemoglobin levels below 12 g/dL and pregnant women with haemoglobin levels below 11 g/dL.

Source: World Health Organization, Global Health Observatory Data Repository/World Health Statistics.

Topic: Health: Nutrition


Generalcomments: Anemia is defined as a low blood haemoglobin concentration. Anaemia may result from a number of causes, with the most significant contributor being iron deficiency. Anaemia resulting from iron deficiency adversely affects cognitive and motor development and causes fatigue and low productivity. Children under age 5 and pregnant women have the highest risk for anemia.

IndicatorName: Prevalence of anemia among children (% of children ages 6-59 months)

Limitationsandexceptions: Data for blood haemoglobin concentrations are still limited, compared to other nutritional indicators such as hild anthropometry. As a result, the estimates may not capture the full variation across countries and regions.

Longdefinition: Prevalence of anemia, children ages 6-59 months, is the percentage of children ages 6-59 months whose hemoglobin level is less than 110 grams per liter, adjusted for altitude.

Statisticalconceptandmethodology: Data on anemia are compiled by the WHO, and a statistical model was used to estimate trends. WHO’s hemoglobin threshold concentration in blood was used.


IndicatorName: Prevalence of anemia among non-pregnant women (% of women ages 15-49)

Longdefinition: Prevalence of anemia, non-pregnant women, is the percentage of non-pregnant women whose hemoglobin level is less than 120 grams per liter at sea level.


IndicatorName: Condom use, population ages 15-24, female (% of females ages 15-24)

Longdefinition: Condom use, female is the percentage of the female population ages 15-24 who used a condom at last intercourse in the last 12 months.

Source: Demographic and Health Surveys, and UNAIDS.

Topic: Health: Disease prevention


IndicatorName: Condom use, population ages 15-24, male (% of males ages 15-24)

Longdefinition: Condom use, male is the percentage of the male population ages 15-24 who used a condom at last intercourse in the last 12 months.


Aggregationmethod: Sum

Generalcomments: Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

IndicatorName: Number of deaths ages 5-9 years

Limitationsandexceptions: Complete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.

Longdefinition: Number of deaths of children ages 5-9 years

Source: Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.

Topic: Health: Mortality


IndicatorName: Number of deaths ages 5-14 years

Longdefinition: Number of deaths of children ages 5-14 years


IndicatorName: Number of deaths ages 10-14 years

Longdefinition: Number of deaths of adolescents ages 10-14 years


IndicatorName: Number of deaths ages 15-19 years

Longdefinition: Number of deaths of adolescents ages 15-19 years


IndicatorName: Number of deaths ages 20-24 years

Longdefinition: Number of deaths of youths ages 20-24 years


IndicatorName: Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total)

Limitationsandexceptions: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.

Longdefinition: Cause of death refers to the share of all deaths for all ages by underlying causes. Communicable diseases and maternal, prenatal and nutrition conditions include infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.

Source: Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death

Statisticalconceptandmethodology: Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in undercovered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source.


IndicatorName: Number of infant deaths

Longdefinition: Number of infants dying before reaching one year of age.


IndicatorName: Cause of death, by injury (% of total)

Longdefinition: Cause of death refers to the share of all deaths for all ages by underlying causes. Injuries include unintentional and intentional injuries.

Statisticalconceptandmethodology: Data on cause of death are compiled by the WHO, based mainly on data from national vital registry systems, as well as sample registration systems, population laboratories, and epidemiological analysis of specific conditions. Data are classified based on the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Data have been carefully analyzed to take into account incomplete coverage of vital registration and the likely differences in cause of death patterns that would be expected in under-covered and often poorer subpopulations. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, consult the original source.


IndicatorName: Number of under-five deaths

Longdefinition: Number of children dying before reaching age five.


IndicatorName: Cause of death, by non-communicable diseases (% of total)

Longdefinition: Cause of death refers to the share of all deaths for all ages by underlying causes. Non-communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.


