Aggregationmethod: Sum

Developmentrelevance: Large numbers of children out of school create pressure to enroll children and provide classrooms, teachers, and educational materials, a task made difficult in many countries by limited education budgets. However, getting children into school is a high priority for countries and crucial for achieving universal primary education.

IndicatorName: Children out of school, primary

Limitationsandexceptions: Due to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school.

Longdefinition: Children out of school are the number of primary-school-age children not enrolled in primary or secondary school.

Statisticalconceptandmethodology:
The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children.

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


IndicatorName: Children out of school, primary, female

License_Type: CC BY-4.0

Periodicity: Annual

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

Topic: Education


IndicatorName: Children out of school, primary, male


Aggregationmethod: Weighted average

IndicatorName: Children out of school (% of primary school age)

Limitationsandexceptions: The administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries.

Longdefinition: Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school.

Statisticalconceptandmethodology:
The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


IndicatorName: Expected years of schooling

Longdefinition: Expected years of schooling is the number of years a child of school entrance age is expected to spend at school, or university, including years spent on repetition. It is the sum of the age-specific enrolment ratios for primary, secondary, post-secondary non-tertiary and tertiary education.


Developmentrelevance: School life expectancy indicates the average number of years of schooling that the education system can offer. A high value indicates a probability for children to spend more years in education. Note that the expected number of years does not necessarily coincide with the expected number of grades of education completed, because of repetition.

IndicatorName: Expected years of schooling, female

Limitationsandexceptions: The expected number of years of schooling may not be comparable across countries due to different lengths of the school year and policies on repetition and promotion. It is also affected by the magnitude of children who never go to school.

Statisticalconceptandmethodology:
The school life expectancy is calculated as the sum of the age specific enrollment rates for the levels of education specified. The part of the enrolment that is not distributed by age is divided by the school-age population for the level of education they are enrolled in, and multiplied by the duration of that level of education. The result is then added to the sum of the age-specific enrolment rates.

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


IndicatorName: Expected years of schooling, male


IndicatorName: Lower secondary completion rate, female (% of relevant age group)

Limitationsandexceptions:
Data limitations preclude adjusting for students who drop out during the final year of lower secondary education. Thus this rate is a proxy that should be taken as an upper estimate of the actual lower secondary completion rate.

There are many reasons why the rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of lower secondary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of lower secondary education.

Longdefinition: Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education.

Statisticalconceptandmethodology:
Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education.

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


IndicatorName: Lower secondary completion rate, male (% of relevant age group)


IndicatorName: Lower secondary completion rate, total (% of relevant age group)


Developmentrelevance: A relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.

IndicatorName: Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative)

Limitationsandexceptions: Caution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts).

Longdefinition: The percentage of population ages 25 and over that attained or completed lower secondary education.

Statisticalconceptandmethodology:
It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.

Data are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.


IndicatorName: Educational attainment, at least completed lower secondary, population 25+, male (%) (cumulative)


IndicatorName: Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)


IndicatorName: Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)

Longdefinition: The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education.

Statisticalconceptandmethodology:
It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.

Data are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.


IndicatorName: Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)


IndicatorName: Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)


IndicatorName: Educational attainment, at least completed upper secondary, population 25+, female (%) (cumulative)

Longdefinition: The percentage of population ages 25 and over that attained or completed upper secondary education.

Statisticalconceptandmethodology:
It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.

Data are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.


IndicatorName: Educational attainment, at least completed upper secondary, population 25+, male (%) (cumulative)


IndicatorName: Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)


IndicatorName: Vocational and Technical enrolment (% of total secondary enrolment), female


Developmentrelevance: The share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education.

IndicatorName: Secondary education, pupils (% female)

Limitationsandexceptions: The percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity.

Longdefinition: Female pupils as a percentage of total pupils at secondary level includes enrollments in public and private schools.

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

Statisticalconceptandmethodology:
Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


IndicatorName: Percentage of male students in secondary education enrolled in vocational programmes, male (%)

Longdefinition: Total number of male students enrolled in vocational programmes at the secondary education level, expressed as a percentage of the total number of male students enrolled in all programmes (vocational and general) at the secondary level. Vocational education is designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation or trade or class of occupations or trades. Vocational education may have work-based components (e.g. apprenticeships). Successful completion of such programmes leads to labour-market relevant vocational qualifications acknowledged as occupationally-oriented by the relevant national authorities and/or the labour market.

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


IndicatorName: Secondary education, vocational pupils

Longdefinition: Secondary vocational pupils are the number of secondary students enrolled in technical and vocational education programs, including teacher training.

Statisticalconceptandmethodology:
Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


IndicatorName: Secondary education, vocational pupils (% female)


IndicatorName: Vocational and Technical enrolment (% of total secondary enrolment), total


Developmentrelevance: Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments.

IndicatorName: School enrollment, secondary (% gross)

Limitationsandexceptions: Enrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.

Longdefinition: Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.

