Meta Platforms had 74,067 full-time employees as of December 2024, down from 67317 people in 2023. As of December 2023, more than 262,000 employees at tech companies worldwide were laid off throughout the year across more than one thousand companies. Facebook: how it all beganIn 2003, a sophomore at named Mark Zuckerberg hacked into protected areas of the university's computer network in order to find photos of other students. He then would pair two of them next to each other on a program called “Facemash” and ask users to choose the more attractive person. At the beginning of 2004, Zuckerberg launched “The Facebook,” a social network dedicated to Harvard students, which later grew to encompass Columbia, Yale and Stanford. The popularity of this new service sky-rocketed and in mid-2004, Zuckerberg interrupted his studies and moved his operation to Palo Alto, California, in the heart of Silicon Valley. By 2006, Facebook was open to the general public. In 2020, the company reported almost 86 billion U.S. dollars in revenue and a net income of 29.15 billion US dollars. It is also the most popular social network in the world, with 2.7 billion monthly active users as of December 2020. Facebook employee diversity criticismLike many other tech companies, Facebook has been criticized for having a diversity problem. As of June 2020, tech positions, as well as management roles in U.S. offices were overwhelmingly occupied by men. Furthermore, almost 41 percent of Facebook employees in the U.S. are White and only 3.9 percent are African-American, which has sparked concern regarding representation and equal opportunities. Around 63.2 percent of senior level positions are occupied by White employees and only 4.3 percent by Hispanic-Americans.

Meta reported 67.32K in Employees for its fiscal year ending in December of 2023. Data for Meta | FB - Employees Total Number including historical, tables and charts were last updated by Trading Economics this last March in 2025.

In fiscal year 2024, the total number of employees at Meta Platforms was 74,067. The employee count increasedby 6,750 from 67,317 (in 2023) to 74,067 (in 2024). It represents a 10.03% year-over-year growth in employee count.

In 2023, Amazon.com was the top-ranked internet company based on number of employees. The e-commerce giant reported a workforce of more than 1.52 million employees. Amazon has consistently topped the ranking as the online company with the biggest workforce, but the global COVID-19 pandemic has widened the gap as e-commerce has boomed since. During the same period, Meta (formerly Facebook Inc.) had a total of 67,317 full-time employees. Additionally, Google's parent company Alphabet had 183,323 full-time workers in 2024.

In 2022, 6.5 percent of Meta employees in the United States identified as Hispanic and 4.9 percent identified as Black. Asian employees accounted for over 46.5 percent of the overall workforce, whilst white employees made up 37.6 percent of Meta's workforce.

As of June 2022, 57.6 percent of employees in leadership roles at Meta were white, whilst 28.6 percent were Asian. Overall, 11.7 percent of employees in non-technical roles were Hispanic, and 11.2 percent were Black. Moreover, Asian employees accounted for the majority of employees in technical roles, making up 55.8 percent of employees in these positions.

As of June 2022, only 37.1 percent of all global Meta Platforms employees were women. The majority of employees were male. Overall, women made up 25.8 percent of tech roles and 60.5 percent of non-tech roles.

According to a 2022 report, 46.7 percent of global Meta employees were underrepresented people. Overall, 6.2 percent of employees self reported as disabled, and ten percent self reported as being a member of the LBGTQ+ community. Additionally, 2.3 percent of Meta's workforce were veterans.

Meta Platforms's CEO salary and other executives compensation in 2023 was as follows: Javier Olivan Chief Operating Officer at Meta Platforms, received a total compensation of $25.56M in 2023, Mark Zuckerberg Chief Executive Officer at Meta Platforms, received a total compensation of $24.40M in 2023, Christopher K. Cox Chief Product Officer at Meta Platforms, received a total compensation of $23.51M in 2023, Andrew Bosworth Chief Technology Officer at Meta Platforms, received a total compensation of $23.49M in 2023, Susan Li Chief Financial Officer at Meta Platforms, received a total compensation of $23.46M in 2023.

This dataset is about companies and is filtered where the company is Meta, featuring 5 columns: CEO, CEO approval, CEO gender, city, and employees. The preview is ordered by revenues (descending).

Meta کارمندان تعداد کل - ارزش های فعلی، داده های تاریخی، پیش بینی، آمار، نمودار و تقویم اقتصادی - Mar 2025.Data for Meta | کارمندان | تعداد کل including historical, tables and charts were last updated by Trading Economics this last March in 2025.

Meta Financial is a public company headquartered in South Dakota with an estimated 1,134 employees. In the US, the company has a notable market share in at least one industry: Real Estate Loans & Collateralized Debt, where they account for an estimated 0.2% of total industry revenue.

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About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Meta. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Meta, the median income for all workers aged 15 years and older, regardless of work hours, was $40,000 for males and $25,893 for females.

These income figures highlight a substantial gender-based income gap in Meta. Women, regardless of work hours, earn 65 cents for each dollar earned by men. This significant gender pay gap, approximately 35%, underscores concerning gender-based income inequality in the city of Meta.

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.08 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta median household income by race. You can refer the same here

Meta Empleados Numero total - Los valores actuales, los datos históricos, las previsiones, estadísticas, gráficas y calendario económico - Mar 2025.Data for Meta | Empleados | Numero total including historical, tables and charts were last updated by Trading Economics this last March in 2025.

PLEASE, CITE AS Kalabikhina IE, Kuznetsova PO, Zhuravleva SA (2024) Size and factors of the motherhood penalty in the labour market: A meta-analysis. Population and Economics 8(2): 178-205. https://doi.org/10.3897/popecon.8.e121438

Explanatory note 1: List of papers used in the meta-analysis - see the file "Meta_regression_analysis_papers".

The data is presented in WORD format.

