Meta Platforms net profit margin for the quarter ending March 31, 2025 was 39.11%. Meta Platforms average net profit margin for 2024 was 34.97%, a 56.47% increase from 2023. Meta Platforms average net profit margin for 2023 was 22.35%, a 13.77% increase from 2022. Meta Platforms average net profit margin for 2022 was 25.92%, a 27.09% decline from 2021. Net profit margin can be defined as net Income as a portion of total sales revenue.

In 2024, Meta's average revenue per user was 49.63 U.S. dollars, up from 44.60 USD in 2023. The social network's family of apps segment revenue (which mainly consists of advertising) in 2024 was over 162 billion U.S. dollars.

In 2024, Meta (formerly Facebook Inc) generated over 160 billion U.S. dollars in ad revenues. Advertising accounts for the vast majority of the social network's revenue. Facebook advertising revenue – additional information Facebook’s business model heavily relies on ads, as the majority of social network’s revenue comes from advertising. In 2020, about 97.9 percent of Facebook's global revenue was generated from advertising, whereas only around two percent was generated by payments and other fees revenue. Facebook ad revenue stood at close to 86 billion U.S. dollars in 2020, a new record for the company and a significant increase in comparison to the previous years. For instance, the social network generated almost seven billion U.S. dollars in ad revenue in 2013, about 10 billion less than the 2015 figure. Facebook's average revenue per user also significantly increased in the same time span, going from 6.81 U.S. dollars in 2013 to 32.03 U.S. dollars in 2020. The U.S. and Canada are important markets for Facebook, considering the average revenue per user (ARPU) in these two countries is far above the global average. Facebook’s ARPU in the U.S. and Canada was 41.41 U.S. dollars in the last quarter of 2019, while the global average was 8.52 U.S. dollars. In Europe, Facebook’s average revenue per user was 13.21 U.S. dollars during the same time period. In terms of segments, mobile is the most promising advertising form for the company. In 2018, Facebook’s mobile advertising revenue already accounted for 92 percent of the social network’s total advertising revenue. Facebook’s mobile advertising revenue grew from an estimate of 13 billion U.S. dollars in 2015 to 50.6 billion U.S. dollars in 2018.

Facebook’s efforts to monetize its users have vastly differing results across global regions. In the fourth quarter of 2023, Facebook's average revenue per user (ARPU) in the Asia Pacific region was 5.52 U.S. dollars. This result pales in comparison to the combined U.S. and Canada market, where Facebook’s APRU amounted to 68.44 U.S. dollars.

Facebook revenue Facebook accumulated an impressive 69.66 billion U.S. dollars in annual ad revenues in 2019. The social network generates the majority of its revenues via social media marketing and advertising. Almost all of Facebook's ad revenue is generated via mobile – a staggering 92 percent in 2018.

Facebook is the biggest social media platform worldwide and the platform’s annual revenue in 2019 amounted to 70.7 billion U.S. dollars. Despite Facebook’s impressive growth, the company still lags behind other online companies in terms of total revenue. The company stated in its 2018 10K filing that it was dependent on the retention and engagement of its users, which has become increasingly difficult in the North American market.

Facebook usage concerns in North America With various user data controversies such as the Cambridge Analytics scandal in early spring, Facebook had a tumultuous 2018. A significant portion of U.S. Facebook users have come to rethink their Facebook usage. An April 2018 survey of adults in the United States that almost a third of respondents planned on using Facebook much less in the future. It is estimated that the average daily time spent on Facebook will stagnate at around 38 to 37 minutes per day. In comparison, Facebook-owned photo sharing app Instagram is projected to increase daily user engagement to 29 daily minutes in 2021.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Meta, MO, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, only 1, and 2-person households were found in Meta. Across the different household sizes in Meta the mean income is $42,814, and the standard deviation is $10,151. The coefficient of variation (CV) is 23.71%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $35,637. It then further increased to $49,992 for 2-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/meta-mo-median-household-income-by-household-size.jpeg" alt="Meta, MO median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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. You can refer the same here

Facebook probably needs no introduction; nonetheless, here is a quick history of the company. The world’s biggest and most-famous social network was launched by Mark Zuckerberg while he was a...

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Meta. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Meta. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Meta, the median household income stands at $58,750 for householders within the 25 to 44 years age group, followed by $56,875 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $26,458.

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.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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 age. You can refer the same here

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Meta (NASDAQ:META)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

About this dataset

Context

The dataset illustrates the median household income in Meta, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Meta decreased by $17,037 (30.30%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 7 years and declined for 4 years.

https://i.neilsberg.com/ch/meta-mo-median-household-income-trend.jpeg" alt="Meta, MO median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous 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. You can refer the same here

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Meta Platforms (META) on the Metaverse Road to Redemption

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Meta, MO, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 13,429, while the mean income for the highest quintile (20% of households with the highest income) is 103,908. This indicates that the top earners earn 8 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 117,319, which is 112.91% higher compared to the highest quintile, and 873.62% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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. You can refer the same here

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Meta Stock: The Future of Social Media