Statisticalconceptandmethodology:
Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100.

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


IndicatorName: School enrollment, secondary, female (% gross)


IndicatorName: School enrollment, secondary, male (% gross)


IndicatorName: School enrollment, secondary (% net)

Longdefinition: Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.

Statisticalconceptandmethodology:
Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100.

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


IndicatorName: School enrollment, secondary, female (% net)


IndicatorName: School enrollment, secondary, male (% net)


Developmentrelevance: The effective transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating in lower secondary education, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low effective transition rate can signal such problems as an inadequate examination and promotion system or insufficient secondary education capacity.

IndicatorName: Progression to secondary school, female (%)

Limitationsandexceptions: The quality of data on the transition rate is affected when new entrants and repeaters are not correctly distinguished. Students who interrupt their studies after completing primary education could also affect data quality.

Longdefinition: Progression to secondary school refers to the number of new entrants to the first grade of secondary school in a given year as a percentage of the number of students enrolled in the final grade of primary school in the previous year (minus the number of repeaters from the last grade of primary education in the given year).

Statisticalconceptandmethodology:
Effective transition rate is calculated by dividing the number of new entrants in the first grade of secondary education in a given year (t) by the number of students who enrolled in the final grade of primary education in the previous school year (t-1) minus the number of repeaters from the last grade of primary education in the given year (t), and multiplying by 100.
Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


IndicatorName: Progression to secondary school, male (%)


IndicatorName: Progression to secondary school (%)


Developmentrelevance: The share of female teachers shows the level of gender representation in the teaching force. A value of greater than 50% indicates more opportunities or preference for women to participate in teaching activities. Women teachers are important as they serve as role models to girls and help to attract and retain girls in school.

IndicatorName: Secondary education, teachers (% female)

Longdefinition: Female teachers as a percentage of total secondary education teachers includes full-time and part-time teachers.

Statisticalconceptandmethodology:
The share of female teachers in secondary education is calculated by dividing the total number of female teachers at secondary level of education by the total number of teachers at the same level, and multiplying by 100.

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


IndicatorName: Gross graduation ratio, tertiary, female (%)

Limitationsandexceptions: Caution is needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors.

Longdefinition: Number of graduates from first degree programmes (at ISCED 6 and 7) expressed as a percentage of the population of the theoretical graduation age of the most common first degree programme.

Statisticalconceptandmethodology: Gross graduation ratio is calculated by dividing the number of graduates from first degree programmes (at ISCED 6 and 7) by the population of the theoretical graduation age of the most common first degree programme and multiplying by 100. Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


IndicatorName: Gross graduation ratio, tertiary, male (%)


IndicatorName: Gross graduation ratio, tertiary, total (%)


IndicatorName: Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)

Longdefinition: The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.

Statisticalconceptandmethodology:
It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.

Data are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.


IndicatorName: Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)


IndicatorName: Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)


IndicatorName: Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)

Longdefinition: The percentage of population ages 25 and over that attained or completed Doctoral or equivalent.

Statisticalconceptandmethodology:
It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.

Data are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.


IndicatorName: Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)


IndicatorName: Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)


IndicatorName: Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)

Longdefinition: The percentage of population ages 25 and over that attained or completed Master's or equivalent.

Statisticalconceptandmethodology:
It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.

Data are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.


IndicatorName: Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)


IndicatorName: Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)


IndicatorName: Educational attainment, at least completed short-cycle tertiary, population 25+, female (%) (cumulative)

Longdefinition: The percentage of population ages 25 and over that attained or completed short-cycle tertiary education.

Statisticalconceptandmethodology:
It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.

Data are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.


IndicatorName: Educational attainment, at least completed short-cycle tertiary, population 25+, male (%) (cumulative)


IndicatorName: Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)


IndicatorName: School enrollment, tertiary (% gross)

Longdefinition: Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level.

Statisticalconceptandmethodology:
Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100.

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


IndicatorName: School enrollment, tertiary, female (% gross)


IndicatorName: School enrollment, tertiary, male (% gross)


Generalcomments: The indicator has been changed from "Female share of graduates in Agriculture programmes, tertiary (%)" to "Female share of graduates in Agriculture, Forestry, Fisheries and Veterinary programmes, tertiary (%)" as of January 2021.

IndicatorName: Female share of graduates in Agriculture, Forestry, Fisheries and Veterinary programmes (%, tertiary)

Longdefinition: Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education.

Statisticalconceptandmethodology:
Percentage of female graduates by field of study in tertiary education is calculated by dividing the number of female graduates in a given field of education from tertiary education by the total number of graduates in the same field, and multiplying by 100.

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


IndicatorName: Female share of graduates in education (%, tertiary)


IndicatorName: Female share of graduates in engineering, manufacturing and construction (%, tertiary)


IndicatorName: Female share of graduates in health and welfare (%, tertiary)


IndicatorName: Female share of graduates in Arts and Humanities programmes (%, tertiary)


IndicatorName: Female share of graduates in unknown or unspecified fields (%, tertiary)


Generalcomments: The indicator has been changed from "Female share of graduates in Science programmes, tertiary (%)" to "Female share of graduates in Natural Sciences, Mathematics and Statisticsprogrammes, tertiary (%)" as of January 2021.

