In 2023, the unemployment rate of African Americans in the United States stood at 5.5 percent. This was over the national average of 3.6 percent.

The high rate of unemployment

There are many reasons why the unemployment rate among minorities is different than the national average. When it comes to African Americans, a large part of this is due to historical events, such as slavery and the struggle for civil rights, as well as the number of Black families living below the poverty level. Additionally, in 2019, for every 100,000 of the population, there were 2,203 Black men in prison. This high rate of imprisonment can contribute to the unemployment rate for African Americans, since having been in prison can reduce one’s chances of finding a job once released.

Earning differences

African Americans also make less money than other ethnicities in the United States. In 2020, the median weekly earnings of African Americans were 794 U.S. dollars, compared to Asians, who made 1,310 U.S. dollars per week, and whites, who made 1,003 U.S. dollars per week. While the African American unemployment rate may be low, it is clear that much has to change in order to achieve full equality.

In 1990, the unemployment rate of the United States stood at 5.6 percent. Since then there have been many significant fluctuations to this number - the 2008 financial crisis left millions of people without work, as did the COVID-19 pandemic. By the end of 2022 and throughout 2023, the unemployment rate came to 3.6 percent, the lowest rate seen for decades. However, 2024 saw an increase up to four percent. For monthly updates on unemployment in the United States visit either the monthly national unemployment rate here, or the monthly state unemployment rate here. Both are seasonally adjusted. UnemploymentUnemployment is defined as a situation when an employed person is laid off, fired or quits his work and is still actively looking for a job. Unemployment can be found even in the healthiest economies, and many economists consider an unemployment rate at or below five percent to mean there is 'full employment' within an economy. If former employed persons go back to school or leave the job to take care of children they are no longer part of the active labor force and therefore not counted among the unemployed. Unemployment can also be the effect of events that are not part of the normal dynamics of an economy. Layoffs can be the result of technological progress, for example when robots replace workers in automobile production. Sometimes unemployment is caused by job outsourcing, due to the fact that employers often search for cheap labor around the globe and not only domestically. In 2022, the tech sector in the U.S. experienced significant lay-offs amid growing economic uncertainty. In the fourth quarter of 2022, more than 70,000 workers were laid off, despite low unemployment nationwide. The unemployment rate in the United States varies from state to state. In 2021, California had the highest number of unemployed persons with 1.38 million out of work.

The unemployment rate for people ages 16 to 24 in the United States in 202024 23 was 10 percent. However, this rate was much lower for people aged 45 and over, at 2.9 percent. U.S. unemployment The unemployment rate in the United States varies based on several factors, such as race, gender, and level of education. Black and African-American individuals had the highest unemployment rate in 2021 out of any ethnicity, and people who had less than a high school diploma had the highest unemployment rate by education level. Alaska is consistently the state with the highest unemployment rate, although the El Centro, California metropolitan area was the area with the highest unemployment rate in the country in 2019. Additionally, in August 2022, farming, fishing, and forestry occupations had the highest unemployment rate in the United States Unemployment rate The U.S. Bureau of Labor Statistics is the agency that researches and calculates the unemployment rate in the United States. Unemployment rises during recessions, which causes the cost of social welfare programs to increase. The Bureau of Labor Statistics says unemployed people are those who are jobless, have looked for employment within the last four weeks, and are free to work.

Unemployment Rate in Australia remained unchanged at 4.10 percent in May. This dataset provides - Australia Unemployment Rate at 5.8% in December - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Unemployment Rate in South Africa increased to 32.90 percent in the first quarter of 2025 from 31.90 percent in the fourth quarter of 2024. This dataset provides - South Africa Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This interactive chart compares three different measures of unemployment. U3 is the official unemployment rate. U5 includes discouraged workers and all other marginally attached workers. U6 adds on those workers who are part-time purely for economic reasons.

