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.

Context

There has been a lot of discussion of the ways in which the workforce for Silicon Valley tech companies differs from that of the United States as a whole. In particular, a lot of evidence suggests that tech workers (who tend to be more highly paid than workers in many other professions) are more likely to be white and male. This dataset will allow you to investigate the demographics for 23 Silicon Valley tech companies for yourself.

Updates!

NEW June 2018: The spreadsheet Distributions_data_2016.csv contains workforce distributions by job category and race for 177 of the largest tech companies headquartered in Silicon Valley.

Each figure in the dataset represents the percentage of each job category that is made up of employees with a given race/gender combination, and are based on each company's EEO-1 report.

This dataset was created through a unique collaboration with the Center for Employment Equity and Reveal. The equity center provided Reveal with anonymized data for 177 large companies, and Reveal identified companies that have publicly released their data in this anonymized dataset. The equity center and Reveal analyzed the data independently.

For more information on the data, read our post here.

The spreadsheet Reveal_EEO1_for_2016.csv has been updated to include EEO-1s from companies PayPal, NetApp and Sanmina for 2016. The race and job categories have been modified to ensure consistency across all the datasets.

NEW April 2018: The spreadsheet Tech_sector_diversity_demographics_2016.csv contains aggregated diversity data for 177 large Silicon Valley tech companies. We calculated averages for the largest race and gender groups across job categories. For information on the aggregated data, read our post here.

This repository also contains EEO-1 reports filed by Silicon Valley tech companies. Please read our complete methodology for details on this data.

The data was compiled by Reveal from The Center for Investigative Reporting.

Contents

This database contains EEO-1 reports filed by Silicon Valley tech companies. It was compiled by Reveal from The Center for Investigative Reporting.

There are six columns in this dataset:

• company: Company name
• year: For now, 2016 only
• race: Possible values: "American_Indian_Alaskan_Native", "Asian", "Black_or_African_American", "Latino", "Native_Hawaiian_or_Pacific_Islander", "Two_or_more_races", "White", "Overall_totals"
• gender: Possible values: "male", "female". Non-binary gender is not counted in EEO-1 reports.
• job_category: Possible values: "Administrative support", "Craft workers", "Executive/Senior officials & Mgrs", "First/Mid officials & Mgrs", "laborers and helpers", "operatives", "Professionals", "Sales workers", "Service workers", "Technicians", "Previous_totals", "Totals"
• count: Mostly integer values, but contains "na" for a no-data variable.

Acknowledgements:

The EEO-1 database is licensed under the Open Database License (ODbL) by Reveal from The Center for Investigative Reporting.

You are free to copy, distribute, transmit and adapt the spreadsheet, so long as you:

• credit Reveal (including this link if it’s distributed online);
• inform Reveal that you are using the data in your work by emailing Sinduja Rangarajan at srangarajan@revealnews.org; and
• offer any new work under the same license.

Inspiration:

• How does each company’s workforce compare to the United States population as a whole? You can find county level diversity information here.
• Which company is the most diverse? Least diverse?

Between April 2022 and March 2023, there were 24.5 stop and searches for every 1,000 black people in England and Wales. There were 5.9 for every 1,000 white people.

Infant mortality rates in the United States reveal significant disparities among racial and ethnic groups. In 2023, Black mothers faced the highest rate at nearly 11 deaths per 1,000 live births, more than double the rate for white mothers. This stark contrast persists despite overall improvements in healthcare and highlights the need for targeted interventions to address these inequalities. Birth rates and fertility trends While infant mortality rates vary, birth rates also differ across ethnicities. Native Hawaiian and Pacific Islander women had the highest fertility rate in 2022, with about 2,237.5 births per 1,000 women, far exceeding the national average of 1,656.5. In 2023, this group maintained the highest birth rate at 79 births per 1,000 women. Asian women, by contrast, had a much lower birth rate of around 50 per thousand women. These differences in fertility rates can impact overall population growth and demographic shifts within the United States. Hispanic birth trends and fertility decline The Hispanic population in the United States has experienced significant changes in birth trends over recent decades. In 2021, 885,916 babies were born to Hispanic mothers, with a birth rate of 14.1 per 1,000 of the Hispanic population. This represents a slight increase from the previous year. However, the fertility rate among Hispanic women has declined dramatically since 1990, dropping from 108 children per 1,000 women aged 15-44 to 63.4 in 2021. This decline aligns with broader trends of decreasing fertility rates in more industrialized nations.

Introduction

As a part of the Google Data Analytics Professional Certificate Program, this case study serves as a data analytics adventure and a way to dive into something personal. While many face the difficulty of finding employment out of college, it became especially tedious to do so due to the COVID-19 pandemic. As such, this case study revolves around unemployment trends from 2021 using data sourced from the United States Bureau of Labor Statistics. I used datasets surrounding unemployment and employment trends in 2021 to answer the following:

Questions

1. What methods for job searching were the most prevalent across age ranges? Across gender/race/Hispanic-Latino ethnicity?
2. What trends exist between and within the most prevalent venues for job searching among the unemployed?
3. What job sector(s) does the majority of the population comprise? What trends exist within and between the most popular job sector and the least popular job sector? What relationship do these factors have with race/gender/Hispanic-Latino ethnicity?
4. How does information about prevalent job searching influence the job market and the applicants in the job search phase?