Generalcomments:
Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

This indicator is related to Sustainable Development Goal 3.2.2 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Number of neonatal deaths

Longdefinition: Number of neonates dying before reaching 28 days of age.


Developmentrelevance: Mortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.

IndicatorName: Probability of dying among children ages 5-9 years (per 1,000)

Longdefinition: Probability of dying between age 5-9 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified year.

Statisticalconceptandmethodology:
The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A "complete" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.

Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.


Developmentrelevance: Mortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.

IndicatorName: Probability of dying at age 5-14 years (per 1,000 children age 5)

Longdefinition: Probability of dying between age 5-14 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified year.


IndicatorName: Probability of dying among adolescents ages 10-14 years (per 1,000)

Longdefinition: Probability of dying between age 10-14 years of age expressed per 1,000 adolescents age 10, if subject to age-specific mortality rates of the specified year.


IndicatorName: Probability of dying among adolescents ages 15-19 years (per 1,000)

Longdefinition: Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year.


IndicatorName: Probability of dying among youth ages 20-24 years (per 1,000)

Longdefinition: Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, if subject to age-specific mortality rates of the specified year.


IndicatorName: Women's share of population ages 15+ living with HIV (%)

Limitationsandexceptions: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information.

Longdefinition: Prevalence of HIV is the percentage of people who are infected with HIV. Female rate is as a percentage of the total population ages 15+ who are living with HIV.

Source: UNAIDS estimates.

Statisticalconceptandmethodology:
HIV prevalence rates reflect the rate of HIV infection in each country's population. Low national prevalence rates can be misleading, however. They often disguise epidemics that are initially concentrated in certain localities or population groups and threaten to spill over into the wider population. In many developing countries most new infections occur in young adults, with young women especially vulnerable.

Data on HIV are from the Joint United Nations Programme on HIV/AIDS (UNAIDS). Changes in procedures and assumptions for estimating the data and better coordination with countries have resulted in improved estimates of HIV and AIDS. The models, which are routinely updated, track the course of HIV epidemics and their impact, making full use of information in HIV prevalence trends from surveillance data as well as survey data. The models take into account reduced infectivity among people receiving antiretroviral therapy (which is having a larger impact on HIV prevalence and allowing HIV-positive people to live longer) and allow for changes in urbanization over time in generalized epidemics. The estimates include plausibility bounds, which reflect the certainty associated with each of the estimates.


IndicatorName: Prevalence of HIV, total (% of population ages 15-49)

Longdefinition: Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV.


Generalcomments: Relevance to gender indicator: Child mortality captures the effect of gender discrimination better than infant mortality, as malnutrition and medical interventions have significant impacts on this age group. Where female child mortality is higher, girls are likely to have less access to resources than boys.

IndicatorName: Mortality rate, female child (per 1,000 female children age one)

Longdefinition: Child mortality rate is the probability of dying between the exact ages of one and five, if subject to age-specific mortality rates of the specified year. The probability is expressed as a rate per 1,000.

Source: Demographic and Health Surveys by ICF International, Multiple Indicators Cluster Surveys by UNICEF, Reproductive Health Surveys by U.S. Center for Disease Control, and Family Health Surveys by Pan Arab Project for Family Health. See footnotes for a source.


IndicatorName: Mortality rate, male child (per 1,000 male children age one)


Generalcomments:
Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

This is the Sustainable Development Goal indicator 3.2.1[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality rate, under-5 (per 1,000 live births)

Longdefinition: Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.


Generalcomments:
Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

This is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality rate, under-5, female (per 1,000 live births)

Longdefinition: Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year.


IndicatorName: Mortality rate, under-5, male (per 1,000 live births)

Longdefinition: Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year.


IndicatorName: Under-5 mortality rate (per 1,000 live births): Q1 (lowest)

Longdefinition: Under-5 mortality rate: Number of deaths to children under age five years per 1000 live births, based on experience during the reference period before the survey. The reference period is ten years preceding the survey for DHS surveys, and the reference period varies for MICS surveys (often three to five years preceding the survey).