Unemployment Rate in Canada decreased to 6.90 percent in June from 7 percent in May of 2025. This dataset provides - Canada Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2015 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Armed Forces data are not shown for the population 65 years and over..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2015 American Community Survey 1-Year Estimates

Unemployment Rate in the United Kingdom increased to 4.60 percent in April from 4.50 percent in March of 2025. This dataset provides the latest reported value for - United Kingdom Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Data are aggregated from census tract to Countywide Statistical Area (CSA).Link to full report, State of Black LA.For more information about the purpose of this data, please contact CEO-ARDI.For more information about the configuration of this data, please contact ISD-Enterprise GIS. Field Descriptions:

Field

Description

Source

Source Year

csa

Countywide Statistical Area

eGIS

2022

sd

Supervisorial District

eGIS

2021

med_income_total

Average median household income for all residents

US Census ACS 5-year table S1903

2020

med_income_black

Average median household income for Black residents

US Census ACS 5-year table S1903

2020

homeownership_total

Homeownership rate for all residents

US Census ACS 5-year table B25003

2020

homeownership_black

Homeownership rate for Black residents

US Census ACS 5-year table B25003B

2020

eviction_filings_per100_renters

Eviction filings per 100 renter households

The Eviction Lab

2002-2018 (yearly average of available years)

life_expectancy

Average life expectancy

CDC

2015

black_pop

Black population (alone or in combination)

US Census ACS 5-year table DP05

2020

black_pct

% Black population (alone or in combination)

US Census ACS 5-year table DP05

2020

nh_black_pop

Non-Hispanic Black alone population

US Census ACS 5-year table DP05

2020

nh_black_pct

% Non-Hispanic Black alone population

US Census ACS 5-year table DP05

2020

college_grad

Population of residents age 25+ with bachelor degree or higher

US Census ACS 5-year table DP02

2020

college_grad_pct

% of all residents age 25+ with bachelor degree or higher

US Census ACS 5-year table DP02

2020

college_grad_black

Population of Black residents age 25+ with bachelor degree or higher

US Census ACS 5-year table S1501

2020

college_grad_black_pct

% of Black residents age 25+ with bachelor degree or higher

US Census ACS 5-year table S1501

2020

unemployment

Unemployment Rate

US Census ACS 5-year table S2301

2020

unemployment_black

Black (Alone) Unemployment Rate

US Census ACS 5-year table S2301

2020

total_pop

Total population

US Census ACS 5-year table DP05

2020

Shape

CSA Geometry

eGIS

2022

Since the mid-2000s the percentage of people looking for a job in France has gradually increased. However, one pattern remained the same. The immigrant population remains more affected by this phenomenon. In 2023, more than **** percent of immigrants were jobless in France, compared to *** percent of the non-immigrant population. The characteristics of unemployment in France Ever since the 2008 financial crisis, France has been struggling with the issue of unemployment. The unemployment rate reached a record number in 2015, with ***** percent. Even though France has seen an improvement in its unemployment rate since then, this keeps being one of the country’s main difficulties. In 2023, the unemployment rate for women who were foreigners amounted to **** percent. In comparison, that same year, the unemployment rate of French women was *** percent. Facing discriminations In a survey from 2016, ** percent of responding French declared that they have already experienced discrimination based on gender, age, origin, skin, color, religion, or health condition during their professional career. Work areas like career or job search were among the fields in which the interviewees reported to have suffered the most from discrimination. Age, gender, and origin or skin color were said to be factors that could lead to discrimination not only at work but in everyday life. In 2016, ** percent of French individuals who have experienced discrimination when looking for a property to rent stated that this discrimination was related to their skin color or their origins.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

In any society, the human element represents the basis of the work force which exercises all the service and production activities. Therefore, it is a mandate to produce labor force statistics and studies, that is related to the growth and distribution of manpower and labor force distribution by different types and characteristics.

