This statistic shows the labor participation rate of women in the United States in 2004 and 2014, by race. In 2014, the labor participation rate of black women was **** percent, * percent higher than the rate for the total female population.

Graph and download economic data for Labor Force Participation Rate - Black or African American (LNS11300006) from Jan 1972 to Jul 2025 about African-American, participation, labor force, 16 years +, labor, household survey, rate, and USA.

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]

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.

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.

This statistic shows the labor force participation rate of parents with children under six in the United States in 2014, by race and gender. In 2014, the labor force participation rate of black women with children under six was **** percent, about ** percent higher than the rate for the total female population.

LFPR: Female: 35 to 39 Years: Non Citizens data was reported at 62.900 % in 2017. This records a decrease from the previous number of 64.700 % for 2016. LFPR: Female: 35 to 39 Years: Non Citizens data is updated yearly, averaging 61.600 % from Dec 1995 (Median) to 2017, with 22 observations. The data reached an all-time high of 65.200 % in 2014 and a record low of 44.300 % in 1995. LFPR: Female: 35 to 39 Years: Non Citizens data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.G032: Labour Force Survey: Labour Force: Participation Rates: By Ethnic Group.

Data on labour force status including employment, unemployment and labour force participation rates by visible minority, immigrant status and period of immigration, highest level of education, age and gender.

LFPR: Female: 30 to 34 Years: Citizens: Chinese data was reported at 81.800 % in 2017. This records an increase from the previous number of 80.700 % for 2016. LFPR: Female: 30 to 34 Years: Citizens: Chinese data is updated yearly, averaging 64.300 % from Dec 1995 (Median) to 2017, with 22 observations. The data reached an all-time high of 81.800 % in 2017 and a record low of 51.800 % in 1995. LFPR: Female: 30 to 34 Years: Citizens: Chinese data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.G032: Labour Force Survey: Labour Force: Participation Rates: By Ethnic Group.

Female Labor Force Participation: A Horse Race between Ancestral vs. Current Arable Land.

This dataset presents the total number of economically active individuals aged 15 years and above in the State of Qatar, disaggregated by nationality (Qatari and Non-Qatari) and gender (males and females) from 2018 to 2023. It includes annual totals for each subgroup and overall aggregates for males, females, and the entire population. This data is essential for analyzing labor force participation trends and informing workforce planning and policy.

LFPR: Female: 40 to 44 Years: Citizens: Others data was reported at 73.100 % in 2017. This records an increase from the previous number of 64.000 % for 2016. LFPR: Female: 40 to 44 Years: Citizens: Others data is updated yearly, averaging 51.500 % from Dec 1995 (Median) to 2017, with 22 observations. The data reached an all-time high of 73.100 % in 2017 and a record low of 35.400 % in 1996. LFPR: Female: 40 to 44 Years: Citizens: Others data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.G032: Labour Force Survey: Labour Force: Participation Rates: By Ethnic Group.

LFPR: Female: 45 to 49 Years: Citizens data was reported at 61.100 % in 2017. This records a decrease from the previous number of 61.600 % for 2016. LFPR: Female: 45 to 49 Years: Citizens data is updated yearly, averaging 49.550 % from Dec 1995 (Median) to 2017, with 22 observations. The data reached an all-time high of 61.600 % in 2016 and a record low of 45.300 % in 1995. LFPR: Female: 45 to 49 Years: Citizens data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.G032: Labour Force Survey: Labour Force: Participation Rates: By Ethnic Group.

LFPR: Female: 45 to 49 Years: Citizens: Chinese data was reported at 60.900 % in 2017. This records a decrease from the previous number of 62.100 % for 2016. LFPR: Female: 45 to 49 Years: Citizens: Chinese data is updated yearly, averaging 47.400 % from Dec 1995 (Median) to 2017, with 22 observations. The data reached an all-time high of 62.100 % in 2016 and a record low of 43.000 % in 1995. LFPR: Female: 45 to 49 Years: Citizens: Chinese data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.G032: Labour Force Survey: Labour Force: Participation Rates: By Ethnic Group.

Purpose: The purpose of this article was to study the association between hearing loss (HL) and labor force participation in the National Health and Nutrition Examination Survey (NHANES). Method: This cross-sectional study used data from the 1999–2000, 2001–2002, 2003–2004, 2011–2012, and 2015–2016 cycles of the NHANES. The sample was restricted to adults aged 25–65 years with complete audiometric data. HL was defined based on the pure-tone average (PTA) of 0.5-, 1-, 2-, and 4-kHz thresholds in the better hearing ear as follows: no loss (PTA < 25 dB), mild HL (25 dB < PTA < 40 dB), and moderate-to-severe HL (PTA > 40 dB). The association between HL and labor force participation was estimated using weighted logistic regression adjusted for age, sex, race/ethnicity, education, living arrangements, and health status. Results: In a sample of 9,963 participants (50.6% women, 22.6% Black, 27% Hispanic), we found that compared with adults without HL, individuals with moderate-to-severe HL had greater odds of being outside of the labor force (odds ratio = 2.35; 95% confidence interval: 1.42–3.88). However, there were no differences by HL status in being employed or having a full- versus part-time job. Conclusions: Moderate-to-severe HL, but not mild HL, was associated with higher odds of not participating in the labor force. However, there were no differences by HL status in being employed or having a full- versus part-time job. Further research is needed to better characterize how HL may affect labor force participation.

Supplemental Material S1. Weighted logistic regression model for the association between hearing loss and the odds of different labor outcomes, including self-reported hearing perception as a covariate.

