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.
3. The indicator name has been changed as of February 2021.
Previous indicator name: Paid leave of at least 14 weeks is available to women (1=yes; 0=no)

IndicatorName: Paid leave of at least 14 weeks available to mothers (1=yes; 0=no)

Longdefinition: The indicator measures  whether women are legally entitled to at least 14 weeks (98 calendar days) of paid leave for the birth of a child through maternity leave, parental leave or a combination of both.

Notesfromoriginalsource: This is one of the 35 scored indicators. The underlying inidcator is "Length of paid maternity leave (days)" (SH.MMR.LEVE).


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:
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: The government administers 100 percent of maternity leave benefits (1=yes; 0=no)

License_Type: CC BY-4.0

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 indicator measures whether leave benefits are fully administered by a government entity, including compulsory social insurance schemes (such as social security), public funds, government-mandated private insurance or employer reimbursement of any maternity leave benefits paid directly to an employee.

Notesfromoriginalsource: This is one of the 35 scored indicators.

Periodicity: Annual

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 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


Aggregationmethod: Weighted average

IndicatorName: Lifetime risk of maternal death (1 in: rate varies by country)

Limitationsandexceptions: The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The probability cannot be assumed to provide an exact estimate of risk of maternal death.

Longdefinition: Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death.

Source: WHO, UNICEF, UNFPA, World Bank Group, and UNDESA/Population Division. Trends in Maternal Mortality 2000 to 2020. Geneva, World Health Organization, 2023

Statisticalconceptandmethodology:
Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.

Maternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation.

The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.

In countries with a high risk of maternal death, many girls die before reaching reproductive age. Lifetime risk of maternal mortality refers to the probability that a 15-year-old girl will eventually die due to a maternal cause.

Topic: Health


IndicatorName: Lifetime risk of maternal death (%)


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.
3. The indicator name has been changed as of February 2021.
Previous indicator name: Length of paid shared parental leave (days)

IndicatorName: Length of paid shared parental leave (calendar days)

Longdefinition: The indicator refers to parental leave that can be shared amongst the parents as they choose. Parental leave refers to leave available equally to parents—regardless of gender—for the purpose of childcare immediately following maternity and paternity leave OR instead of maternity and paternity leave.

Notesfromoriginalsource: This indicator is additional to the 35 scored indicators, and associated to the following indicator "There is paid parental leave (1=yes; 0=no)" (SH.PAR.LEVE.AL).


IndicatorName: There is paid parental leave (1=yes; 0=no)

Longdefinition: The indicator measures whether both parents are legally entitled to some form of full-time paid parental leave either shared between mother and father or as an individual entitlement that each can take regardless of the other.

Notesfromoriginalsource: This is one of the 35 scored indicators.  The underlying inidcators are as follows: 1. Length of paid shared parental leave (days) (SH.PAR.LEVE), 2. Length of paid parental leave for mother (days) (SH.PAR.LEVE.FE), 3. Length of paid parental leave for father (days) (SH.PAR.LEVE.MA)


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.
The indicator name has been changed as of February 2021.
3. Previous indicator name: Length of paid parental leave for mother (days)

IndicatorName: Length of paid parental leave for mother (calendar days)

Longdefinition: The indicator refers to days of parental leave reserved specifically for mothers—through “use-it-or-lose-it” policies or mothers’ quotas; or days of parental leave that employees are individually entitled to.


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.
3.The indicator name has been changed as of February 2021.
Previous indicator name: Length of paid parental leave for father (days)

IndicatorName: Length of paid parental leave for father (calendar days)

Longdefinition: The indicator refers to days of parental leave reserved specifically for fathers—that is, through “use-it-or-lose-it” policies or fathers’ quotas; or days of parental leave that employees are individually entitled to.


IndicatorName: Prevalence of anemia among pregnant women (%)

Limitationsandexceptions:
Data should be used with caution because surveys differ in quality, coverage, age group interviewed, and treatment of missing values across countries and over time.

