This dataset presents the labor force participation rate of females aged 15 years and above in the State of Qatar, disaggregated by age group, nationality (Qatari and Non-Qatari), and year. Values are expressed in percentages with decimal precision. The data provides insights into workforce engagement trends among women across different age cohorts and population groups. This can support planning in employment, gender equality, and socio-economic policy.

Analysis of ‘Female Employment vs Socioeconimic Factors’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mdmuhtasimbillah/female-employment-vs-socioeconimic-factors on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Women roughly occupy half of the world's population but when it comes to the total workforce of a country, the percentage of male and female workers are rarely similar. This is even more prominent for the developing and underdeveloped countries. While several reasons such as the insufficient access to education, religious superstitions, lack of adequate infrastructures are responsible for this discrepancy, it goes way beyond these. And to show the effects of multiple socioeconomic factors on the participation of women in the total workforce, percentage of female employment in the total labor force has been considered. Using multiple linear regression model, the relationship between these factors can be analyzed.

Content

For the current study, the data set has been chosen from a survey performed on the population of Bangladesh. The datasets selected for this study span over 25 years (from 1995 to 2019). Data has been collected separately from multiple datasets from the World Bank databank for the employed women percentage and the related predictor variables. These datasets were compiled into one dataset and it corresponds to the 25 data points for the variables. There is one response variable which is the percentage of the employed women and 10 exlnanatory variables of predictors. Brief descriptions of these variables are given below.

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

FertilityRate Fertility rate (birth per women). Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.

RatioMaletoFemale Ratio of female to male labor force participation rate. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period. Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100.

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

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

Industry Employment in industry, female (% of female employment). The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).

Services Employment in services, female (% of female employment). The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).

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

ContrFamWorkers Contributing family workers, female (% of female employment). Contributing family workers are those workers who hold "self-employment jobs" as own-account workers in a market-oriented establishment operated by a related person living in the same household.

OwnAccount Own-account female workers (% of employment). Own-account workers are workers who, working on their own account or with one or more partners, hold the types of jobs defined as "self-employment jobs" and have not engaged on a continuous basis any employees to work for them. Own account workers are a subcategory of "self-employed".

Vulnerable Vulnerable employment, female (% of female employment). Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment.

Inspiration

Linear model as well as other statistical methods can be applied on this dataset to analyze if there is any viable relationship between the predictor and the response variables.

--- Original source retains full ownership of the source dataset ---

United Kingdom UK: Labour Force: Female: % of Total Labour Force data was reported at 46.528 % in 2017. This records an increase from the previous number of 46.515 % for 2016. United Kingdom UK: Labour Force: Female: % of Total Labour Force data is updated yearly, averaging 45.614 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 46.528 % in 2017 and a record low of 43.192 % in 1990. United Kingdom UK: Labour Force: Female: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Labour Force. Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.; ; Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

Abstract

This data is from a quantitative survey administered in 2023 to 2,000 married Nepali women and men from 4 provinces in the country about their own beliefs regarding norms-related behaviors, their expectations of how common it is for others in their social group to engage in those behaviors, and the expected social consequences surrounding those behaviors. It is the primary dataset used to author the working paper titled "Women’s Labor Force Participation in Nepal: An Exploration of The Role of Social Norms" - which presents rigorous evidence on whether and the extent to which social norms matter for women's labor force participation in Nepal.

Geographic coverage

The survey data includes a representative sample of households from 4 out of 7 provinces in Nepal: 1. Bagmati Province 2. Sudurpashchim Province 3. Madhesh Province 4. Gandaki Province

Analysis unit

Individual

Universe

The sampling frame is a list of all wards within each selected province.

Kind of data

Sample survey data [ssd]

Sampling procedure

Ward (cluster) selection: The sampling frame consisted of the list of all wards within each selected province. Each province comprises districts and within each district are municipalities (urban and rural municipalities) which are further broken down into wards – the smallest administrative units. The list of wards and their population figures were taken from the latest available 2021 Census. First, the universe of all districts was stratified by urban and rural to ensure greater statistical power for detecting differences between the 2 localities. The stratification by urban-rural proportionate to the population proportion of each group within a province resulted in a self-weighted sample, allowing for analysis of data at the province level and further at locality level within each province. To select the wards, a random start point was generated to negate any bias in the list and to provide an independent chance of selection from the list. The sampling method used here, probability proportionate to size (PPS), gives an independent chance of selection to each ward as per its population size, i.e., a higher chance of selection to wards with a higher population size.38 As a first step of random selection of wards, the cumulative frequency (CF) of the population of households in a ward was calculated. Since the unit of analysis for our study purpose was households having certain criteria and we expected the main outcome variables (social norms) to vary at household levels (as opposed to at an individual level), the household population figures served as the basis for sampling purpose (as opposed to the population size of individuals for a ward). Applying PPS, in the first step, the required number of wards were selected for Categories 1 and 2 households (households with working and non-working females respectively). Following this, the clusters allocated for Category 3 (households with migrant population) households were taken as a subset of the wards selected for Categories 1 and 2.