IndicatorName: Female share of graduates in Natural Sciences, Mathematics and Statistics programmes (%, tertiary)


Generalcomments: For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2020 (July 1, 2019-June 30, 2020)

IndicatorName: Female share of graduates from Science, Technology, Engineering and Mathematics (STEM) programmes, tertiary (%)

Shortdefinition: Female share of graduates in the given field of education, tertiary is the number of female graduates expressed as a percentage of the total number of graduates in the given field of education from tertiary education.

Topic: Technology


Generalcomments: The indicator has been changed from "Female share of graduates in Social Science, Business and Law programmes, tertiary (%)" to "Female share of graduates in Social Sciences, Journalism and Information programmes, tertiary (%)" as of January 2021.

IndicatorName: Female share of graduates in Social Sciences, Journalism and Information programmes (%, tertiary)


IndicatorName: Female share of graduates in services (%, tertiary)


Longdefinition: Tertiary education, academic staff (% female) is the share of female academic staff in tertiary education.

Statisticalconceptandmethodology:
The share of female academic staffs in tertiary education is calculated by dividing the total number of female academic staffs at tertiary level of education by the total number of academic staffs at the same level, and multiplying by 100.

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


Aggregationmethod: Median

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

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

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

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Data on GDP per capita come from the World Bank.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


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

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

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Data on GDP per capita come from the World Bank.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


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

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

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

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

Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. GDP data come from the World Bank.

The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).


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: There are periods of absence due to childcare accounted for in pension benefits (1=yes; 0=no)

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 pension contributions are paid or credited during maternity or parental leave, or the leave period is considered a qualifying period of employment used for the purpose of calculating pension benefits; or there are mechanisms to compensate for any contribution gaps and to ensure that the leave period does not reduce the assessment base or pension amounts, or if there are no mandatory contributory pension schemes, but there is a noncontributory universal social pension conditioned on noncontributory requirements with no means test attached.

Notesfromoriginalsource: This is one of the 35 scored indicators.

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

Statisticalconceptandmethodology: Women, Business and the Law tracks progress toward legal equality between men and women in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 2,000 respondents with expertise in family, labor, and 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


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: The ages at which men and women can retire with full pension benefits are the same (1=yes; 0=no)

IndicatorName: The age at which women and men can retire with full pension benefits is the same (1=yes; 0=no)

Longdefinition: The indicator measures whether the statutory age at which men and women can retire and receive an irrevocable minimum old-age pension is the same. If transitional provisions gradually increase, decrease or equalize the statutory retirement age, the answer reflect the age according to the report's data collection cycle, even if the law provides for changes over time.

Notesfromoriginalsource: This is one of the 35 scored indicators. The underlying inidcators are as follows: 1. Retirement age with full benefits, female (SG.AGE.RTRE.FL.FE), 2. Retirement age with full benefits, male (SG.AGE.RTRE.FL.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: The mandatory retirement age for men and women are the same (1=yes; 0=no)

IndicatorName: The mandatory retirement age for women and men is the same (1=yes; 0=no)

Longdefinition: The indicator measures whether the relative ages at which men and women must cease employment or can be terminated by their employer are equal.

Notesfromoriginalsource: This is one of the 35 scored indicators. The underlying inidcators are as follows: 1.Mandatory retirement age, female (SG.AGE.RTRE.FE), 2. Mandatory retirement age, male (SG.AGE.RTRE.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.
3. The indicator name has been changed as of February 2021.
Previous indicator name: The ages at which men and women can retire with partial pension benefits are the same (1=yes; 0=no)

IndicatorName: The age at which women and men can retire with partial pension benefits is the same (1=yes; 0=no)

Longdefinition: The indicator measures measures whether the age at which men and women can retire and receive partial pension benefits is the same, or if the age at which men and women can retire and receive partial benefits is not mandated. Partial pension benefits refer to a reduced or proportional minimum old-age pension payable to workers who did not accumulate enough work experience or contributions or have not reached the statutory age to qualify for a minimum old-age pension. If transitional provisions gradually increase, decrease or equalize the statutory retirement age, the answer reflect the age according to the report's data collection cycle, even if the law provides for changes over time.

Notesfromoriginalsource: This is one of the 35 scored indicators. The underlying inidcators are as follows: 1 .Retirement age with partial benefits, female (SG.AGE.RTRE.PL.FE), 2. Retirement age with partial benefits, male (SG.AGE.RTRE.PL.MA)


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

IndicatorName: Mandatory retirement age, female

Longdefinition: The indicator represents the actual age at which women are required to retire, or employers can terminate a contract, according to the applicable legislation.