Data on anemia are compiled by the WHO based mainly on nationally representative surveys, which measure hemoglobin in the blood. WHO's hemoglobin thresholds are then used to determine anemia status based on age, sex, and physiological status.

Longdefinition: Prevalence of anemia, pregnant women, is the percentage of pregnant women whose hemoglobin level is less than 110 grams per liter at sea level.

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

Statisticalconceptandmethodology: Anemia is a condition in which the number of red blood cells or their oxygen-carrying capacity is insufficient to meet physiologic needs, which vary by age, sex, altitude, smoking status, and pregnancy status. In its severe form it is associated with fatigue, weakness, dizziness, and drowsiness. Children under age 5 and pregnant women have the highest risk for anemia.


IndicatorName: Prevalence of syphilis (% of women attending antenatal care)

Longdefinition: Percentage of women attending antenatal care seropositive for syphilis

Source: World Health Organization's Global Health Observatory Data Repository


Generalcomments:
This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].

Previous indicator name: Smoking prevalence, total (ages 15+)
The previous indicator excluded smokeless tobacco use, while the current indicator includes. The indicator name and definition were updated in December, 2020.

IndicatorName: Prevalence of current tobacco use (% of adults)

Limitationsandexceptions: Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution.

Longdefinition: The percentage of the population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population.

Periodicity: Biennial

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

Statisticalconceptandmethodology:
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.

A statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women.

The model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate.
Depending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015).


Generalcomments:
This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].

Previous indicator name: Smoking prevalence, females (% of adults)
The previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020.

IndicatorName: Prevalence of current tobacco use, females (% of female adults)

Longdefinition: The percentage of the female population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population.


Generalcomments:
This is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].

Previous indicator name: Smoking prevalence, males (% of adults)
The previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020.

IndicatorName: Prevalence of current tobacco use, males (% of male adults)

Longdefinition: The percentage of the male population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population.

Statisticalconceptandmethodology:
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.

Smoking is the most common form of tobacco use and the prevalence of smoking is therefore a good measure of the tobacco epidemic. (Corrao MA, Guindon GE, Sharma N, Shokoohi  DF (eds). Tobacco Control Country Profiles, 2000, American Cancer Society, Atlanta.) Tobacco use causes heart and other vascular diseases and cancers of the lung and other organs. Given the long delay between starting to smoke and the onset of disease, the health impact of smoking will increase rapidly only in the next few decades. The data presented are age-standardized rates for adults ages 15 and older from the WHO.


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.
3. The indicator name has been changed as of February 2021.
Previous indicator name: Length of paid paternity leave (days)

IndicatorName: Length of paid paternity leave (calendar days)

Longdefinition: The indicator refers to leave available only to the father for the birth of a child.

Notesfromoriginalsource: This indicator is additional to the 35 scored indicators, and associated to the following indicator "Paid leave is available to fathers (1=yes; 0=no)" (SH.PTR.LEVE.AL).


IndicatorName: Paid leave is available to fathers (1=yes; 0=no)

Longdefinition: The indicator measures whether fathers are legally entitled to at least one day of paid leave for the birth of a child, or if the law reserves a portion of paid parental leave specifically for fathers—that is, through “use-it-or-lose-it” policies or fathers’ quotas; or if fathers are individually entitled to paid parental leave.

Notesfromoriginalsource: This is one of the 35 scored indicators.  The underlying inidcator is "Length of paid paternity leave (days)" (SH.PTR.LEVE).


Developmentrelevance: Air pollution is one of the biggest environmental risks to health.  According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year.  Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections.  The majority of the burden is borne by populations in low and middle income countries.

Generalcomments: This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality rate attributed to household and ambient air pollution, age-standardized, female (per 100,000 female population)

Limitationsandexceptions: Estimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions

Longdefinition: Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized.  Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years).

Statisticalconceptandmethodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g.  the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed  to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003).