Selection of the random starting point within each ward during in-field random sampling of households: The selection of the random starting point within a PSU was done by the survey supervisors. For every ward, a predefined landmark for the starting point was chosen. The predefined landmark consisted of i) school, ii) health post, iii) central marketplace, or iv) ward office. The selection of a predefined landmark was the basis of the starting point which was made at the central office. The chosen landmark for every cluster was rotated to account for randomization and to avoid interviewer bias. Once the landmark was chosen, each enumerator used the spin-the-bottle method to randomize the direction in which the survey took place. After starting with a household, enumerators used a skip interval to survey every third household in rural and every fifth household in urban areas. Once the household was chosen, the interviewer used the screener to ascertain the eligibility as per the category quota set aside for them.

Respondent selection: The respondents were selected based on a screener instrument that surveyed the following factors: 1. Gender: Since the views about social norms and labor market outcomes vary by gender, both males and females within a household were interviewed. However, for households with migrant men, only the women were interviewed. 2. Age group: For all women, the screener was applied so as to ensure that only women within the economically active age range, i.e., between the ages of 18-59 years were interviewed. For spouses of female respondents, they had to be at least 18 years of age with no maximum age limit set. 3. Ethnicity: Nepal has more than a hundred ethnic groups residing across the country, and thus the major 8-10 groups are captured in the sample. The other objective of applying a screener for monitoring ethnic composition was to ensure that marginalized ethnic groups such as Dalits were sufficiently represented in the survey. 4. Marital Status: Only married men and women were interviewed since marriage and the responsibilities that come with are sown to impose greater social barriers and restrictions on mobility and work of females. 5. Location: The survey was carried out in both rural and urban locations in a total of 4 provinces. 6. General demographic factors include: • Perceived economic situation: Low to middle-income • It was ensured that both the respondents (male and female for Categories 1 and 2) and female respondent for Category 3 belonged to the second generation of the selected household (for example, not the in-laws residing in a household but their son and his wife.

Mode of data collection

Computer Assisted Personal Interview [capi]

Hong Kong HK: Labour Force: Female: % of Total Labour Force data was reported at 48.985 % in 2017. This records an increase from the previous number of 48.817 % for 2016. Hong Kong HK: Labour Force: Female: % of Total Labour Force data is updated yearly, averaging 43.964 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 48.985 % in 2017 and a record low of 36.956 % in 1990. Hong Kong HK: Labour Force: Female: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Hong Kong SAR – Table HK.World Bank.WDI: Labour Force. Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.; ; Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

This statistic presents the percentage of employed women in computing-related occupations in the United States from 2000 to 2020. As of the last reported year, 28.8 percent of U.S. database administrators were female, down from 46.2 percent in 2016.

Brazil BR: Labour Force: Female: % of Total Labour Force data was reported at 43.342 % in 2024. This records an increase from the previous number of 43.325 % for 2023. Brazil BR: Labour Force: Female: % of Total Labour Force data is updated yearly, averaging 42.032 % from Dec 1990 (Median) to 2024, with 35 observations. The data reached an all-time high of 43.596 % in 2019 and a record low of 34.754 % in 1990. Brazil BR: Labour Force: Female: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Labour Force. Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.;World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization and United Nations Population Division.;Weighted average;

Egypt EG: Labour Force Participation Rate: National Estimate: Female: % of Female Population Aged 15+ data was reported at 22.979 % in 2016. This records an increase from the previous number of 22.666 % for 2015. Egypt EG: Labour Force Participation Rate: National Estimate: Female: % of Female Population Aged 15+ data is updated yearly, averaging 20.160 % from Dec 1960 (Median) to 2016, with 41 observations. The data reached an all-time high of 29.970 % in 1989 and a record low of 4.360 % in 1973. Egypt EG: Labour Force Participation Rate: National Estimate: Female: % of Female Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.World Bank: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Iceland IS: Labour Force: Female: % of Total Labour Force data was reported at 47.144 % in 2017. This records an increase from the previous number of 47.100 % for 2016. Iceland IS: Labour Force: Female: % of Total Labour Force data is updated yearly, averaging 46.900 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 47.537 % in 2013 and a record low of 45.495 % in 1991. Iceland IS: Labour Force: Female: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iceland – Table IS.World Bank: Labour Force. Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.; ; Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor data retrieved in November 2017.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

Philippines PH: Labour Force: Female: % of Total Labour Force data was reported at 39.899 % in 2017. This records an increase from the previous number of 39.788 % for 2016. Philippines PH: Labour Force: Female: % of Total Labour Force data is updated yearly, averaging 38.052 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 39.899 % in 2017 and a record low of 37.378 % in 1990. Philippines PH: Labour Force: Female: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Labour Force. Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.; ; Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

This dataset provides the percentage distribution of females aged 15 years and above in Qatar by educational attainment. Data is disaggregated by age group, nationality (Qatari and Non-Qatari), and year. Educational levels range from illiterate to university and above. Blank values indicate unavailable or inapplicable data. This dataset supports analysis of gender and education trends by age and nationality over time.

