Meta Platforms had ****** full-time employees as of December 2024, down from ****** people in 2023. As of December 2023, more than ******* employees at tech companies worldwide were laid off throughout the year across more than 1,000 companies. Facebook: how it all began In 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 ** billion U.S. dollars in revenue and a net income of ***** billion US dollars. It is also the most popular social network in the world, with *** billion monthly active users as of December 2020. Facebook employee diversity criticism Like 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 ** percent of Facebook employees in the U.S. are White and only *** percent are African-American, which has sparked concern regarding representation and equal opportunities. Around **** percent of senior level positions are occupied by White employees and only *** percent by Hispanic-Americans.

Meta’s workforce has shifted dramatically. In key industries like tech and advertising, employee numbers affect agility, innovation, and cost-efficiency. For instance, advertising platforms must balance headcount with dynamic campaign demands. Likewise, AI teams rely on small, highly skilled clusters to drive rapid breakthroughs. See how Meta’s staffing shapes its performance,...

In fiscal year 2025, the total number of employees at Meta Platforms was 74,067. The employee count increasedby 6,750 from 67,317 (in 2024) to 74,067 (in 2025). It represents a 10.03% year-over-year growth in employee count.

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 December in 2025.

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 **** 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 ****** full-time employees. Additionally, Google's parent company Alphabet had ******* full-time workers in 2024.

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.

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.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Meta. The dataset can be utilized to gain insights into gender-based income distribution within the Meta population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Meta, among individuals aged 15 years and older with income, there were 56 men and 49 women in the workforce. Among them, 28 men were engaged in full-time, year-round employment, while 19 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 17.86% fell within the income range of under $24,999, while 5.26% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: none of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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 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 $25.56 M in 2023, Mark Zuckerberg Chief Executive Officer at Meta Platforms, received a total compensation of $24.40 M in 2023, Christopher K. Cox Chief Product Officer at Meta Platforms, received a total compensation of $23.51 M in 2023, Andrew Bosworth Chief Technology Officer at Meta Platforms, received a total compensation of $23.49 M in 2023, Susan Li Chief Financial Officer at Meta Platforms, received a total compensation of $23.46 M in 2023.

Meta Des Employés Nombre total - Les valeurs actuelles, des données historiques, des prévisions, des statistiques, des tableaux et le calendrier économique - Nov 2025.Data for Meta | Des Employés | Nombre total including historical, tables and charts were last updated by Trading Economics this last November in 2025.

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

Context

Technology companies have become a dominant driver in recent years of economic growth, consumer tastes and the financial markets. The biggest tech stocks as a group, for example, have dramatically outpaced the broader market in the past decade.

That's because technology has reshaped in a major way how people communicate, consume information, shop, socialize, and work.

Broadly speaking, companies in the technology sector engage in the research, development, and manufacture of technologically based goods and services. They create software, and design and manufacture computers, mobile devices, and home appliances. They also provide products and services related to information technology.

Content

This dataset contains 3 files with the daily stock price and volume of the three companies: Google, Apple, and Facebook from 07/09/2017 to 07/09/2022. Source: Yahoo! Finance

Profile

Apple

Apple Inc. (AAPL) One Apple Park Way Cupertino, CA 95014 United States 408 996 1010 https://www.apple.com

Sector(s): Technology Industry: Consumer Electronics Full Time Employees: 154,000

Total Revenue (2021): $365,817,000
Net Income (2021):$94,680,000
Exchange: Nasdaq

Google

Alphabet Inc. (GOOG) 1600 Amphitheatre Parkway Mountain View, CA 94043 United States 650 253 0000 https://www.abc.xyz

Sector(s): Communication Services Industry: Internet Content & Information Full Time Employees: 174,014

Total Revenue (2021): $257,637,000 Net Income (2021):$76,033,000 Exchange: Nasdaq

Facebook

Meta Platforms, Inc. (META) 1601 Willow Road Menlo Park, CA 94025 United States 650 543 4800 https://investor.fb.com

Sector(s): Communication Services Industry: Internet Content & Information Full Time Employees: 83,553

Total Revenue (2021): $117,929,000 Net Income (2021):$39,370,000 Exchange: Nasdaq

Acknowledgements

Yahoo! Finance Investopedia Nasdaq

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Meta Funcionários Número total - Valores atuais, dados históricos, previsões, estatísticas, gráficos e calendário econômico - Dec 2025.Data for Meta | Funcionários | Número total including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Competitive employment counts and percentages.