Topic: Environment


IndicatorName: Mortality rate attributed to household and ambient air pollution, age-standardized, male (per 100,000 male population)


Generalcomments: This is the Sustainable Development Goal indicator 3.9.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)


Generalcomments: Good prenatal and postnatal care improve maternal health and reduce maternal and infant mortality.

IndicatorName: Pregnant women receiving prenatal care of at least four visits (% of pregnant women)

Limitationsandexceptions: For the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources.

Longdefinition: Pregnant women receiving prenatal care, at least four times, are the percentage of women attended at least four times during pregnancy by skilled health personnel for reasons related to pregnancy.

Source: UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys.

Statisticalconceptandmethodology:
Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.

Good prenatal and postnatal care improves maternal health and reduces maternal and infant mortality. However, indicators on use of antenatal care services provide no information on the content or quality of the services. Data on antenatal care are obtained mostly from household surveys, which ask women who have had a live birth whether and from whom they received antenatal care.


IndicatorName: Pregnant women receiving prenatal care (%)

Longdefinition: Pregnant women receiving prenatal care are the percentage of women attended at least once during pregnancy by skilled health personnel for reasons related to pregnancy.


Developmentrelevance:
Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.

The share of births attended by skilled health staff is an indicator of a health system's ability to provide adequate care for pregnant women.

Generalcomments:
Assistance by trained professionals during birth reduces the incidence of maternal deaths during childbirth. The share of births attended by skilled health staff is an indicator of a health system’s ability to provide adequate care for pregnant women.

This is the Sustainable Development Goal indicator 3.1.2[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Births attended by skilled health staff (% of total)

Longdefinition: Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns.


Developmentrelevance: Female genital mutilation (FGM) is defined as all procedures involving partial or total removal of the female external genitalia or other injury to the female genital organs for non-medical reasons. It doesn’t provide any health benefits, but rather causes serious risks on women’s health including chronic infections and pain, menstrual problems, and complications in childbirth. FGM has been criticized as a violation of women’s basic human rights in the international community, and the elimination of FGM has been set as one of Sustainable Development Goals targets (5.3).  However, the United Nations Children's Fund (UNICEF) estimates that at least 200 million women and girls have undergone FGM in the world. The attitudes about FGM among people where FGM has been practiced vary widely across countries.

IndicatorName: Women who believe that female genital mutilation should not be continued (% of women ages 15-49 who have heard about FGM)

Longdefinition: Percentage of women ages 15-49 who have heard about female genital mutilation and think the practice should end.

Source: UNICEF DATA (http://www.data.unicef.org/);  Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS), and other surveys.

Statisticalconceptandmethodology:
Demographic and Health Surveys (DHS) are nationally-representative household surveys that provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. The surveys use model quesionnaires that contain a core set of questions that span all questionnaires, in addition to country-specific questions. Surveys have large sample sizes (usually between 5,000 and 30,000 households) and are typically conducted about every 5 years, to allow comparisons over time. The time to complete a survey depends on survey type, survey instruments, and sample size. Eligible households and individuals are usually obtained in a sampling frame from the National Statistics Office of the country. The time it takes to conduct all interviews is on average 18-20 months.

Data on domestic violence in DHS surveys comes from an optional module of questions. Thus, indicators in this chapter are available for some, but not all countries. Additionally, The DHS Program, in accordance with the WHO guidelines “Putting Women First: Ethical and Safety Recommendations for Research on Domestic Violence against Women” World Health Organization, 2001, randomly selects only one woman per household among all eligible women in the household selected for the individual questionnaire for this module. Thus, the number of women who have information for domestic violence will always be less than the number of women selected for the complete DHS individual interview.

 A large part of the domestic violence module asks about violence perpetrated by the current husband/partner for women who are currently married and the most recent husband/partner for women who are currently divorced, separated or widowed. Thus, women who have been ever-married are asked many more questions about their experience of violence than women who have never been married. Note also that ever-married women are women who self-report as being married, divorced, separated, or widowed, or living with or having ever lived with a man as if married. Thus, a “partner” is a man with whom the respondent lives with or lived with as if married. A previous husband/partner is a husband/partner other than the current husband/partner for currently married women and the most recent husband/partner for divorced, separated, or widowed women.