Insights (see the data section below for charts, graphs, and the .Rmd file I utilized)

• In 2021, the unemployed, with ages ranging from 16-65, preferred resumes and applications as their method for seeking out jobs. This method was especially prevalent in the age range 16-34, where, the highest bracket of job seekers were 24-35 years old. A close second was contacting an employer directly, primarily used by 45-64-year-olds. When considering gender/ethnicity/race, however, compared to their male counterparts, white women and women of color were the highest users of the resumes and applications method. However, white males and men of color were the highest users of the contacting employers directly method.
• Among the unemployed resumes were overall the most prevalent method of applying for jobs in 2021, where, people aged 16--34 and women regardless of ethnicity/race were the most likely to utilize this method to search for jobs.
• The majority of the population resides in the "Management, Professional, and related occupations" job sector, with the least popular form of occupations being in the "Farming, Fishing, and Forestry" sector. This sentiment can be found almost across all genders/races/ethnicities, though, some other job sectors like "Production, transportation, and material moving occupations" and "Natural resources, construction, and maintenance occupations" were more prevalent concerning the Black/African American men, Hispanic/Latino women, and Hispanic/Latino men respectively.
• This information is highly useful for job industries, specifically, those in the "Management, Professional, and related occupations" sector. With this, industries in this job sector can project what their incoming job applicant pool may look like and how to prepare for making the application process more accessible. This information can also serve to reinforce fairness and inclusivity in the job application process and in the work environment.

** Overall**

Using this information a company can project in 2022-2023 the majority of applicants will either apply to jobs using resumes/applications, the majority of these applicants may be 16-34 years old, and women regardless of ethnicity and race. They can also look out for applicants who are older, 45-64 years old, and applicants who are men regardless of ethnicity and race, being more likely to contact them as an employer directly. If an employer prefers to be directly contacted, they should make sure to consider the difficulties that people of different race/ethnic/and gender identities may have done so, and, either should either make the job positing more welcoming and inclusive to do so or, be sure to include a process of hiring via resumes/applications in order to better represent the unemployed population seeking jobs.

This feature layer was created using Census 2016 data produced by the Central Statistics Office (CSO) and Settlements boundary data (generalised to 20m) produced by Tailte Éireann. The layer represents Census 2016 theme 2.2, the population usually resident in Ireland by ethnic or cultural background. Attributes include population breakdown by ethnicity or cultural background (e.g. Asian or Asian Irish, White Irish). Census 2016 theme 2 represents Migration, Ethnicity and Religion. The Census is carried out every five years by the CSO to determine an account of every person in Ireland. The results provide information on a range of themes, such as, population, housing and education. The data were sourced from the CSO. In order to distinguish between the urban and rural population for census analysis, the boundaries of distinct settlements need to be defined. This requires the creation of suburbs and extensions to existing cities and legal towns as well as delineating boundaries for settlements which are not legally defined (called Census towns). From 1971 to 2006, Census towns were defined as a cluster of fifty or more occupied dwellings where, within a radius of 800 metres there was a nucleus of thirty occupied dwellings (on both sides of a road, or twenty on one side of a road), along with a clearly defined urban centre e.g. a shop, a school, a place of worship or a community centre. Census town boundaries where extended over time where there was an occupied dwelling within 200 metres of the existing boundary. To avoid the agglomeration of adjacent towns caused by the inclusion of low density one off dwellings on the approach routes to towns, the 2011 criteria were tightened, in line with UN criteria. In Census 2011 a new Census town was defined as being a cluster with a minimum of 50 occupied dwellings, with a maximum distance between any dwelling and the building closest to it of 100 metres, and where there was evidence of an urban centre (shop, school etc). The proximity criteria for extending existing 2006 Census town boundaries was also amended to include all occupied dwellings within 100 metres of an existing building. Other information based on Tailte Éireann mapping and orthogonal photography was taken into account when extending boundaries. Boundary extensions were generally made to include the land parcel on which a dwelling was built or using other physical features such as roads, paths etc. Extensions to the environs and suburbs of legal towns and cities were also constructed using the 100 metre proximity rule applied to Census towns. For census reports, urban settlements are towns with a population of 1,500 or more, while settlements with a population of less than 1,500 are classified as rural.

In 2022, about 37.7 percent of the U.S. population who were aged 25 and above had graduated from college or another higher education institution, a slight decline from 37.9 the previous year. However, this is a significant increase from 1960, when only 7.7 percent of the U.S. population had graduated from college. Demographics Educational attainment varies by gender, location, race, and age throughout the United States. Asian-American and Pacific Islanders had the highest level of education, on average, while Massachusetts and the District of Colombia are areas home to the highest rates of residents with a bachelor’s degree or higher. However, education levels are correlated with wealth. While public education is free up until the 12th grade, the cost of university is out of reach for many Americans, making social mobility increasingly difficult. Earnings White Americans with a professional degree earned the most money on average, compared to other educational levels and races. However, regardless of educational attainment, males typically earned far more on average compared to females. Despite the decreasing wage gap over the years in the country, it remains an issue to this day. Not only is there a large wage gap between males and females, but there is also a large income gap linked to race as well.

Households in the Indian and White Other ethnic groups received the highest percentage of their income from employment out of all ethnic groups.

In the 2022 to 2023 school year, pupils from the Chinese ethnic group had the highest Attainment 8 score out of all ethnic groups (65.5 out of 90.0).