Notesfromoriginalsource: This indicator is additional to the 35 scored indicators, and associated to the following indicator "The mandatory retirement age for men and women are equal (1=yes; 0=no)" (SG.AGE.MRET.EQ).


IndicatorName: Retirement age with full benefits, female

Longdefinition: The indicator represents the actual age at which women can retire with full pension benefits, according to the applicable legislation.

Notesfromoriginalsource: This indicator is additional to the 35 scored indicators, and associated to the following indicator "The ages at which men and women can retire with full pension benefits are equal (1=yes; 0=no)" (SG.AGE.FUPN.EQ).


IndicatorName: Retirement age with full benefits, male

Longdefinition: The indicator represents the actual age at which men can retire with full pension benefits, according to the applicable legislation.


IndicatorName: Mandatory retirement age, male

Longdefinition: The indicator represents the actual age at which men are required to retire, or employers can terminate a contract, according to the applicable legislation.


IndicatorName: Retirement age with partial benefits, female

Longdefinition: The indicator represents the actual age at which women can retire with partial pension benefits, according to the applicable legislation.

Notesfromoriginalsource: This indicator is additional to the 35 scored indicators, and associated to the following indicator "The ages at which men and women can retire with partial pension benefits are equal (1=yes; 0=no)" (SG.AGE.PAPN.EQ).


IndicatorName: Retirement age with partial benefits, male

Longdefinition: The indicator represents the actual age at which men can retire with partial pension benefits, according to the applicable legislation.


IndicatorName: A woman can apply for a passport in the same way as a man (1=yes; 0=no)

Longdefinition: The indicator measures whether there are differences in passport application procedures between men and women.

Topic: Norms and Decision-making


IndicatorName: A woman can register a business in the same way as a man (1=yes; 0=no)

Longdefinition: The indicator measures whether there are restrictions on a woman registering a business (i.e. if a woman needs her husband’s or guardian’s permission, signature or consent to register a business; or the registration process at any stage requires a woman to provide additional information or documentation that is not required of a man).

Topic: Entrepreneurship


IndicatorName: A woman can sign a contract in the same way as a man (1=yes; 0=no)

Longdefinition: The indicator measures whether a woman obtains full legal capacity upon reaching the age of majority and there are no restrictions on her signing legally binding contracts.


IndicatorName: Main cooking fuel: charcoal (% of households)

Longdefinition: Percentage of households who use charcoal as thier main cooking fuel

Source: Demographic and Health Surveys (DHS)

Topic: Economic and Social Context


IndicatorName: Main cooking fuel: agricultural crop (% of households)

Longdefinition: Percentage of households who use agricultural crop as thier main cooking fuel


IndicatorName: Main cooking fuel: dung (% of households)

Longdefinition: Percentage of households who use dung as thier main cooking fuel


IndicatorName: Main cooking fuel: electricity (% of households)

Longdefinition: Percentage of households who use electricity as thier main cooking fuel


IndicatorName: Location of cooking: inside the house (% of households)

Longdefinition: Percentage of households who do their cooking inside the house


IndicatorName: Main cooking fuel: LPG/natural gas/biogas (% of households)

Longdefinition: Percentage of households who use Liquified Petroleum Gas (LPG) or natural gas or biogas as thier main cooking fuel


IndicatorName: Location of cooking: other places (% of households)

Longdefinition: Percentage of households who do their cooking in other places


IndicatorName: Location of cooking: outdoors (% of households)

Longdefinition: Percentage of households who do their cooking outdoors


IndicatorName: Location of cooking: separate building (% of households)

Longdefinition: Percentage of households who do their cooking in a separate building


IndicatorName: Main cooking fuel: straw/shrubs/grass (% of households)

Longdefinition: Percentage of households who use straw/shrubs/grass as thier main cooking fuel


IndicatorName: Main cooking fuel: wood (% of households)

Longdefinition: Percentage of households who use wood as thier main cooking fuel


IndicatorName: A woman can travel outside the country in the same way as a man (1=yes; 0=no)

Longdefinition: The indicator measures whether there are restrictions on a woman traveling alone internationally.


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

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

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

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


IndicatorName: Women participating in making daily purchase decisions (% of women age 15-49)

Longdefinition: Women participating in making daily purchase decisions is Percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in making daily purchases

Statisticalconceptandmethodology: Women participating in making daily purchase decisions is the number of currently married women aged 15-49 who say they alone or jointly have the final say in making daily purchases, expressed as percentage of currently married women aged 15-49 who have been interviewed and it's derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly have the final say in making daily purchases by total number of currently married women age 15-49 who have been interviewed.


IndicatorName: Women participating in decision of what food to cook daily (% of women age 15-49)

Longdefinition: Women participating in decision of what food to cook daily is Percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in what food to cook daily

Statisticalconceptandmethodology: Women participating in decisions of what food to cook daily is the number of currently married women aged 15-49 who say they alone or jointly have the final say in what food to cook daily, expressed as percentage of currently married women aged 15-49 who have been interviewed and it's derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly have the final say in what food to cook daily by total number of currently married women age 15-49 who have been interviewed.