Topic: Violence

Unitofmeasure: Percent


IndicatorName: Men who believe that female genital mutilation should not be continued (% of men who have heard about FGM)

Longdefinition: Percentage of men who have heard about female genital mutilation and think the practice should end.


IndicatorName: Women who believe religion requires female genital mutilation (% of women ages 15-49 who have heard about FGM)

Longdefinition: Percentage of women ages 15-49 who have heard about female genital mutilation (FGM) and whose opinion is that religion requires FGM.


IndicatorName: Men who believe religion requires female genital mutilation  (% of men who have heard about FGM)

Longdefinition: Percentage of men who have heard about female genital mutilation (FGM) and whose opinion is that religion requires FGM.


Generalcomments:
This is a disaggregated indicator for Sustainable Development Goal 5.3.2
[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Female genital mutilation prevalence (%): Q1 (lowest)

Longdefinition: Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons. Each wealth quintile represents one fifth of households with quintile 1 being the poorest 20 percent of households and quintile 5 being the richest 20 percent of households.


IndicatorName: Female genital mutilation prevalence (%): Q2


IndicatorName: Female genital mutilation prevalence (%): Q3


IndicatorName: Female genital mutilation prevalence (%): Q4


IndicatorName: Female genital mutilation prevalence (%): Q5 (highest)


Generalcomments: This is the Sustainable Development Goal indicator 5.3.2[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Female genital mutilation prevalence (%)

Longdefinition: Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons.


DataQuality: UNICEF, WHO and the World Bank undertake a joint review for each potential primary data source included in the joint database. The group conducts a review when (at minimum) a final report with full methodological details and results are available, as well as (ideally) a data quality assessment flagging potential limitation. When the raw data are available, they are analysed using the Anthro Survey Analyzer software to produce a standard set of results and data quality outputs against which the review is conducted. Comments are documented in a standard review template extracting methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., weight and height distributions, percentage of cases that were flagged as implausible according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from the data source under review generated based on the standard recommended methodology. These estimates are compared against the reported values, as well as against those from other data sources already included in the JME dataset, to assess the plausibility of the trend before including the new point. Reports that are preliminary, or that lack key details on methodology or results, cannot be reviewed and are left pending until full information is available.

Derivationmethod: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old and WHO Anthro Survey Analyser.

Generalcomments: Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

IndicatorName: Prevalence of underweight, weight for age, female (% of children under 5)

Limitationsandexceptions: Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels.

Longdefinition: Prevalence of underweight, female, is the percentage of girls under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards.

Otherweblinks:
Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO.
https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/
https://www.who.int/publications/i/item/9789241515559

WHO Anthro Survey Analyzer - https://www.who.int/tools/child-growth-standards/software

Source: UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.


IndicatorName: Prevalence of underweight, weight for age, male (% of children under 5)

Longdefinition: Prevalence of underweight, male, is the percentage of boys under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards.


Aggregationmethod: Linear mixed-effect model estimates

IndicatorName: Prevalence of underweight, weight for age (% of children under 5)

Longdefinition: Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards.


Generalcomments: This indicator represents the risk associated with each pregnancy and is also a Sustainable Development Goal Indicator (3.1.1) for monitoring maternal health.

IndicatorName: Maternal mortality ratio (modeled estimate, per 100,000 live births)

Limitationsandexceptions: The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality.

Longdefinition: Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).

Notesfromoriginalsource: Estimates of maternal mortality are presented along with upper and lower  limits of intervals (see footnote) designed to depict the uncertainty of estimates. The intervals are the product of a detailed probabilistic evaluation of the uncertainty attributable to the various components of the estimation process. For estimates derived from the multilevel regression model, the components of uncertainty were divided into two groups: those reflected within the regression model (internal sources), and those due to assumptions or calculations that occur outside the model (external sources). Estimates of the total uncertainty reflect a combination of these various sources.