Scholars have conducted numerous studies on how distal antecedents influence motivational states, subsequently affecting employee proactive work behavior. However, there is still debate about the strength of the effects different motivational states have on employee proactive behavior. This paper employs meta-analysis to explore the motivational mechanism proposed in Parker's (2010) model of proactive work behavior. It analyzes the relative strength of the effects of three motivational states—"can do," "reason to," and "energized to"—on employee proactive work behavior. Through literature screening, this study conducted a meta-analysis of 94 Chinese and English studies (with a total sample size of 30,724) that adopted Parker's theoretical model. It compared the correlations between distal antecedents, different motivational perspectives, and proactive work behavior. The results show that all three motivational states positively influence proactive work behavior. "Reason to" has a stronger effect on proactive work behavior compared to the other motivational states, and "can do" is stronger than "energized to," highlighting the importance of "reason to" in promoting employee proactive work behavior. Further discussion on this topic is provided.

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

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

IntroductionHealthcare workers risk of exposure to the influenza virus in their work, is a high-risk group for flu infections. Thus WHO recommends prioritizing flu vaccination for them–an approach adopted by >40 countries and/or regions worldwide.MethodsCross-sectional studies on influenza vaccination rates among healthcare workers were collected from PubMed, EMBASE, CNKI, and CBM databases from inception to February 26, 2023. Influenza vaccination rates and relevant data for multiple logistic regression analysis, such as odds ratios (OR) and 95% confidence intervals (CI), were extracted.ResultsA total of 92 studies comprising 125 vaccination data points from 26 countries were included in the analysis. The meta-analysis revealed that the overall vaccination rate among healthcare workers was 41.7%. Further analysis indicated that the vaccination rate was 46.9% or 35.6% in low income or high income countries. Vaccination rates in the Americas, the Middle East, Oceania, Europe, Asia, and Africa were 67.1, 51.3, 48.7, 42.5, 28.5, and 6.5%, respectively. Influencing factors were age, length of service, education, department, occupation, awareness of the risk of influenza, and/or vaccines.ConclusionThe global influenza vaccination rate among healthcare workers is low, and comprehensive measures are needed to promote influenza vaccination among this population.Systematic review registrationwww.inplysy.com, identifier: 202350051.

Post-campaign quantitative telephone survey using the ACET research tool to evaluate the campaign. The research called for a nationally representative telephone survey of the general population to be completed according to Advertising Campaign Evaluation Tool (ACET) guidelines. A questionnaire of approximately 10 minutes in duration was administered based on the ACET tool with some revisions to address specific issues related to the campaign and/or the target audience. A total of 1000 Canadians, aged 18 or older, were surveyed from March 4th to March 10th, 2010, with a response rate of 12%.

BackgroundThe effectiveness of pre-exposure prophylaxis has been extensively documented. However, there are substantial gaps between the actual implementation of pre-exposure prophylaxis and the ideal goal, especially in low-and middle-income countries. Healthcare workers play critical roles in the pre-exposure prophylaxis implementation, and they have more multi-level experiences about the barriers of pre-exposure prophylaxis implementation and how to facilitate it. However, the evidence aiming to synthesize their experiences is limited.ObjectiveThis study aims to aggregate the healthcare workers’ experiences of providing pre-exposure prophylaxis in low-and middle-income countries, and find the barriers, facilitators, and recommendations of pre-exposure prophylaxis implementation.MethodsThe ENTREQ (Enhancing transparency in reporting the synthesis of qualitative research) statement was used to guide the design and reporting of this qualitative meta-synthesis. A comprehensive search was conducted from inception of databases to 16th March 2023 in four databases: PubMed, CINAHL Plus with Full Text, Embase, Web of Science. The quality appraisal was conducted using the Joanna Briggs Institute Critical Appraisal Checklist. JBI’s meta-aggregation approach was used to guide the data extraction and synthesis, and the JBI ConQual approach was used to evaluate the evidence level of the synthesized findings.ResultsFourteen articles with good methodological quality were included in this review. A total of 122 findings were extracted and 117 findings with credibility ratings of “unequivocal” or “equivocal” were included in this meta-synthesis. The eligible findings were aggregated into 13 new categories and subsequently developed into 3 synthesized findings: the barriers, facilitators, and recommendations of pre-exposure prophylaxis implementation in low-and middle-income countries. The overall ConQual score of all three synthesized findings was rated as “low.”ConclusionThis review aggregated the experience of health care workers implementing pre-exposure prophylaxis in low-and middle-income countries and we could focus on the following key points to promote the uptake of pre-exposure prophylaxis: improve knowledge about pre-exposure prophylaxis, create a supportive environment, address medication-related barriers, increase the human resources and financial investments, and diversify the providing models.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/. The protocol of this review has been registered in the International Prospective Register of Systematic Reviews (PROSPERO, CRD42023411604).