IndicatorName: Women participating in own health care decisions (% of women age 15-49)

Longdefinition: Women Participating in own health care decisions is Percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in own health care

Statisticalconceptandmethodology: Women participating in own health care decisions is the number of currently married women aged 15-49 who say they alone or jointly with thier husband have the final say in own health care, expressed as percentage of currently married women aged 15-49 who have been interviewed and it's derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly with thier husband have the final say in own health care by total number of currently married women age 15-49 who have been interviewed.


IndicatorName: Decision maker about a woman's own health care: mainly husband (% of women age 15-49)

Longdefinition: Decision maker about women own health care: mainly husband is Percentage of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the husband

Statisticalconceptandmethodology: Decision maker about Own health care: mainly husband is the number of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the husband, expressed as percentage of currently married women aged 15-49 who have been interviewed .


IndicatorName: Decision maker about a woman's own health care: other (% of women age 15-49)

Longdefinition: Decision maker about women own health care: other is Percentage of currently married women aged 15-49 for whom the decision maker for their own health care is recorded as 'other'

Statisticalconceptandmethodology: Decision maker about Own health care: other  is the number of currently married women aged 15-49 for whom the decision maker for their own health care is registered as others, expressed as percentage of all interviewed currently married women aged 15-49.


IndicatorName: Decision maker about a woman's own health care: someone else (% of women age 15-49)

Longdefinition: Decision maker about women own health care: someone else is Percentage of currently married women aged 15-49 for whom the decision maker for their own health care is mainly someone else

Statisticalconceptandmethodology: Decision maker about Own health care: someone else is the number of currently married women aged 15-49 for whom the decision maker for their own health care is someone esle, expressed as percentage of all interviewed currently married women aged 15-49.


IndicatorName: Decision maker about a woman's own health care: mainly wife  (% of women age 15-49)

Longdefinition: Decision maker about women own health care: mainly wife  is Percentage of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the respondent

Statisticalconceptandmethodology: Decision maker about Own health care: mainly wife  is the number of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the respondent, expressed as percentage of currently married women aged 15-49 who have been interviewed .


IndicatorName: Decision maker about a woman's own health care: wife and husband jointly (% of women age 15-49)

Longdefinition: Decision maker about women own health care: wife and husband jointly is Percentage of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the respondent and her husband

Statisticalconceptandmethodology: Decision maker about Own health care: wife and husband jointly is the number of currently married women aged 15-49 for whom the decision maker for their own health care is mainly the respondent and her husband, expressed as percentage of currently married women aged 15-49 who have been interviewed.


IndicatorName: Women participating in none of the three decisions (own health care, major household purchases, and visiting family) (% of women age 15-49)

Longdefinition: Women participating in none of the three decisions (own health care, major household purchases, and visiting family) is Percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in none of the three decisions (own health care, major household purchases, and visiting family)

Statisticalconceptandmethodology: Women participating in none of the three decisions (own health care, major household purchases, and visiting family) is the number of currently married women aged 15-49 who say they alone or jointly have the final say in none of the three decisions, expressed as percentage decisions by total number of currently married women age 15-49 who have been interviewed.


IndicatorName: Women participating in making major household purchase decisions (% of women age 15-49)

Longdefinition: Women participating in making major household purchase decisions is Percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in making major household purchases

Statisticalconceptandmethodology: Women participating in making major household purchase decision is the number of currently married women aged 15-49 who say that they alone or jointly with thier husband have the final say in making major household purchases, expressed as percentage of currently married women aged 15-49 who have been interviewed and it's derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly have the final say in making major household purchases by total number of currently married women age 15-49 who have been interviewed.


IndicatorName: Decision maker about major household purchases: mainly husband (% of women age 15-49)

Longdefinition: Decision maker about ajor household purchases: mainly husband is Percentage of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly the husband

Statisticalconceptandmethodology: Decision maker about major household purchases: mainly husband is the number of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly the husband, expressed as percentage of currently married women aged 15-49 who have been interviewed.


IndicatorName: Decision maker about major household purchases: other (% of women age 15-49)

Longdefinition: Decision maker about major household purchases: other is Percentage of currently married women aged 15-49 for whom the decision maker for major household purchases is recorded as 'other'

Statisticalconceptandmethodology: Decision maker about major household purchases: other is the number of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly recorded as 'other', expressed as percentage of currently married women aged 15-49 who have been interviewed.


IndicatorName: Decision maker about major household purchases: someone else (% of women age 15-49)

Longdefinition: Decision maker about Major household purchases: someone else is Percentage of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly someone else

Statisticalconceptandmethodology: Decision maker about major household purchases: someone else is the number of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly someone else, expressed as percentage of currently married women aged 15-49 who have been interviewed.


