Corporate Profits in the United States decreased to 3203.60 USD Billion in the first quarter of 2025 from 3312 USD Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Corporate Profits in the United Kingdom increased to 152043 GBP Million in the first quarter of 2025 from 148024 GBP Million in the fourth quarter of 2024. This dataset provides the latest reported value for - United Kingdom Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Corporate Profits in Canada increased to 142196 CAD Million in the first quarter of 2025 from 137544 CAD Million in the fourth quarter of 2024. This dataset provides - Canada Corporate Profits - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Corporate Profits in Japan decreased to 28469.40 JPY Billion in the first quarter of 2025 from 28691.91 JPY Billion in the fourth quarter of 2024. This dataset provides - Japan Corporate Profits - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides annual personal income estimates for State of Iowa produced by the U.S. Bureau of Economic Analysis beginning in 1997. Data includes the following estimates: personal income, per capita personal income, wages and salaries, supplements to wages and salaries, private nonfarm earnings, compensation of employees, average compensation per job, and private nonfarm compensation. Personal income is defined as the sum of wages and salaries, supplements to wages and salaries, proprietors’ income, dividends, interest, and rent, and personal current transfer receipts, less contributions for government social insurance. Personal income for Iowa is the income received by, or on behalf of all persons residing in Iowa, regardless of the duration of residence, except for foreign nationals employed by their home governments in Iowa. Per capita personal income is personal income divided by the Census Bureau’s annual midyear (July 1) population estimates. Wages and salaries is defined as the remuneration receivable by employees (including corporate officers) from employers for the provision of labor services. It includes commissions, tips, and bonuses; employee gains from exercising stock options; and pay-in-kind. Judicial fees paid to jurors and witnesses are classified as wages and salaries. Wages and salaries are measured before deductions, such as social security contributions, union dues, and voluntary employee contributions to defined contribution pension plans. Supplements to wages and salaries consists of employer contributions for government social insurance and employer contributions for employee pension and insurance funds. Private nonfarm earnings is the sum of wages and salaries, supplements to wages and salaries, and nonfarm proprietors' income, excluding farm and government. Compensation to employees is the total remuneration, both monetary and in kind, payable by employers to employees in return for their work during the period. It consists of wages and salaries and of supplements to wages and salaries. Compensation is presented on an accrual basis - that is, it reflects compensation liabilities incurred by the employer in a given period regardless of when the compensation is actually received by the employee. Average compensation per job is compensation of employees divided by total full-time and part-time wage and salary employment. Private nonfarm compensation is the sum of wages and salaries and supplements to wages and salaries, excluding farm and government. More terms and definitions are available on https://apps.bea.gov/regional/definitions/.

This dataset provides values for CORPORATE PROFITS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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 Enterprise. 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 Enterprise, the median income for all workers aged 15 years and older, regardless of work hours, was $49,578 for males and $25,689 for females.

These income figures highlight a substantial gender-based income gap in Enterprise. Women, regardless of work hours, earn 52 cents for each dollar earned by men. This significant gender pay gap, approximately 48%, underscores concerning gender-based income inequality in the city of Enterprise.

- Full-time workers, aged 15 years and older: In Enterprise, among full-time, year-round workers aged 15 years and older, males earned a median income of $70,921, while females earned $45,080, leading to a 36% gender pay gap among full-time workers. This illustrates that women earn 64 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Enterprise, showcasing a consistent income pattern irrespective of employment status.

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 Enterprise median household income by race. You can refer the same here

The dataset included with this article contains three files describing and defining the sample and variables for VAT impact, and Excel file 1 consists of all raw and filtered data for the variables for the panel data sample. Excel file 2 depicts time-series and cross-sectional data for nonfinancial firms listed on the Saudi market for the second and third quarters of 2019 and the third and fourth quarters of 2020. Excel file 3 presents the raw material of variables used in measuring the company's profitability of the panel data sample

The OpenWeb Ninja Glassdoor Data API provides real-time access to extensive company data and employer reviews data from Glassdoor.

Key company data points included in the dataset: Name, Rating, Website, Salary and Job counts, Company size, Revenue, Stock, Competitors, Awards won, and 30+ more data points.