Statisticalconceptandmethodology:
Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.

Maternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation.

The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.


IndicatorName: Maternal mortality ratio (national estimate, per 100,000 live births)

Limitationsandexceptions: Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality. Some of the figures shown were adjusted by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) to enhance reliability, so that they may differ from the published figures from sources such as household surveys.

Longdefinition: Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births.

Source: The country data compiled, adjusted and used in the estimation model by the Maternal Mortality Estimation Inter-Agency Group (MMEIG). The country data were compiled from the following sources:  civil registration and vital statistics; specialized studies on maternal mortality; population based surveys and censuses; other available data sources including data from surveillance sites.

Statisticalconceptandmethodology:
Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.

Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation.

The national estimates of maternal mortality ratios are based on national surveys, vital registration records, and surveillance data or are derived from community and hospital records.


IndicatorName: Prevalence of obesity, female (% of female population ages 18+)

Longdefinition: Prevalence of obesity adult is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is 30 kg/m² or higher. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters.

Source: World Health Organization (WHO):Global Health Observatory Data Repository


IndicatorName: Prevalence of obesity, male (% of male population ages 18+)

Source: World Health Organization (WHO): Global Health Observatory Data Repository


Generalcomments: This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: People practicing open defecation, rural (% of rural population)

Longdefinition: People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste.

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

Topic: Economic and Social Context


IndicatorName: People practicing open defecation, urban (% of urban population)


Generalcomments: This is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: People practicing open defecation (% of population)


IndicatorName: Prevalence of overweight, female (% of female adults)

Longdefinition: Prevalence of overweight female adults is the percentage of females ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters.


IndicatorName: Prevalence of overweight, male (% of male adults)

Longdefinition: Prevalence of overweight male adults is the percentage of males ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters.


IndicatorName: Prevalence of overweight (% of adults)

Longdefinition: Prevalence of overweight adults is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters.


Generalcomments: Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.

IndicatorName: Prevalence of overweight, weight for height, female (% of children under 5)

Longdefinition: Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.


IndicatorName: Prevalence of overweight, weight for height, male (% of children under 5)

Longdefinition: Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.


Aggregationmethod: See SH.STA.OWGH.ME.ZS for aggregation

IndicatorName: Prevalence of overweight, weight for height (% of children under 5)

Longdefinition: Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.


Developmentrelevance: Mortality rates due to unintentional poisoning remains relatively high in low income countries.  This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system.

Generalcomments: This is the Sustainable Development Goal indicator 3.9.3[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality rate attributed to unintentional poisoning (per 100,000 population)

Limitationsandexceptions: Some countries do not have death registration data or sample registration systems.  The estimates on this indicator need to be completed with other type of information for these countries.

Longdefinition:
Mortality rate attributed to unintentional poisonings is the number of deaths from unintentional poisonings in a year per 100,000 population.  Unintentional poisoning can
be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.


Generalcomments: This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality rate attributed to unintentional poisoning, female (per 100,000 female population)

Longdefinition: Mortality rate attributed to unintentional poisonings is the number of female deaths from unintentional poisonings in a year per 100,000 female population.  Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.


IndicatorName: Mortality rate attributed to unintentional poisoning, male (per 100,000 male population)

Longdefinition:
Mortality rate attributed to unintentional poisonings is the number of male deaths from unintentional poisonings in a year per 100,000 male population. Unintentional poisoning can
be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.


IndicatorName: Prevalence of stunting, height for age, female (% of children under 5)

Longdefinition: Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.


IndicatorName: Prevalence of stunting, height for age, male (% of children under 5)

Longdefinition: Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.


Aggregationmethod: See SH.STA.STNT.ME.ZS for aggregation

IndicatorName: Prevalence of stunting, height for age (% of children under 5)

Longdefinition: Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.


Generalcomments: This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Suicide mortality rate, female (per 100,000 female population)

Longdefinition: Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).