Supplemental Material S2. Weighted logistic regression model for the association between better-ear PTA and the odds of different labor outcomes, including self-reported hearing perception as a covariate.

Garcia Morales, E. E., Lin, H., Suen, J. J., Varadaraj, V., Lin, F. R., & Reed, N. S. (2022). Labor force participation and hearing loss among adults in the United States: Evidence from the National Health and Nutrition Examination Survey. American Journal of Audiology. Advance online publication. https://doi.org/10.1044/2022_AJA-21-00266

Diversity in Tech Statistics: In today's tech-driven world, discussions about diversity in the technology sector have gained significant traction. Recent statistics shed light on the disparities and opportunities within this industry. According to data from various sources, including reports from leading tech companies and diversity advocacy groups, the lack of diversity remains a prominent issue. For example, studies reveal that only 25% of computing jobs in the United States are held by women, while Black and Hispanic individuals make up just 9% of the tech workforce combined. Additionally, research indicates that LGBTQ+ individuals are underrepresented in tech, with only 2.3% of tech workers identifying as LGBTQ+. Despite these challenges, there are promising signs of progress. Companies are increasingly recognizing the importance of diversity and inclusion initiatives, with some allocating significant resources to address these issues. For instance, tech giants like Google and Microsoft have committed millions of USD to diversity programs aimed at recruiting and retaining underrepresented talent. As discussions surrounding diversity in tech continue to evolve, understanding the statistical landscape is crucial in fostering meaningful change and creating a more inclusive industry for all. Editor’s Choice In 2021, 7.9% of the US labor force was employed in technology. Women hold only 26.7% of tech employment, while men hold 73.3% of these positions. White Americans hold 62.5% of the positions in the US tech sector. Asian Americans account for 20% of jobs, Latinx Americans 8%, and Black Americans 7%. 83.3% of tech executives in the US are white. Black Americans comprised 14% of the population in 2019 but held only 7% of tech employment. For the same position, at the same business, and with the same experience, women in tech are typically paid 3% less than men. The high-tech sector employs more men (64% against 52%), Asian Americans (14% compared to 5.8%), and white people (68.5% versus 63.5%) compared to other industries. The tech industry is urged to prioritize inclusion when hiring, mentoring, and retaining employees to bridge the digital skills gap. Black professionals only account for 4% of all tech workers despite being 13% of the US workforce. Hispanic professionals hold just 8% of all STEM jobs despite being 17% of the national workforce. Only 22% of workers in tech are ethnic minorities. Gender diversity in tech is low, with just 26% of jobs in computer-related sectors occupied by women. Companies with diverse teams have higher profitability, with those in the top quartile for gender diversity being 25% more likely to have above-average profitability. Every month, the tech industry adds about 9,600 jobs to the U.S. economy. Between May 2009 and May 2015, over 800,000 net STEM jobs were added to the U.S. economy. STEM jobs are expected to grow by another 8.9% between 2015 and 2024. The percentage of black and Hispanic employees at major tech companies is very low, making up just one to three percent of the tech workforce. Tech hiring relies heavily on poaching and incentives, creating an unsustainable ecosystem ripe for disruption. Recruiters have a significant role in disrupting the hiring process to support diversity and inclusion. You May Also Like To Read Outsourcing Statistics Digital Transformation Statistics Internet of Things Statistics Computer Vision Statistics

يعرض هذا الملف الإحصائي معدل المشاركة الاقتصادية للإناث من عمر 15 سنة فأكثر في دولة قطر، موزعًا حسب الفئات العمرية والجنسية (قطريات وغير قطريات). يتم عرض القيم كنسب مئوية بدقة عشرية. توفر البيانات رؤى حول اتجاهات مشاركة المرأة في القوى العاملة عبر الفئات العمرية والمجموعات السكانية المختلفة، مما يدعم التخطيط في مجالات العمل والمساواة بين الجنسين والسياسات الاجتماعية والاقتصادية.

The data this week comes from the National Database of Childcare Prices.

Data Dictionary

childcare_costs.csv

On 31 March 2024, 91.6% of police officers were White, and 8.4% were from Asian, Black, Mixed, and Other ethnic backgrounds.

In the second quarter of 2024, the unemployment rate among Black South Africans was 36.9 percent, marking a year-on-year change of 0.8 percent compared to the second quarter of 2023. On the other hand, the unemployment rate among white South Africans was 7.9 percent in the second quarter of 2024, with a 0.5 percent year-on-year change. Unemployment prevalent among youth and women The unemployment rate is the share of the labor force population that is unemployed, while the labor force includes individuals who are employed as well as those who are unemployed but looking for work. South Africa is struggling to absorb its youth into the job market. For instance, the unemployment rate among young South Africans aged 15-24 years reached a staggering 60.7 percent in the second quarter of 2023. Furthermore, women had higher unemployment rates than men. Since the start of 2016, the unemployment rate of women has been consistently more than that of men, reaching close to 36 percent compared to 30 percent, respectively. A new minimum wage and most paying jobs      In South Africa, a new minimum hourly wage went into effect on March 1, 2022. The minimum salary reached 23.19 South African rand per hour (1.44 U.S. dollars per hour), up from 21.69 South African rand per hour (1.35 U.S. dollars per hour) in 2021. In addition, the preponderance of employed South Africans worked between 40 and 45 hours weekly in 2021. Individuals holding Executive Management and Change Management jobs were the highest paid in the country, with salaries averaging 74,000 U.S. dollars per year.