In this context, the Central Agency for Public Mobilization and Statistics conducts "Quarterly Labor Force Survey" which includes data on the size of manpower and labor force (employed and unemployed) and their geographical distribution by their characteristics.

By the end of each year, CAPMAS issues the annual aggregated labor force bulletin publication that includes the results of the quarterly survey rounds that represent the manpower and labor force characteristics during the year.

---> Historical Review of the Labor Force Survey:

1- The First Labor Force survey was undertaken in 1957. The first round was conducted in November of that year, the survey continued to be conducted in successive rounds (quarterly, bi-annually, or annually) till now.

2- Starting the October 2006 round, the fieldwork of the labor force survey was developed to focus on the following two points: a. The importance of using the panel sample that is part of the survey sample, to monitor the dynamic changes of the labor market. b. Improving the used questionnaire to include more questions, that help in better defining of relationship to labor force of each household member (employed, unemployed, out of labor force ...etc.). In addition to re-order of some of the already existing questions in much logical way.

3- Starting the January 2008 round, the used methodology was developed to collect more representative sample during the survey year. This is done through distributing the sample of each governorate into five groups, the questionnaires are collected from each of them separately every 15 days for 3 months (in the middle and the end of the month)

4- Starting the January 2012 round, in order to follow the international recommendation, to avoid asking extra questions that affect the precision and accuracy of the collected data, a shortened version of the questionnaire was designed to include the core questions that enable obtaining the basic Egyptian labor market indicators. The shortened version is collected in two rounds (January-March), (April-June), and (October-December) while the long version of the questionnaire is collected in the 3rd round (July-September) that includes more information on housing conditions and immigration.

---> The survey aims at covering the following topics:

1- Measuring the size of the Egyptian labor force among civilians (for all governorates of the republic) by their different characteristics. 2- Measuring the employment rate at national level and different geographical areas. 3- Measuring the distribution of employed people by the following characteristics: Gender, age, educational status, occupation, economic activity, and sector. 4- Measuring unemployment rate at different geographic areas. 5- Measuring the distribution of unemployed people by the following characteristics: Gender, age, educational status, unemployment type “ever employed/never employed”, occupation, economic activity, and sector for people who have ever worked.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

---> Sample Design and Selection

The sample of the LFS 2015 survey is a self-weighted two-stage stratified cluster sample. The main elements of the sampling design are described as follows:

• Sample Size The sample size in each quarter is 22,896 households with a total number of 91,584 households annually. These households are distributed on the governorate level (urban/rural), according to the estimated number of households in each governorate in accordance with the percentage of urban and rural population in each governorate.

• Cluster size The cluster size is 18 households.

• Sampling stages:

  (1) Primary Sampling Unit (PSU): The 2006 Population Census provided sufficient data at the level of the Enumeration Area (EA). Hence, the electronic list of EA's represented the frame of the first stage sample; in which the corresponding number of households per EA was taken as a measure of size. The size of an EA is almost 200 households on average, with some variability expected. The size of first stage national sample was estimated to be 5,024 EA.

  (2) Sample Distribution by Governorate: The primary stratifying variable is the governorate of residence, which in turn is divided into urban and rural sub-strata, whenever applicable.

  (3) First Stage Sample frame: The census lists of EAs for each substratum, associated with the corresponding number of households, constitute the frame of the first stage sample. The identification information appears on the EA's list includes the District code, Shiakha/Village code, Census Supervisor number, and Enumerator number. Prior to the selection of the first stage sample, the frame was arranged to provide implicit stratification with regard to the geographic location. The urban frame of each governorate was ordered in a serpentine fashion according to the geographic location of kism/ district capitals. The same sort of ordering was made on the rural frame, but according to the district location. The systematic selection of EA's sample from such a sorted frame will ensure a balanced spread of the sample over the area of respective governorates. The sample was selected with Probability Proportional to Size (PPS), with the number of census households taken as a Measure of Size (MOS).