IndicatorName: Suicide mortality rate, male (per 100,000 male population)


Generalcomments: This is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Suicide mortality rate (per 100,000 population)


Developmentrelevance: Road traffic injuries and deaths is a major global public health problem. Road traffic crashes are currently the leading cause of death for children and young adults in the world.  There is a strong association between the risk of road traffic death and the income level of countries.  The burden of road traffic deaths is disproportionately high among low- and middle-income countries.

Generalcomments: This is a sex-disaggregated indicator for Sustainable Development Goal 3.6.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality caused by road traffic injury, female (per 100,000 female population)

Longdefinition: Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population.


IndicatorName: Mortality caused by road traffic injury, male (per 100,000 male population)


Generalcomments: This is the Sustainable Development Goal indicator 3.6.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality caused by road traffic injury (per 100,000 population)


Developmentrelevance: Unsafe drinking water, unsafe sanitation and lack of hygiene are important causes of death.  Most diarrheal deaths in the world are caused by unsafe water, sanitation or hygiene.  According to the World Health Organization, in addition to diarrea, the following diseases could be prevented if adequate WASH services are provided: malnutrition, intestinal nematode infections, lymphatic filariasis, trachoma, schistosomiasis and malaria.

Generalcomments: This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.2 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene, female (per 100,000 female population)

Longdefinition: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.


IndicatorName: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene, male (per 100,000 male population)


Generalcomments: This is the Sustainable Development Goal indicator 3.9.2[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (per 100,000 population)


IndicatorName: Prevalence of wasting, weight for height, female (% of children under 5)

Limitationsandexceptions: Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided.

Longdefinition: Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months.


IndicatorName: Prevalence of wasting, weight for height, male (% of children under 5)

Longdefinition: Prevalence of wasting, male, is the proportion of boys under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months.


IndicatorName: Prevalence of wasting, weight for height (% of children under 5)

Longdefinition: Prevalence of wasting is the proportion of children under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months.


IndicatorName: Prevalence of severe wasting, weight for height, female (% of children under 5)

Longdefinition: Prevalence of severe wasting, female, is the proportion of girls under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months.


IndicatorName: Prevalence of severe wasting, weight for height, male (% of children under 5)

Longdefinition: Prevalence of severe wasting, male, is the proportion of boys under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months.


IndicatorName: Prevalence of severe wasting, weight for height (% of children under 5)

Longdefinition: Prevalence of severe wasting is the proportion of children under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months.


Developmentrelevance: The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The World Bank produced its first global poverty estimates for developing countries for World Development Report 1990: Poverty (World Bank 1990) using household survey data for 22 countries (Ravallion, Datt, and van de Walle 1991). Since then there has been considerable expansion in the number of countries that field household income and expenditure surveys.

Generalcomments: The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

IndicatorName: Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population)

Limitationsandexceptions:
Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.

Besides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.

However, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.

Longdefinition: Poverty headcount ratio at $2.15 a day is the percentage of the population living on less than $2.15 a day at 2017 purchasing power adjusted prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

Relatedsourcelinks: World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/

Shortdefinition: Poverty headcount ratio at $2.15 a day is the percentage of the population living on less than $2.15 a day at 2017 international prices.

Source: World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.

Statisticalconceptandmethodology:
International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.

Since World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $2.15 as the international poverty line using the 2017 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $3.65 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $6.85 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.

Early editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2017 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $2.15 a day in 2017 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

The statistics reported here are based on consumption data or, when unavailable, on income surveys.


Developmentrelevance: The World Bank Group's goal of promoting shared prosperity has been defined as fostering income growth of the bottom 40 per cent of the welfare distribution in every country. Gini coefficients are important background information for shared prosperity.

IndicatorName: Gini index

Limitationsandexceptions:
Gini coefficients are not unique. It is possible for two different Lorenz curves to give rise to the same Gini coefficient. Furthermore it is possible for the Gini coefficient of a developing country to rise (due to increasing inequality of income) while the number of people in absolute poverty decreases. This is because the Gini coefficient measures relative, not absolute, wealth.