IndicatorName: Decision maker about major household purchases: mainly wife (% of women age 15-49)

Longdefinition: Decision maker about major household purchases: mainly wife is Percentage of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly the respondent

Statisticalconceptandmethodology: Decision maker about major household purchases: mainly wife is the number of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly the respondent, expressed as percentage of all interviewed currently married women aged 15-49.


IndicatorName: Decision maker about major household purchases: wife and husband jointly (% of women age 15-49)

Longdefinition: Decision maker about major household purchases: wife and husband jointly is Percentage of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly the respondent and her husband

Statisticalconceptandmethodology: Decision maker about major household purchases: wife and husband jointly is the number of currently married women aged 15-49 for whom the decision maker for major household purchases is mainly the respondent and her husband, expressed as percentage of currently married women aged 15-49 who have been interviewed.


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

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

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

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

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

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


IndicatorName: Women participating in decision of visits to family, relatives, friends (% of women age 15-49)

Longdefinition: Women participating in decision of visits to family, relatives, friends is Percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in visits to family, relatives, friends

Statisticalconceptandmethodology: Women participating in decision of visits to family, relatives, friends is the number of currently married women aged 15-49 who say they alone or jointly with thier husband have the final say in visits to family, relatives, friends, expressed as percentage of currently married women aged 15-49 who have been interviewed and it's derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly have the final say in visits to family, relatives, friends by total number of currently married women age 15-49 who have been interviewed.


IndicatorName: Decision maker about a woman's visits to her family or relatives: mainly husband (% of women age 15-49)

Longdefinition: Decision maker about a woman's visits to her family or relatives: mainly husband is Percentage of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly the husband

Statisticalconceptandmethodology: Decision maker about Visits to her family or relatives: mainly husband is the number of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly the husband, expressed as percentage of currently married women aged 15-49 who have been interviewed.


IndicatorName: Decision maker about a woman's visits to her family or relatives: other (% of women age 15-49)

Longdefinition: Decision maker about a woman's visits to her family or relatives: other is Percentage of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is recorded as 'other'

Statisticalconceptandmethodology: Decision maker about Visits to her family or relatives: other is the number of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is recorded as 'other', expressed as percentage of currently married women aged 15-49 who have been interviewed.


IndicatorName: Decision maker about a woman's visits to her family or relatives: someone else (% of women age 15-49)

Longdefinition: Decision maker about a woman's visits to her family or relatives: someone else is Percentage of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly someone else

Statisticalconceptandmethodology: Decision maker about Visits to her family or relatives: someone else is the number of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly someone else, expressed as percentage of currently married women aged 15-49 who have been interviewed.


IndicatorName: Decision maker about a woman's visits to her family or relatives: mainly wife (% of women age 15-49)

Longdefinition: Decision maker about a woman's visits to her family or relatives: mainly wife is Percentage of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly the respondent

Statisticalconceptandmethodology: Decision maker about Visits to her family or relatives: mainly wife is the number of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly the respondent, expressed as percentage of currently married women aged 15-49 who have been interviewed.


IndicatorName: Decision maker about Visits to her family or relatives: wife and husband jointly (% of women age 15-49)

Longdefinition: Decision maker about Visits to her family or relatives: wife and husband jointly is Percentage of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is mainly the respondent and her husband

Statisticalconceptandmethodology: Decision maker about Visits to her family or relatives: wife and husband jointly is the number of currently married women aged 15-49 for whom the decision maker for visits to her family or relatives is  mainly the respondent and her husband, expressed as percentage of currently married women aged 15-49 who have been interviewed.


IndicatorName: Dismissal of pregnant workers is prohibited (1=yes; 0=no)

Longdefinition: The indicator measures whether the law explicitly prohibits the dismissal of pregnant women, if pregnancy cannot serve as grounds for termination of a contract or if dismissal of pregnant workers is considered a form of unlawful termination, unfair dismissal or wrongful discharge.

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.


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: Women can work in jobs deemed dangerous in the same way as men (1=yes; 0=no)

IndicatorName: A woman can work in a job deemed dangerous in the same way as a man (1=yes; 0=no)

Longdefinition: The indicator measures whether there are laws that prohibit or restrict nonpregnant and non-nursing women from working in a broad and subjective category of jobs deemed “hazardous,” “arduous” or “morally inappropriate.”


IndicatorName: Proportion of women in ministerial level positions (%)

Longdefinition: Women in ministerial level positions is the proportion of women in ministerial or equivalent positions (including deputy prime ministers) in the government. Prime Ministers/Heads of Government are included when they hold ministerial portfolios. Vice-Presidents and heads of governmental or public agencies are excluded.

Source: Inter-Parliamentary Union. United Nations Entity for Gender Equality and the Empowerment of Women (UN Women). Women in Politics.