  (4) Core Sample allocation The core sample EAs (5,024) were divided among the survey 4 rounds, each round included 1,272 EAs (588 in urban areas and 684 in rural areas).

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among external resources in Arabic.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire design follows the latest International Labor Organization (ILO) concepts and definitions of labor force, employment, and unemployment.

The questionnaire comprises 4 tables in addition to the identification and geographic data of household on the cover page.

---> Table 1- The housing conditions of the households

This table includes information on the housing conditions of the household: - Type of the dwelling, - Tenure of the dwelling (owned/rent) , - Availability of facilities and services connected to the house - Ownership of durables.

---> Table 2- Demographic and employment characteristics and basic data for all household individuals

Including: gender, age, educational status, marital status, residence mobility and current work status

---> Table 3- Employment characteristics table

This table is filled by employed individuals at the time of the survey or those who were engaged to work during the reference week, and provided information on: - Relationship to employer: employer, self-employed, waged worker, and unpaid family worker - Economic activity - Sector - Occupation - Effective working hours - Health and social insurance - Work place - Contract type - Average monthly wage

---> Table 4- Unemployment characteristics table

This table is filled by all unemployed individuals who satisfied the unemployment criteria, and provided information on: - Type of unemployment (unemployed, unemployed ever worked) - Economic activity and occupation in the last held job before being unemployed - Last unemployment duration in months - Main reason for unemployment

Cleaning operations

---> Raw Data

Office editing is one of the main stages of the survey. It started once the questionnaires were received from the field and accomplished by the selected work groups. It includes: a-Editing of coverage and completeness b-Editing of consistency

---> Harmonized Data

• The SPSS package is used to clean and harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A

Release Date: 2017-07-13.[NOTE: Includes firms with payroll at any time during 2015. Employment reflects the number of paid employees during the March 12 pay period. Data are based on Census administrative records, and the estimates of business ownership by gender, ethnicity, race, and veteran status are from the 2015 Annual Survey of Entrepreneurs. Detail may not add to total due to rounding or because a Hispanic firm may be of any race. Moreover, each owner had the option of selecting more than one race and therefore is included in each race selected. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Survey Methodology.]..Table Name. . Statistics for U.S. Employer Firms by Sector, Gender, Ethnicity, Race, Veteran Status, and Employment Size of Firm for the U.S., States, and Top 50 MSAs: 2015. ..Release Schedule. . This file was released in July 2017.. ..Key Table Information. . These data are related to all other 2015 ASE files.. Refer to the Methodology section of the Annual Survey of Entrepreneurs website for additional information.. ..Universe. . The universe for the 2015 Annual Survey of Entrepreneurs (ASE) includes all U.S. firms with paid employees operating during 2015 with receipts of $1,000 or more which are classified in the North American Industry Classification System (NAICS) sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. total.. ..Geographic Coverage. . The data are shown for:. . United States. States and the District of Columbia. The fifty most populous metropolitan areas. . ..Industry Coverage. . The data are shown for the total of all sectors (00) and the 2-digit NAICS code level.. ..Data Items and Other Identifying Records. . Statistics for U.S. Employer Firms by Sector, Gender, Ethnicity, Race, Veteran Status, and Employment Size of Firm for the U.S., States, and Top 50 MSAs: 2015 contains data on:. . Number of firms with paid employees. Sales and receipts for firms with paid employees. Number of employees for firms with paid employees. Annual payroll for firms with paid employees. . The data are shown for:. . All firms classifiable by gender, ethnicity, race, and veteran status. . Gender. . Female-owned. Male-owned. Equally male-/female-owned. . . Ethnicity. . Hispanic. Equally Hispanic/non-Hispanic. Non-Hispanic. . . Race. . White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Some other race. Minority. Equally minority/nonminority. Nonminority. . . Veteran Status. . Veteran-owned. Equally veteran-/nonveteran-owned. Nonveteran-owned. . . . . Publicly held and other firms not classifiable by gender, ethnicity, race, and veteran status. Employment size of firm during the March 12 pay period for firms with paid employees at any time during 2015. . All firms. Firms with no employees. Firms with 1 to 4 employees. Firms with 5 to 9 employees. Firms with 10 to 19 employees. Firms with 20 to 49 employees. Firms with 50 to 99 employees. Firms with 100 to 249 employees. Firms with 250 to 499 employees. Firms with 500 employees or more. . . . ..Sort Order. . Data are presented in ascending levels by:. . Geography (GEO_ID). NAICS code (NAICS2012). Gender (SEX). Ethnicity (ETH_GROUP). Race (RACE_GROUP). Veteran Status (VET_GROUP). Employment size of firm (EMPSZFI). . The data are sorted on underlying control field values, so control fields may not appear in alphabetical order.. ..FTP Download. . Download the entire SE1500CSA04 table at: https://www2.census.gov/programs-surveys/ase/data/2015/SE1500CSA04.zip. ..Contact Information. . To contact the Annual Survey of Entrepreneurs staff:. . Visit the website at http://www.census.gov/programs-surveys/ase.html.. Em...