Bolivia BO: Labour Force: Female: % of Total Labour Force data was reported at 46.239 % in 2024. This records an increase from the previous number of 46.081 % for 2023. Bolivia BO: Labour Force: Female: % of Total Labour Force data is updated yearly, averaging 41.893 % from Dec 1990 (Median) to 2024, with 35 observations. The data reached an all-time high of 46.239 % in 2024 and a record low of 41.054 % in 1990. Bolivia BO: Labour Force: Female: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bolivia – Table BO.World Bank.WDI: Labour Force. Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.;World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization and United Nations Population Division.;Weighted average;

Peru PE: Labour Force: Female: % of Total Labour Force data was reported at 45.422 % in 2017. This records an increase from the previous number of 45.334 % for 2016. Peru PE: Labour Force: Female: % of Total Labour Force data is updated yearly, averaging 44.036 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 46.029 % in 2010 and a record low of 37.311 % in 1990. Peru PE: Labour Force: Female: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Peru – Table PE.World Bank: Labour Force. Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.; ; Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor data retrieved in November 2017.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

Denmark DK: Labour Force: Female: % of Total Labour Force data was reported at 47.400 % in 2017. This records an increase from the previous number of 47.379 % for 2016. Denmark DK: Labour Force: Female: % of Total Labour Force data is updated yearly, averaging 46.785 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 47.601 % in 2013 and a record low of 45.208 % in 1995. Denmark DK: Labour Force: Female: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Denmark – Table DK.World Bank: Labour Force. Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.; ; Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor data retrieved in November 2017.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

United States US: Labour Force: Female: % of Total Labour Force data was reported at 45.821 % in 2017. This records a decrease from the previous number of 45.848 % for 2016. United States US: Labour Force: Female: % of Total Labour Force data is updated yearly, averaging 45.754 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 46.165 % in 2010 and a record low of 44.318 % in 1990. United States US: Labour Force: Female: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Labour Force. Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.; ; Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor data retrieved in November 2017.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

Saudi Arabia SA: Labour Force: Female: % of Total Labour Force data was reported at 16.184 % in 2017. This records a decrease from the previous number of 16.299 % for 2016. Saudi Arabia SA: Labour Force: Female: % of Total Labour Force data is updated yearly, averaging 14.241 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 16.356 % in 2015 and a record low of 10.622 % in 1990. Saudi Arabia SA: Labour Force: Female: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Labour Force. Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.; ; Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

Belgium BE: Labour Force: Female: % of Total Labour Force data was reported at 46.819 % in 2024. This records an increase from the previous number of 46.779 % for 2023. Belgium BE: Labour Force: Female: % of Total Labour Force data is updated yearly, averaging 44.694 % from Dec 1990 (Median) to 2024, with 35 observations. The data reached an all-time high of 46.827 % in 2022 and a record low of 39.030 % in 1990. Belgium BE: Labour Force: Female: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belgium – Table BE.World Bank.WDI: Labour Force. Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.;World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization and United Nations Population Division.;Weighted average;

Czech Republic CZ: Labour Force: Female: % of Total Labour Force data was reported at 44.993 % in 2024. This records an increase from the previous number of 44.961 % for 2023. Czech Republic CZ: Labour Force: Female: % of Total Labour Force data is updated yearly, averaging 44.205 % from Dec 1990 (Median) to 2024, with 35 observations. The data reached an all-time high of 44.993 % in 2024 and a record low of 43.304 % in 2010. Czech Republic CZ: Labour Force: Female: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Czech Republic – Table CZ.World Bank.WDI: Labour Force. Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.;World Bank, World Development Indicators database. Estimates are based on data obtained from International Labour Organization and United Nations Population Division.;Weighted average;

Qatar QA: Labour Force: Female: % of Total Labour Force data was reported at 14.070 % in 2017. This records an increase from the previous number of 13.996 % for 2016. Qatar QA: Labour Force: Female: % of Total Labour Force data is updated yearly, averaging 14.268 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 17.182 % in 2003 and a record low of 12.051 % in 2010. Qatar QA: Labour Force: Female: % of Total Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Qatar – Table QA.World Bank.WDI: Labour Force. Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.; ; Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

United States US: Unemployment with Intermediate Education: Female: % of Female Labour Force data was reported at 5.791 % in 2017. This records a decrease from the previous number of 6.524 % for 2016. United States US: Unemployment with Intermediate Education: Female: % of Female Labour Force data is updated yearly, averaging 6.771 % from Dec 1994 (Median) to 2017, with 24 observations. The data reached an all-time high of 13.389 % in 2010 and a record low of 4.732 % in 2000. United States US: Unemployment with Intermediate Education: Female: % of Female Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Employment and Unemployment. The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011).; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average;