IndicatorName: Under-5 mortality rate (per 1,000 live births): Q5 (highest)


Generalcomments: This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, female (%)

Longdefinition: Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes,  or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).


IndicatorName: Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, male (%)


Generalcomments: This is the Sustainable Development Goal indicator 3.4.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)


Generalcomments:
Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries.

Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

This is the Sustainable Development Goal indicator 3.2.2 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality rate, neonatal (per 1,000 live births)

Longdefinition: Neonatal mortality rate is the number of neonates dying before reaching 28 days of age, per 1,000 live births in a given year.


IndicatorName: Knowledge of contraception (any method) (% of married women): Q1 (lowest)

Longdefinition: Knowledge of contraception: Percentage of currently married women who know at least one contraceptive method and at least one modern contraceptive method.


IndicatorName: Knowledge of contraception (any method) (% of married women): Q5 (highest)


Generalcomments: This is the Sustainable Development Goal indicator 3.7.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Demand for family planning satisfied by modern methods (% of married women with demand for family planning)

Longdefinition: Demand for family planning satisfied by modern methods refers to the percentage of married women ages 15-49 years whose need for family planning is satisfied with modern methods.

Topic: Health: Reproductive health


Developmentrelevance:
Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities.

Lack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact.

Women and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families.

Many international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings.

IndicatorName: People using at least basic drinking water services, rural (% of rural population)

Limitationsandexceptions: National, regional and income group estimates are made when data are available for at least 50 percent of the population.

Longdefinition: 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.

Source: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).

Statisticalconceptandmethodology: Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys.  WHO/UNICEF defines a basic drinking water service 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.


IndicatorName: People using at least basic drinking water services, urban (% of urban population)


IndicatorName: People using at least basic drinking water services (% of population)


Developmentrelevance:
Water is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities.

Lack of access to adequate water contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and include diarrhea, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improvement of access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity.

Women and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families.

Many international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of improved drinking water include higher economic productivity, more education, and health-care savings.

IndicatorName: Improved water source, rural (% of rural population with access)

Limitationsandexceptions:
Please note that the data for this indicator have not been updated since 2015.  The WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation has introduced updated water and sanitation indicators.   For the most recent data on water access in rural areas, please see the following indicators: People using safely managed drinking water services, rural (% of rural population) (SH.H2O.SMDW.RU.ZS) and People using basic drinking water services, rural (% of rural population) (SH.H2O.BASW.RU.ZS).

The data on access to an improved water source measure the percentage of the population with ready access to water for domestic purposes.

Access to drinking water from an improved source does not ensure that the water is safe or adequate, as these characteristics are not tested at the time of survey. But improved drinking water technologies are more likely than those characterized as unimproved to provide safe drinking water and to prevent contact with human excreta. While information on access to an improved water source is widely used, it is extremely subjective, and such terms as safe, improved, adequate, and reasonable may have different meanings in different countries despite official WHO definitions (see Definitions). Even in high-income countries treated water may not always be safe to drink. Access to an improved water source is equated with connection to a supply system; it does not take into account variations in the quality and cost (broadly defined) of the service.

Longdefinition: Access to an improved water source, rural, refers to the percentage of the rural population using an improved drinking water source. The improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection).

Source: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/).

Statisticalconceptandmethodology:
The data are derived by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on national censuses and nationally representative household surveys. The coverage rates for water and sanitation are based on information from service users on the facilities their households actually use rather than on information from service providers, which may include nonfunctioning systems.

WHO/UNICEF define an improved drinking-water source as one that, by nature of its construction or through active intervention, is protected from outside contamination, in particular from contamination with fecal matter. Improved water sources include piped water into dwelling, plot or yard; piped water into neighbor's plot; public tap/standpipe; tube well/borehole; protected dug well; protected spring; and rainwater.