Another limitation of the Gini coefficient is that it is not additive across groups, i.e. the total Gini of a society is not equal to the sum of the Gini's for its sub-groups. Thus, country-level Gini coefficients cannot be aggregated into regional or global Gini's, although a Gini coefficient can be computed for the aggregate.

Because the underlying household surveys differ in methods and types of welfare measures collected, data are not strictly comparable across countries or even across years within a country. Two sources of non-comparability should be noted for distributions of income in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (number of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution.

World Bank staff have made an effort to ensure that the data are as comparable as possible. Wherever possible, consumption has been used rather than income. Income distribution and Gini indexes for high-income economies are calculated directly from the Luxembourg Income Study database, using an estimation method consistent with that applied for developing countries.

Longdefinition: Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

Shortdefinition: The Gini index measures the extent to which the distribution of income or consumption among individuals or households within an economy deviates from a perfectly equal distribution. A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

Statisticalconceptandmethodology:
The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

The Gini index provides a convenient summary measure of the degree of inequality. Data on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.

The distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption.

The year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.


Developmentrelevance: The poverty rate as defined by national poverty lines reflects the share of the population that fails to meet the standard a country thinks is necessarty to cover basic needs.

Generalcomments: This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

IndicatorName: Poverty headcount ratio at national poverty lines (% of population)

Longdefinition: National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year.

Source: World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.

Statisticalconceptandmethodology:
Poverty headcount ratio among the population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.

Poverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.

National poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.

Almost all national poverty lines in developing economies are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years.

This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year. For all other economies, the year reported is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which data collection started.


Developmentrelevance:
Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labour flows from agriculture and other labour-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.

The breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.
Segregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries.

IndicatorName: Employment in agriculture, female (% of female employment) (modeled ILO estimate)

Limitationsandexceptions:
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.

The ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata.

Longdefinition: Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).

Notesfromoriginalsource: Given the exceptional situation, including the scarcity of relevant data, the  ILO modeled estimates and projections from 2020 onwards are subject to substantial uncertainty.

Source: International Labour Organization. “ILO modelled estimates database” ILOSTAT. Accessed February 07, 2024. https://ilostat.ilo.org/data/.

Statisticalconceptandmethodology:
The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.

The series is part of the "ILO modeled estimates database," including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/


IndicatorName: Employment in agriculture, male (% of male employment) (modeled ILO estimate)


IndicatorName: Employment in agriculture (% of total employment) (modeled ILO estimate)


Developmentrelevance: Four targets were added to the UN Millennium Declaration at the 2005 World Summit High-Level Plenary Meeting of the 60th Session of the UN General Assembly. One was full and productive employment and decent work for all, which is seen as the main route for people to escape poverty. Employment to population ratio is a key measure to monitor whether a country is on track to achieve the Millennium Development Goal of eradicating extreme poverty and hunger by 2015. And it continues to be a priority in the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all.

Generalcomments: The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

IndicatorName: Employment to population ratio, ages 15-24, female (%) (national estimate)

Limitationsandexceptions:
Data on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country.

Comparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.

The reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.

This indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms.

Longdefinition: Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.

Source: International Labour Organization. “Labour Force Statistics database (LFS)” ILOSTAT. Accessed October 16, 2024. https://ilostat.ilo.org/data/.

Statisticalconceptandmethodology: The employment to population ratio indicates how efficiently an economy provides jobs for people who want to work. A high ratio means that a large proportion of the population is employed. But a lower employment to population ratio can be seen as a positive sign, especially for young people, if it is caused by an increase in their education.


Generalcomments: National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

IndicatorName: Employment to population ratio, ages 15-24, female (%) (modeled ILO estimate)

Source: International Labour Organization. “ILO Modelled Estimates and Projections database (ILOEST)” ILOSTAT. Accessed February 06, 2024. https://ilostat.ilo.org/data/.