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Since going mainstream over a decade ago, hundreds of millions of Americans have embraced social networking sites, including Meta, X, LinkedIn and dozens more. People use these networks to maintain relationships with friends, follow the news and share photos and videos. By leveraging user data for targeted advertisements, where most revenue is derived, sites have been able to capitalize on the popularity of their platforms. As a result, industry revenue has surged at a CAGR of 20.3% over the past five years, including a climb of 12.0% to total an estimated $104.9 billion in 2024 alone. The industry has benefited from the continual shift of advertising spending to the internet, the proliferation of internet-connected mobile devices and more powerful networks. The industry is highly concentrated, with the top three companies making up a significant portion of industry revenue in 2024. Because of its early entry into the sector, Meta (previously Facebook) alone holds most of the market in 2024. The company's high market share and tremendously strong profit have resulted in the average industry profit margin accounting for 30.1% of revenue in 2024. Despite the industry's high profit level, many smaller companies operate at a loss. Since most industry revenue is generated through advertisements, sites must have a large and active user base to successfully attract advertisers. Many websites offer free services to gain users, but it can take a significant amount of time to build up a large user base, and many companies fail to do so before running out of money. Moving forward, industry revenue growth will slow somewhat because of deaccelerated growth in the number of mobile internet connections and the percentage of services conducted online, both of which are critical drivers for social networking sites. Nonetheless, the industry will grow substantially, increasing at a CAGR of 10.7% to $230.6 billion in 2029. Despite less pronounced revenue growth, new sites will continue to enter the industry and exacerbate competition. To compete, social networking sites are poised to focus on serving niche markets and advertisers' interests.

About this dataset

Context

The dataset presents the median household income across different racial categories in Meta. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Meta population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 88.74% of the total residents in Meta. Notably, the median household income for White households is $44,926. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $44,926.

https://i.neilsberg.com/ch/meta-mo-median-household-income-by-race.jpeg" alt="Meta median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Meta.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

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

The price of Meta (former Facebook) shares traded on the Nasdaq stock exchange fluctuated significantly but increased overall during the period from May 2012 to January 2025. After peaking at ****** U.S. dollars per share in August 2021, the price of Meta shares started to fluctuate and exceeded its previous peak in 2025. The share price stood at ****** U.S. dollars as of the end of January 2025. Substantial fluctuations in the last few years Meta's stock prices have fluctuated particularly after the rebranding announcement in late 2021. Following the announcement and through 2022, Meta's revenue remained rather stagnant, and its net income decreased considerably. Moreover, the tech giant announced one of the industry's largest layoffs in late 2022. As a result, the share price hit a low of ***** U.S. dollars in October 2022, the lowest value observed since 2016. However, Meta's share price has been steadily recovering since then. Shift in strategy for the world’s first social network Meta has shifted its focus to the metaverse, virtual reality (VR), and augmented reality (AR), with the rebranding in late 2021. As a result, Reality Labs was established as a dedicated business and research unit to focus on developing metaverse and AR/VR technologies. However, as of early 2023, Meta still relies mainly on advertising and its Family of Apps to generate most of its revenue, despite having made significant investments in virtual reality. Reality Labs generated *** billion U.S. dollars in revenue in 2024 and has been consistently incurring operating losses since 2019.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Meta's Market Momentum: Where Next? (META)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

To promote food security and sustainability, ecologically intensive farming systems should reliably produce adequate yields of high-quality food, enhance the environment, be profitable, and promote social wellbeing. Yet, while many studies address the mean effects of ecologically intensive farming systems on sustainability metrics, few have considered variability. This represents a knowledge gap because producers depend on reliable provisioning of yields, profits, and environmental services to enhance the sustainability of their production systems over time. Further, stable crop yields are necessary to ensure reliable access to nutritious foods. Here we address this by conducting a global meta-analysis to assess the average magnitude and variability of seven sustainability metrics in organic compared to conventional systems. Specifically, we explored the effects of these systems on (i) biotic abundance, (ii) biotic richness, (iii) soil organic carbon, (iv) soil carbon stocks, (v) crop yield, (vi) total production costs, and (vii) profitability. Organic farms promoted biotic abundance, biotic richness, soil carbon, and profitability, but conventional farms produced higher yields. Compared to conventional farms, organic farms had lower variability in abundance and richness but greater yield variability. Organic farms thus provided a “win-win” (high means and low variability) for environmental sustainability, while conventional farms provided a “win-win” for production by promoting high crop yields with low variability. Despite lower yields, and greater yield variability, organic systems had similar costs to conventional systems and were more profitable due to organic premiums. Our results suggest certification guidelines for organic farms successfully promote reliable environmental benefits, but greater reliance on ecological processes may reduce predictability of crop production.

About this dataset

Context

The dataset illustrates the median household income in Meta, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2023, the median household income for Meta decreased by $16,977 (29%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 8 years and declined for 5 years.

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 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous 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. You can refer the same here

In 2024, Apple generated the highest revenue per employee amongst the leading tech companies (by market capitalization) with **** million U.S. dollars. Meta and NVIDIA were the only other companies with revenues per employee exceeding *** million U.S. dollars.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Meta's (MGX) Stock Expected to See Significant Growth Amid Gene Editing Advancements

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data