IndicatorName: Improved water source, urban (% of urban population with access)

Limitationsandexceptions:
Please note that the data for this indicator have not been updated since 2015.  The WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation has introduced updated water and sanitation indicators.  For the most recent data on water access in urban areas,  please see the following indicators: People using safely managed drinking water services, urban (% of urban population) (SH.H2O.SMDW.UR.ZS) and
People using basic drinking water services, urban (% of urban population) (SH.H2O.BASW.UR.ZS).

The data on access to an improved water source measure the percentage of the population with ready access to water for domestic purposes.

Access to drinking water from an improved source does not ensure that the water is safe or adequate, as these characteristics are not tested at the time of survey. But improved drinking water technologies are more likely than those characterized as unimproved to provide safe drinking water and to prevent contact with human excreta. While information on access to an improved water source is widely used, it is extremely subjective, and such terms as safe, improved, adequate, and reasonable may have different meanings in different countries despite official WHO definitions (see Definitions). Even in high-income countries treated water may not always be safe to drink. Access to an improved water source is equated with connection to a supply system; it does not take into account variations in the quality and cost (broadly defined) of the service.

Longdefinition: Access to an improved water source, urban, refers to the percentage of the urban population using an improved drinking water source. The improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection).


IndicatorName: Improved water source (% of population with access)

Limitationsandexceptions:
Please note that the data for this indicator have not been updated since 2015.  The WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation has introduced updated water and sanitation indicators.  For the most recent data on water access, please see the following indicators: People using safely managed drinking water services (% of population) (SH.H2O.SMDW.ZS) and People using basic drinking water services (% of population) (SH.H2O.BASW.ZS).

The data on access to an improved water source measure the percentage of the population with ready access to water for domestic purposes.

Access to drinking water from an improved source does not ensure that the water is safe or adequate, as these characteristics are not tested at the time of survey. But improved drinking water technologies are more likely than those characterized as unimproved to provide safe drinking water and to prevent contact with human excreta. While information on access to an improved water source is widely used, it is extremely subjective, and such terms as safe, improved, adequate, and reasonable may have different meanings in different countries despite official WHO definitions (see Definitions). Even in high-income countries treated water may not always be safe to drink. Access to an improved water source is equated with connection to a supply system; it does not take into account variations in the quality and cost (broadly defined) of the service.

Longdefinition: Access to an improved water source refers to the percentage of the population using an improved drinking water source. The improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot or yard), and other improved drinking water sources (public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection).


Generalcomments:
Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.

This is a disaggregated indicator for Sustainable Development Goal 6.1.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: People using safely managed drinking water services, rural (% of rural population)

Limitationsandexceptions: In order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality).  Many countries lack data on one or more elements of safely managed drinking water.  The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability).  Regional and income group estimates are made when data are available for at least 30 percent of the population.

Longdefinition: The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from faecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.

Statisticalconceptandmethodology: Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys.  WHO/UNICEF defines a safely managed drinking water as an improved water source that is accessible on premises, available when needed and free from faecal and priority chemical contamination.  Improved water sources include: piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.


IndicatorName: People using safely managed drinking water services, urban (% of urban population)


Generalcomments:
Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.

This is the Sustainable Development Goal indicator 6.1.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: People using safely managed drinking water services (% of population)


IndicatorName: Children (0-14) living with HIV

Longdefinition: Children living with HIV refers to the number of children ages 0-14 who are infected with HIV.


Generalcomments: In many developing countries most new infections occur in young adults, with young women especially vulnerable.

IndicatorName: Prevalence of HIV, female (% ages 15-24)

Longdefinition: Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group.


Generalcomments: In many developing countries most new infections occur in young adults, with young women being especially vulnerable.

IndicatorName: Prevalence of HIV, male (% ages 15-24)

Longdefinition: Prevalence of HIV, male is the percentage of males who are infected with HIV. Youth rates are as a percentage of the relevant age group.