Topic: Leadership


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

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

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

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

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

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

Limitationsandexceptions:
The number of countries covered varies with suspensions or dissolutions of parliaments. There can be difficulties in obtaining information on by-election results and replacements due to death or resignation. These changes are ad hoc events which are more difficult to keep track of. By-elections, for instance, are often not announced internationally as general elections are. Parliaments vary considerably in their internal workings and procedures, however, generally legislate, oversee government and represent the electorate. In terms of measuring women's contribution to political decision making, this indicator may not be sufficient because some women may face obstacles in fully and efficiently carrying out their parliamentary mandate.

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

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

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

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

National parliaments can be bicameral or unicameral. This indicator covers the single chamber in unicameral parliaments and the lower chamber in bicameral parliaments. It does not cover the upper chamber of bicameral parliaments. Seats are usually won by members in general parliamentary elections. Seats may also be filled by nomination, appointment, indirect election, rotation of members and by-election. Seats refer to the number of parliamentary mandates, or the number of members of parliament.


IndicatorName: Female professional and technical workers (% of total)

Longdefinition: Female professional and technical workers refers to the share of professionals and technical workers who are female. Women's share of positions are defined according to the International Standard Classification of Occupations (ISCO-88) to include physical, mathematical and engineering science professionals (and associate professionals), life science and health professionals (and associate professionals), teaching professionals (and associate professionals) and other professionals and associate professionals.

Source: Gender, Institutions and Development Database, Organization for Economic Co-operation and Development (OECD), web site: http://www.oecd.org/document/16/0,3343,en_2649_33935_39323280_1_1_1_1,00.html.


IndicatorName: A woman can get a job in the same way as a man (1=yes; 0=no)

Longdefinition: The indicator measures whether there are restrictions on a woman's legal capacity and ability to work.


IndicatorName: Households with water on the premises (%)

Longdefinition: Percentage of households who have a water source on their premises


IndicatorName: Households with water less than 30 minutes away round trip (%)

Longdefinition: Percentage of households who have a water source in less than 30 minutes away round trip


IndicatorName: Households with water 30 minutes or longer away round trip (%)

Longdefinition: Percentage of households who have a water source 30 minutes or longer away round trip


IndicatorName: A woman can be "head of household" in the same way as a man (1=yes; 0=no)

Longdefinition: The indicator measures whether there are restrictions on a woman becoming head of household or head of family.


IndicatorName: A woman can travel outside her home in the same way as a man (1=yes; 0=no)

Longdefinition: The indicator measures whether there are restrictions on a woman traveling alone domestically.


IndicatorName: Male and female surviving spouses have equal rights to inherit assets (1=yes; 0=no)

Longdefinition: The indicator measures whether both spouses have equal rank and rights to inherit assets when there is no will. For this question, it is assumed that the deceased spouse left no children or any other heirs, other than the surviving spouse.

Topic: Assets


IndicatorName: Sons and daughters have equal rights to inherit assets from their parents (1=yes; 0=no)

Longdefinition: The indicator measures whether there are gender-based differences in the rules of intestate succession for transfer of property from parents to children.


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: Women are able to work in the same industries as men (1=yes; 0=no)

IndicatorName: A woman can work in an industrial job in the same way as a man (1=yes; 0=no)

Longdefinition: The indicator measures whether nonpregnant and non-nursing a woman can work in the mining, construction, manufacturing, energy, water, agriculture, and transportation industries in the same way as men.  nonpregnant and non-nursing women can work in the mining, construction, manufacturing, energy, water, agriculture, and transportation industries in the same way as men.


IndicatorName: The law grants spouses equal administrative authority over assets during marriage (1=yes; 0=no)

Longdefinition: The indicator measures whether spouses retain administrative power over those assets each brought to the marriage or acquired during marriage, and their accrued value, without the need for spousal consent; or spouses administer their separate property, but for major transactions, such as selling or pledging the property as collateral, spousal consent is required, or if both spouses have equal rights in the administration and transaction of joint property.


IndicatorName: The law prohibits discrimination in access to credit based on gender (1=yes; 0=no)

Longdefinition: The indicator measures whether the law prohibits discrimination by creditors based on gender or prescribes equal access for both men and women when conducting financial transactions, entrepreneurial activities or receiving financial assistance, or if the law prohibits gender discrimination when accessing goods and services (and services are defined to include financial services).


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

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


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

Longdefinition: The index measures how laws and regulations affect women’s economic opportunity. Overall scores are calculated by taking the average score of each index (Mobility, Workplace, Pay, Marriage, Parenthood, Entrepreneurship, Assets and Pension), with 100 representing the highest possible score.


IndicatorName: Women, Business and the Law: Assets Indicator Score (scale 1-100)

Longdefinition: The Assets indicator examines gender differences in property and inheritance law, including instances in which legal systems are supported by customary law and judicial precedent. The Assets indicator score is obtained by calculating the unweighted average of the following: 1. SG.OWN.PRRT.IM; 2. SG.IHT.ASST.PT.EQ; 3. SG.IHT.ASST.EQ; 4. SG.LAW.ASST.AR; 5. SG.LAW.NMCN (= 20 points each) and scaling the result to 100.