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

In 2023, 5.4 percent of the labor force in Canada was unemployed. This is a slight increase from the previous year, when unemployment stood at 5.3 percent.

The statistic shows the share of U.S. population, by race and Hispanic origin, in 2016 and a projection for 2060. As of 2016, about 17.79 percent of the U.S. population was of Hispanic origin. Race and ethnicity in the U.S. For decades, America was a melting pot of the racial and ethnical diversity of its population. The number of people of different ethnic groups in the United States has been growing steadily over the last decade, as has the population in total. For example, 35.81 million Black or African Americans were counted in the U.S. in 2000, while 43.5 million Black or African Americans were counted in 2017.

The median annual family income in the United States in 2017 earned by Black families was about 50,870 U.S. dollars, while the average family income earned by the Asian population was about 92,784 U.S. dollars. This is more than 15,000 U.S. dollars higher than the U.S. average family income, which was 75,938 U.S. dollars.

The unemployment rate varies by ethnicity as well. In 2018, about 6.5 percent of the Black or African American population in the United States were unemployed. In contrast to that, only three percent of the population with Asian origin was unemployed.

The unemployment rate in South Africa increased by 1.1 percentage points (+3.43 percent) in 2024 in comparison to the previous year. In total, the unemployment rate amounted to 33.17 percent in 2024. This increase was preceded by a declining unemployment rate.The unemployment rate refers to the share of the workforce that is currently not working but is actively searching for work. It does not include the economically inactive population, such as the long-term unemployed, those aged under 15 years, or retired persons.Find more statistics on other topics about South Africa with key insights such as gross tertiary enrollment ratio, youth literacy rate (people aged 15-24), and Gender Parity Index (GPI) in youth literacy.

The SCAG_ATDB_Demographics shapefile contains Census tract level population, race, employment, English speaking, income, and elderly data of the SCAG region. Race data includes the percentage of population that is white, black, Asian, Latino, Pacific Islander, Native American, multiple races, or other. Population data includes 2010 population 2015 population, and population density. Employment data includes 2015 employment, unemployment, and employment density. English speaking data includes the percentage of the population that speaks English well. This shapefile also includes median household income and percentage of the population that is 65 years or older. This data was sourced mostly from Census data as well as the Healthy Places Index (HPI). Original data sources are listed in the relevant fields.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical Documentation.. section......Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the .Methodology.. section..Source: U.S. Census Bureau, 2018 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see .ACS Technical Documentation..). The effect of nonsampling error is not represented in these tables..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to .Labor Force Guidance....Armed Forces data are not shown for the population 65 years and over..While the 2018 American Community Survey (ACS) data generally reflect the July 2015 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas, in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:..An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself..An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution..An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution..An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An "(X)" means that the estimate is not applicable or not available....