IndicatorName: Antiretroviral therapy coverage (% of people living with HIV)

Longdefinition: Antiretroviral therapy coverage indicates the percentage of all people living with HIV who are receiving antiretroviral therapy.

Statisticalconceptandmethodology:
Data on HIV are from the Joint United Nations Programme on HIV/AIDS (UNAIDS). Changes in procedures and assumptions for estimating the data and better coordination with countries have resulted in improved estimates of HIV and AIDS.

Antiretroviral therapy has led to huge reductions in death and suffering of people with advanced HIV infection.


Developmentrelevance: Despite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world.  Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission.

Generalcomments: This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Adults (ages 15-49) newly infected with HIV

Longdefinition: Number of adults (ages 15-49) newly infected with HIV.

Previous_Indicator_Name: Number of adults (ages 15+) newly infected with HIV - Indicator name and definition have been changed since October 2020.


IndicatorName: Children (ages 0-14) newly infected with HIV

Longdefinition: Number of children (ages 0-14) newly infected with HIV.


IndicatorName: Adults (ages 15+) and children (ages 0-14) newly infected with HIV

Longdefinition: Number of adults (ages 15+) and children (ages 0-14) newly infected with HIV.


Generalcomments: This is the Sustainable Development Goal indicator 3.3.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Incidence of HIV, all (per 1,000 uninfected population)

Longdefinition: Number of new HIV infections among uninfected populations expressed per 1,000 uninfected population in the year before the period.

Statisticalconceptandmethodology: Data on incidence of HIV are from the Joint United Nations Programme on HIV/AIDS. Because of challenges in collecting direct measures of HIV incidence, modelled estimates are used (the Spectrum software). The models incorporate data on HIV prevalence from surveys of the general population, antenatal clinic attendees, and populations at increased risk of contracting HIV (such as sex workers, men who have sex with men, and people who inject drugs) and on the number of people receiving antiretroviral therapy, which will increase the prevalence of HIV because people living with HIV now survive longer. In countries with high-quality health information systems the models are also informed by case reporting and vital registration data.


IndicatorName: Young people (ages 15-24) newly infected with HIV

Longdefinition: Number of young people (ages 15-24) newly infected with HIV.


Generalcomments: This is an age-disaggregated indicator for Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24)

Longdefinition: Number of new HIV infections among uninfected populations ages 15-24 expressed per 1,000 uninfected population ages 15-24 in the year before the period.


IndicatorName: Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49)

Longdefinition: Number of new HIV infections among uninfected populations ages 15-49 expressed per 1,000 uninfected population in the year before the period.


IndicatorName: Antiretroviral therapy coverage for PMTCT (% of pregnant women living with HIV)

Longdefinition: Percentage of pregnant women with HIV who receive antiretroviral medicine for prevention of mother-to-child transmission (PMTCT).


Generalcomments: Data disaggregated by wealth quintile provide insights into health differentials by socioeconomic status and allow problems particular to the poor, such as unequal access to health care to be identified. The table shows the estimates for the poorest and richest quintiles only; the full set of estimates for up to 70 indicators is available at http://data.worldbank.org/data-catalog/healthnutrition-population-statistics. The estimates in the table are based on household survey data, which may refer to a period preceding the survey date or use a definition or methodology different from the estimates in the other tables. Thus the estimates may differ, and caution should be exercised in using the data.

IndicatorName: Vaccinations (all vaccinations) (% of children ages 12-23 months): Q1 (lowest)

Longdefinition: Vaccinations: Percentage of children 12-23 months who have received specific vaccines by the time of the survey (according to the vaccination card or the mother's report). Children with all vaccinations refer children who have received BCG, measles, and three doses each of DPT and polio vaccine (excluding polio 0). Some MICS surveys refer children in different age groups (e.g. 18-29 months, or 15-26 months).


IndicatorName: Vaccinations (all vaccinations) (% of children ages 12-23 months): Q5 (highest)