IndicatorName: Women, Business and the Law: Entrepreneurship Indicator Score (scale 1-100)

Longdefinition: The Entrepreneurship indicator measures constraints on women starting and running a business.. The Entrepreneurship indicator score is obtained by calculating the unweighted average of the following: 1. SG.CNT.SIGN.EQ; 2. SG.BUS.REGT.EQ; 3. SG.OPN.BANK.EQ; 4. SG.LAW.CRDD.GR (= 25 points each) and scaling the result to 100.


IndicatorName: Women, Business and the Law: Mobility Indicator Score (scale 1-100)

Longdefinition: The Mobility indicator measures constraints on a woman’s agency and freedom of movement, both of which are likely to influence her decision to enter the labor force and engage in entrepreneurial activity. Mobility indicator score is obtained by calculating the unweighted average of the following: 1. SG.APL.PSPT.EQ; 2. SG.CTR.TRVL.EQ; 3. SG.HME.TRVL.EQ; 4. SG.LOC.LIVE.EQ (= 25 points each) and scaling the result to 100.


IndicatorName: Women, Business and the Law: Marriage Indicator Score (scale 1-100)

Longdefinition: The Marriage indicator measures legal constraints related to marriage and divorce. The Marriage indicator score is obtained by calculating the unweighted average of the following: 1. SG.LAW.OBHB.MR.NO;  2. SG.HLD.HEAD.EQ;  3. SG.LEG.DVAW; 4. SG.OBT.DVRC.EQ; 5. SG.REM.RIGT.EQ (= 20 points each) and scaling the result to 100.


IndicatorName: Women, Business and the Law: Pension Indicator Score (scale 1-100)

Longdefinition: The Pension indicator assesses laws affecting the size of a woman’s pension. The Pension indicator score is obtained by calculating the unweighted average of the following: 1. SG.AGE.FUPN.EQ; 2. SG.AGE.PAPN.EQ; 3. SG.AGE.MRET.EQ; 4. SG.ABS.PENB (= 25 points each) and scaling the result to 100.


IndicatorName: Women, Business and the Law: Parenthood Indicator Score (scale 1-100)

Longdefinition: The Parenthood indicator examines laws affecting women’s work during and after pregnancy. The Parenthoood indicator score is obtained by calculating the unweighted average of the following: 1. SH.MMR.LEVE.AL; 2.SH.MMR.LEVE.GT; 3. SH.PTR.LEVE.AL; 4.SH.PAR.LEVE.AL; 5.SG.DML.PRGW (= 20 points each) and scaling the result to 100.


IndicatorName: Women, Business and the Law: Pay Indicator Score (scale 1-100)

Longdefinition: The Pay indicator measures laws affecting occupational segregation and the gender wage gap.  The Pay indicator score is obtained by calculating the unweighted average of the following: 1. SG.LAW.EQRM.WK; 2. SG.NGT.WORK.EQ; 3. SG.DNG.WORK.DN.EQ; 4. SG.IND.WORK.EQ (= 25 points each) and scaling the result to 100.


IndicatorName: Women, Business and the Law: Workplace Indicator Score (scale 1-100)

Longdefinition: The Workplace indicator analyzes laws affecting women’s decisions to enter the labor market, including women’s legal capacity and ability to work, as well as protections in the workplace against discrimination and sexual harassment. The Workplace indicator score is obtained by calculating the unweighted average of the following: 1. SG.GET.JOBS.EQ; 2. SG.LAW.NODC.HR; 3. SG.LEG.SXHR.EM; 4. SG.PEN.SXHR.EM (= 25 points each) and scaling the result to 100.


IndicatorName: The law provides for the valuation of nonmonetary contributions (1=yes; 0=no)

Longdefinition: The indicator measures whether there is an explicit legal recognition of such contributions and the law provides for equal or equitable division of the property or the transfer of a lump sum to the stay-at-home spouse based on nonmonetary contributions; or the default marital property regime is full community, partial community or deferred community because these regimes implicitly recognize nonmonetary contributions at the time of property division and benefit both spouses regardless of who purchased the property or holds title to it. Nonmonetary contributions include caring for minor children, taking care of the family home, or any other nonmonetized contribution from a stay-at-home spouse.


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

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


IndicatorName: The law is free of legal provisions that require a married woman to obey her husband  (1=yes; 0=no)

Longdefinition: The indicator measures whether a married woman is legally required to obey her husband.


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

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

Topic: Violence


IndicatorName: There is legislation on sexual harassment in employment (1=yes; 0=no)

Longdefinition: The indicator measures whether there is a legal provision or legislation that specifically protects women against sexual harassment in employment, including unwelcome verbal or physical conduct of a sexual nature.


IndicatorName: A woman can choose where to live in the same way as a man (1=yes; 0=no)

Longdefinition: The indicator measures whether there are restrictions on a woman choosing where to live.