Key employer review data points included in the dataset: Review summary, Pros / Cons, Employee status, Location, Work-Life balance, CEO rating, and 20+ more data points.

OpenWeb Ninja's Glassdoor Data API Stats & Capabilities: - 2M+ Companies/Employers - 80M+ Employee Reviews - 30+ company data points - 20+ review data points - Company search capability

OpenWeb Ninja's Glassdoor Data API common use cases: - Investors and Market Analysts - Market and Industry Trends - Competitive Analysis - Company Insights

Lamini Earning Calls QA Dataset

  Description

This dataset contains transcripts of earning calls for various companies, along with questions and answers related to the companies' financial performance and other relevant topics.

  Format

The transcripts, questions, and answers are in the form of jsonlines files, with each json object in the file containing the transcript of an earning call for a single company.

  Data Pipeline Code

The entire data pipeline… See the full description on the dataset page: https://huggingface.co/datasets/lamini/earnings-calls-qa.

This table presents Gross Domestic Product (GDP) and its components according to the income approach. In the income approach, GDP is measured as the sum of compensation of employees (wages and salaries paid to employees and their employers’ social contributions), plus gross operating surplus (business profits) and gross mixed income (profits of the self-employed), plus taxes on production and imports less subsidies.

Data is presented for each country in national currency as well as in euros for the European Union and the euro area. The presentation is on a country-by-country basis. Users are recommended to select one country (or area) at a time in the ‘Reference area’ filter.

These indicators were presented in the previous dissemination system in the QNA dataset.
See User Guide on Quarterly National Accounts (QNA) in OECD Data Explorer: QNA User guide
See QNA Calendar for information on advance release dates: QNA Calendar
See QNA Changes for information on changes in methodology: QNA Changes
See QNA TIPS for a better use of QNA data: QNA TIPS
Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage
OECD statistics contact: STAT.Contact@oecd.org

Earnings Call Summary Dataset (Llama-4-Maverick-17B-128E-Instruct-FP8)

  Dataset Description

This dataset contains comprehensive summaries of corporate earnings call transcripts generated using the Llama-4-Maverick-17B-128E-Instruct-FP8 model. Each summary provides structured insights into company performance, strategic initiatives, market conditions, and forward-looking guidance.

  Dataset Features

High-quality summaries: Generated using… See the full description on the dataset page: https://huggingface.co/datasets/PursuitOfDataScience/earnings-call-llama4-maverick-summary.

Average weekly earnings (including overtime) for all employees by enterprise size and North American Industry Classification System (NAICS), last 5 quarters.

Dataset Information

This dataset includes quarterly earnings reports for various US stocks.

  Instruments Included

7000+ US Stocks

  Dataset Columns

symbol: The stock ticker or financial instrument identifier associated with the data. date: The end date of the fiscal period for which the financial data is reported. reported_date: The actual date on which the company reported its earnings or financial results. reported_eps: The earnings per share (EPS) that the… See the full description on the dataset page: https://huggingface.co/datasets/paperswithbacktest/Stocks-Quarterly-Earnings.

Context

Netflix, Inc. is an American media company engaged in paid streaming and the production of films and series.

Market cap

Market capitalization of Netflix (NFLX)

Market cap: $517.08 Billion USD

As of June 2025 Netflix has a market cap of $517.08 Billion USD. This makes Netflix the world's 19th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Revenue

Revenue for Netflix (NFLX)

Revenue in 2025: $40.17 Billion USD

According to Netflix's latest financial reports the company's current revenue (TTM ) is $40.17 Billion USD. In 2024 the company made a revenue of $39.00 Billion USD an increase over the revenue in the year 2023 that were of $33.72 Billion USD. The revenue is the total amount of income that a company generates by the sale of goods or services. Unlike with the earnings no expenses are subtracted.

Earnings

Earnings for Netflix (NFLX)

Earnings in 2025 (TTM): $11.31 Billion USD

According to Netflix's latest financial reports the company's current earnings are $40.17 Billion USD. In 2024 the company made an earning of $10.70 Billion USD, an increase over its 2023 earnings that were of $7.02 Billion USD. The earnings displayed on this page is the company's Pretax Income.