Explanatory note 2: Set of data used in the meta-analysis - see the file "Meta_regression_analysis_table".

The data is presented in EXCEL format.

Description of table headers:

estimate_number - Number of the estimate

paper_number - Number of the paper

paper_name - Paper (year and first author)

paper_excluded - Paper was excluded from the final sample

survey - Data source

table_in_paper - Number of the table with the regression results in the paper

coeff - Regression coefficient for parenthood variable (estimate)

se - SE of the estimate

t - t-value of the estimate

ols - Estimate is obtained using the OLS method

fixed_effects - Estimate is obtained using the fixed effects method

panel - Model considers panel data (for several years)

quintile - Estimate is obtained using the quintile regression method

other - Estimate is obtained using other methods

selection_into_motherhood - Estimate is obtained allowing for selection into motherhood

hackman - Estimate is obtained allowing for selection into employment (Heckman procedure)

annual_earnings - Annual earnings are considered in the model

monthly_wage - Monthly wage is considered in the model

daily_wage - Daily wage is considered in the model

hourly_wage - Hourly wage is considered in the model

min_age_kid - Child's age (minimum)

max_age_kid - Child's age (maximum)

motherhood - Model uses a dummy variable of the presence of children

num_kids - Model uses a variable of the number of children

kid1 - Model uses a variable of the presence of one child

kid2p - Model uses a variable of the presence of two or more children

kid2 - Model uses a variable of the presence of two children

kid3p - Model uses a variable of the presence of three or more children

kid3 - Model uses a variable of the presence of three children

kid4p - Model uses a variable of the presence of three or more children

race/nationality - Model includes a race/ethnicity variable

age - Model includes the age variable

marstat - Model includes the marital status variable

oth_char_hh - Model includes any other variables of other household characteristics

settl_type - Model includes a variable of the type of settlement (urban, rural)

region - Model includes a variable of the region of the country

education - Model includes information on the level of education

experience - Model includes a variable of work experience

pot_experience - Model includes a variable of potential work experience, to be calculated from the data on age and number of years of education

tenure - Model includes a variable of the duration of employment at the current job

interruptions - Model includes a variable of employment interruptions (related to motherhood)

occupation - Model includes an occupation variable

industry - Model includes a variable of the industry of employment

union - Model includes a variable of trade union membership

friendly_conditions - Model includes a variable of the favourable working conditions for mothers (flexible schedule, possibility to work from home, etc.).

hours - Model includes a variable of the number of hours worked

sector - Model includes a variable of the type of employer ownership (public or private)

informal - Model includes a variable of informal employment

size_ent - Model includes a variable of the employer size

min_age_woman - Woman's age (minimum)

max_age_woman - Woman's age (maximum)

mean_age_woman - Woman's age (mean)

restricted - Sample is limited

private - Model considers only private sector employees

state - Model considers only public sector employees

full_time - Model considers only full-time workers

part_time - Model considers only part-time workers

better_educated - Model considers only women with a high level of education

lower_educated - Model considers only women with a low level of education

married - Model includes only married women

single - Model includes only single women

natives - Model includes only native women (born in the country)

immigrants - Model includes only immigrant women (born abroad)

race - Model includes only women of a particular race

min_year - Time period (minimum year)

max_year - Time period (maximum year)

journal - Type of publication

usa - Sample includes women from the USA

western_europe - Sample includes women from Western Europe (Belgium, France, Germany, Luxembourg, the Netherlands, Switzerland)

north_europe - Sample includes women from Northern Europe (Denmark, Finland, Norway, Sweden)

south_europe - Sample includes women from Southern Europe (Greece, Italy, Portugal, Spain)

east_centre_europe - Sample includes women from Central or Eastern Europe (Czechia, Hungary, Poland, Russia, Serbia, Ukraine)

china - Sample includes women from China

Russia - Sample includes women from Russia

others - Sample includes women from other countries

country - Country name

The tech industry had a rough start to 2024. Technology companies worldwide saw a significant reduction in their workforce in the first quarter of 2024, with over 57 thousand employees being laid off. By the second quarter, layoffs impacted more than 43 thousand tech employees. In the final quarter of the year around 12 thousand employees were laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of 167.6 thousand employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of 263 thousand laid off employees in the global tech sector by trhe end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.

Meta Pracownicy Łączna - Aktualne wartości, dane historyczne, prognozy, statystyki, wykresy i kalendarz ekonomiczny - Mar 2025.Data for Meta | Pracownicy | Łączna including historical, tables and charts were last updated by Trading Economics this last March in 2025.

List of employees and associated meta data that will be used to generate agency, department, office, and section directories.

Burnout among healthcare personnel has been exacerbated by the COVID-19 pandemic's unique features. During the COVID-19 pandemic, this systematic review and meta-analysis aims to provide a complete assessment of the prevalence of burnout across various healthcare personnel. Until January 2021, systematic searches for English language papers were conducted using PubMed, Scopus, EMBASE, Web of Science, Cochrane Library, and ProQuest. Thirty observational studies were found after conducting systematic searches. The pooled overall prevalence of burnout was 52% [95% confidence interval (CI) 40–63%]. Pooled emotional exhaustion (EE), depersonalization (DP), and lack of personal accomplishment (PA) were 51% (95% CI 42–61%), 52% (95% CI 39–65%), and 28% (95% CI 25–31%), respectively. This study demonstrated that nearly half of the healthcare workers experienced burnout during the COVID-19 pandemic. In the studies that were included, non-frontline COVID-19 exposed healthcare personnel also experienced burnout. From high to lower middle-income countries, there was a gradient in the prevalence of total burnout, EE, and lack of PA. Further studies on burnout in low and lower-middle-income countries are suggested. A uniform diagnostic tool for the assessment of burnout is warranted.