Statisticalconceptandmethodology:
The employment-to-population ratio indicates how efficiently an economy provides jobs for people who want to work. A high ratio means that a large proportion of the population is employed. But a lower employment-to-population ratio can be seen as a positive sign, especially for young people, if an increase in their education causes it.

The series is part of the "ILO modeled estimates database," including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/


IndicatorName: Employment to population ratio, ages 15-24, male (%) (national estimate)


IndicatorName: Employment to population ratio, ages 15-24, male (%) (modeled ILO estimate)


IndicatorName: Employment to population ratio, ages 15-24, total (%) (national estimate)


IndicatorName: Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)


Developmentrelevance:
Breaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers — generally unpaid, although compensation might come indirectly in the form of family income — may indicate weak development, little job growth, and often a large rural economy.

Each status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks.

IndicatorName: Employers, female (% of female employment) (modeled ILO estimate)

Limitationsandexceptions: Data are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.

Longdefinition: Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a "self-employment jobs" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).

Statisticalconceptandmethodology:
The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.

The series is part of the "ILO modeled estimates database," including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/


IndicatorName: Employers, male (% of male employment) (modeled ILO estimate)


IndicatorName: Employers, total (% of total employment) (modeled ILO estimate)


IndicatorName: Self-employed, female (% of female employment) (modeled ILO estimate)

Longdefinition: Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a "self-employment jobs." i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include four sub-categories of employers, own-account workers, members of producers' cooperatives, and contributing family workers.


IndicatorName: Self-employed, male (% of male employment) (modeled ILO estimate)


IndicatorName: Self-employed, total (% of total employment) (modeled ILO estimate)


Developmentrelevance: The indicator provides information on the proportion of women who are employed in decision-making and management roles in government, large enterprises and institutions.

IndicatorName: Female share of employment in senior and middle management (%)

Longdefinition: The proportion of females in total employment in senior and middle management. It corresponds to major group 1 in both ISCO-08 and ISCO-88 minus category 14 in ISCO-08 (hospitality, retail and other services managers) and minus category 13 in ISCO-88 (general managers), since these comprise mainly managers of small enterprises.

Source: International Labour Organization. “Labour Market-related SDG Indicators database (ILOSDG)” ILOSTAT. Accessed October 16, 2024. https://ilostat.ilo.org/data/.

Topic: Leadership


IndicatorName: Employment to population ratio, 15+, female (%) (national estimate)

Longdefinition: Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.


IndicatorName: Employment to population ratio, 15+, female (%) (modeled ILO estimate)


IndicatorName: Employment to population ratio, 15+, male (%) (national estimate)


IndicatorName: Employment to population ratio, 15+, male (%) (modeled ILO estimate)


IndicatorName: Employment to population ratio, 15+, total (%) (national estimate)


IndicatorName: Employment to population ratio, 15+, total (%) (modeled ILO estimate)


IndicatorName: Time-related underemployment, female (% of employment)

Longdefinition: Time-related underemployment refers to all persons in employment who (i) wanted to work additional hours, (ii) had worked less than a specified hours threshold (working time in all jobs), and (iii) were available to work additional hours given an opportunity for more work.


IndicatorName: Time-related underemployment, male (% of employment)


IndicatorName: Vulnerable employment, female (% of female employment) (modeled ILO estimate)

Longdefinition: Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment.

Source: World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization, ILOSTAT at https://ilostat.ilo.org/data/.


IndicatorName: Vulnerable employment, male (% of male employment) (modeled ILO estimate)


IndicatorName: Vulnerable employment, total (% of total employment) (modeled ILO estimate)


IndicatorName: Wage and salaried workers, female (% of female employment) (modeled ILO estimate)

Longdefinition: Wage and salaried workers (employees) are those workers who hold the type of jobs defined as "paid employment jobs," where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.


IndicatorName: Wage and salaried workers, male (% of male employment) (modeled ILO estimate)


IndicatorName: Wage and salaried workers, total (% of total employment) (modeled ILO estimate)