End of Day market cap according to different sources

On Jun 12th, 2025 the market cap of Netflix was reported to be:

$517.08 Billion USD by Yahoo Finance

$517.08 Billion USD by CompaniesMarketCap

$517.21 Billion USD by Nasdaq

Content

Geography: USA

Time period: May 2002- June 2025

Unit of analysis: Netflix Stock Data 2025

Variables

Variable	Description
date	date
open	The price at market open.
high	The highest price for that day.
low	The lowest price for that day.
close	The price at market close, adjusted for splits.
adj_close	The closing price after adjustments for all applicable splits and dividend distributions. Data is adjusted using appropriate split and dividend multipliers, adhering to Center for Research in Security Prices (CRSP) standards.
volume	The number of shares traded on that day.

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

Yahoo Finance Business Information dataset to access comprehensive details on companies, including financial data and business profiles. Popular use cases include market analysis, investment research, and competitive benchmarking.

Use our Yahoo Finance Business Information dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape.

Dataset Features

• name: Represents the company name.
• company_id: Unique identifier assigned to each company.
• entity_type: Denotes the type/category of the business entity.
• summary: A brief description or summary of the company.
• stock_ticker: The ticker symbol used for trading on stock exchanges.
• currency: The currency in which financial values are expressed.
• earnings_date: The date for the reported earnings.
• exchange: The stock exchange on which the company is listed.
• closing_price: The final stock price at the end of the trading day.
• previous_close: The stock price at the close of the previous trading day.
• open: The price at which the stock opened for the trading day.
• bid: The current highest price that a buyer is willing to pay for the stock.
• ask: The current lowest price that a seller is willing to accept.
• day_range: The range between the lowest and highest prices during the trading day.
• week_range: A broader price range over the past week.
• volume: Number of shares that traded in the session.
• avg_volume: Average daily share volume over a specific period.
• market_cap: Total market capitalization of the company.
• beta: A measure of the stock's volatility in comparison to the market.
• pe_ratio: Price-to-earnings ratio for valuation.
• eps: Earnings per share.
• dividend_yield: Dividend yield percentage.
• ex_dividend_date: The date on which the stock trades without the right to the declared dividend.
• target_est: The analyst's target price estimate.
• url: The URL to more detailed company information.
• people_also_watch: Companies frequently watched alongside this company.
• similar: Other companies with similar profiles.
• risk_score: A quantified risk score.
• risk_score_text: A textual interpretation of the risk score.
• risk_score_percentile: The risk score expressed in percentile terms.
• recommendation_rating: Analyst recommendation ratings.
• analyst_price_target: Analyst provided stock price target.
• company_profile_address: Company address from the profile.
• company_profile_website: URL for the company’s website.
• company_profile_phone: Contact phone number.
• company_profile_sector: The sector in which the company operates.
• company_profile_industry: Industry classification of the company.
• company_profile_employees: Number of employees in the company.
• company_profile_description: A detailed profile description of the company.
• valuation_measures: Contains key valuation ratios and metrics such as enterprise value, price-to-book, and price-to-sales ratios.
• Financial_highlights: Offers summary financial statistics including EPS, profit margin, revenue, and cash flow indicators.
• financials: This column appears to provide financial statement data.
• financials_quarterly: Similar to the previous field but intended to capture quarterly financial figures.
• earnings_estimate: Contains consensus earnings estimates including average, high, and low estimates along with the number of analysts involved.
• revenue_estimate: Provides revenue estimates with details such as average estimate, high and low values, and sales growth factors.
• earnings_history: This field tracks historical earnings and surprises by comparing actual EPS with estimates.
• eps_trend: Contains information on how the EPS has trended over various recent time intervals.
• eps_revisions: Captures recent changes in EPS forecasts.
• growth_estimates: Offers projections related to growth prospects over different time horizons.
• top_analysts: Intended to list the top analysts covering the company.
• upgrades_and_downgrades: This field shows recent analyst upgrades or downgrades.
• recent_news: Meant to contain recent news articles related to the company.
• fanacials_currency: Appears to indicate the currency used for financial reporting or valuation in the dataset.
• **company_profile_he

Predicting stock market prices following corporate earnings calls remains a significant challenge for investors and researchers alike, requiring innovative approaches that can process diverse information sources. This study investigates the impact of corporate earnings calls on stock prices by introducing a multi-modal predictive model. We leverage textual data from earnings call transcripts, along with images and tables from accompanying presentations, to forecast stock price movements on the trading day immediately following these calls. To facilitate this research, we developed the MiMIC (Multi-Modal Indian Earnings Calls) dataset, encompassing companies representing the Nifty 50, Nifty MidCap 50, and Nifty Small 50 indices. The dataset includes earnings call transcripts, presentations, fundamentals, technical indicators, and subsequent stock prices. We present a multimodal analytical framework that integrates quantitative variables with predictive signals derived from textual and visual modalities, thereby enabling a holistic approach to feature representation and analysis.

Contains quarterly estimates of profits, income from the sale of goods and services, wages and salaries, and the book value of inventories. These data are classified by broad industry, and original, seasonally adjusted and trend estimates are included for Australia, in current price terms. Volume measures are published for sales and inventories. State/territory data will also be included for sales, and wages and salaries, in current price terms.

Abstract copyright UK Data Service and data collection copyright owner.

The Annual Survey of Hours and Earnings (ASHE) is one of the largest surveys of the earnings of individuals in the UK. Data on the wages, paid hours of work, and pensions arrangements of nearly one per cent of the working population are collected. Other variables relating to age, occupation and industrial classification are also available. The ASHE sample is drawn from National Insurance records for working individuals, and the survey forms are sent to their respective employers to complete.

While limited in terms of personal characteristics compared to surveys such as the Labour Force Survey, the ASHE is useful not only because of its larger sample size, but also the responses regarding wages and hours are considered to be more accurate, since the responses are provided by employers rather than from employees themselves. A further advantage of the ASHE is that data for the same individuals are collected year after year. It is therefore possible to construct a panel dataset of responses for each individual running back as far as 1997, and to track how occupations, earnings and working hours change for individuals over time. Furthermore, using the unique business identifiers, it is possible to combine ASHE data with data from other business surveys, such as the Annual Business Survey (UK Data Archive SN 7451).

The ASHE replaced the New Earnings Survey (NES, SN 6704) in 2004. NES was developed in the 1970s in response to the policy needs of the time. The survey had changed very little in its thirty-year history. ASHE datasets for the years 1997-2003 were derived using ASHE methodologies applied to NES data.

The ASHE improves on the NES in the following ways:

• the NES questionnaire allowed too much variation in employer responses, leading to wide variations in the data
• weightings have been introduced to take account of the population size (significant biases were a known problem in NES data)
• the significant numbers of employees who change jobs between the sample selection and survey reference dates are retained in the ASHE sample, whereas these were dropped from the NES

Linking to other business studies
These data contain Inter-Departmental Business Register (IDBR) reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Observations from Northern Ireland
The ASHE data held by the UK Data Archive include very few observations from Northern Ireland. Users requiring access to Northern Ireland data are advised to contact the Northern Ireland Statistics and Research Agency, who administer this aspect of the survey.

Local unit reference variable, luref
The local unit reference variable 'luref', is generated to indicate multiple occurrences of the same local unit for disclosure checking purposes. It is inconsistent across years and is not an IDBR reference number. It should not be used to link ASHE with other business datasets.

For Secure Lab projects applying for access to this study as well as to SN 6697 Business Structure Database and/or SN 7683 Business Structure Database Longitudinal, only postcode-free versions of the data will be made available.

Latest Edition Information
For the twenty-sixth edition (February 2025), the data file 'ashegb_2023r_2024p_pc' has been added, along with the accompanying data dictionary.

Main Topics:

The ASHE contains a small number of variables for each individual, relating to wages, hours of work, pension arrangements, and occupation and industrial classifications. There are also variables for age, gender and full/part-time status. Because the data are collected by the employer, there are also variables relating to the organisation employing the individual. These include employment size and legal status (e.g. public company). Various geography variables are included in the data files.

Corporate Profits in Germany decreased to 205.35 EUR Billion in the first quarter of 2025 from 206.37 EUR Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - Germany Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.