SBOI: sa: Most Pressing Problem: Survey High: Inflation data was reported at 41.000 % in Feb 2025. This stayed constant from the previous number of 41.000 % for Jan 2025. SBOI: sa: Most Pressing Problem: Survey High: Inflation data is updated monthly, averaging 41.000 % from Jan 2014 (Median) to Feb 2025, with 130 observations. The data reached an all-time high of 41.000 % in Feb 2025 and a record low of 41.000 % in Feb 2025. SBOI: sa: Most Pressing Problem: Survey High: Inflation data remains active status in CEIC and is reported by National Federation of Independent Business. The data is categorized under Global Database’s United States – Table US.S032: NFIB Index of Small Business Optimism. [COVID-19-IMPACT]

United States SBOI: sa: Most Pressing Problem: A Year Ago: Inflation data was reported at 23.000 % in Feb 2025. This records an increase from the previous number of 20.000 % for Jan 2025. United States SBOI: sa: Most Pressing Problem: A Year Ago: Inflation data is updated monthly, averaging 3.000 % from Jan 2014 (Median) to Feb 2025, with 129 observations. The data reached an all-time high of 37.000 % in Jul 2023 and a record low of 1.000 % in Dec 2021. United States SBOI: sa: Most Pressing Problem: A Year Ago: Inflation data remains active status in CEIC and is reported by National Federation of Independent Business. The data is categorized under Global Database’s United States – Table US.S032: NFIB Index of Small Business Optimism. [COVID-19-IMPACT]

United States SBOI: sa: Most Pressing Problem: Survey Low: Inflation data was reported at 0.000 % in Jan 2025. This stayed constant from the previous number of 0.000 % for Dec 2024. United States SBOI: sa: Most Pressing Problem: Survey Low: Inflation data is updated monthly, averaging 0.000 % from Jan 2014 (Median) to Jan 2025, with 129 observations. The data reached an all-time high of 0.000 % in Jan 2025 and a record low of 0.000 % in Jan 2025. United States SBOI: sa: Most Pressing Problem: Survey Low: Inflation data remains active status in CEIC and is reported by National Federation of Independent Business. The data is categorized under Global Database’s United States – Table US.S032: NFIB Index of Small Business Optimism. [COVID-19-IMPACT]

Envestnet®| Yodlee®'s Electronic Payment Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

This dataset presents information about total income distribution. The data covers the financial year of 2017-2018, and is based on Statistical Area Level 2 (SA2) according to the 2016 edition of the Australian Statistical Geography Standard (ASGS).

Total Income is the sum of all reported income derived from Employee income, Own unincorporated business, Superannuation, Investments and Other income. Total income does not include the non-lodger population.

Government pensions, benefits or allowances are excluded from the Australian Bureau of Statistics (ABS) income data and do not appear in Other income or Total income. Pension recipients can fall below the income threshold that necessitates them lodging a tax return, or they may only receive tax free pensions or allowances. Hence they will be missing from the personal income tax data set. Recent estimates from the ABS Survey of Income and Housing (which records Government pensions and allowances) suggest that this component can account for between 9% to 11% of Total income.

All monetary values are presented as gross pre-tax dollars, as far as possible. This means they reflect income before deductions and loses, and before any taxation or levies (e.g. the Medicare levy or the temporary budget repair levy) are applied. The amounts shown are nominal, they have not been adjusted for inflation. The income presented in this release has been categorised into income types, these categories have been devised by the ABS to closely align to ABS definitions of income.

The statistics in this release are compiled from the Linked Employer Employee Dataset (LEED), a cross-sectional database based on administrative data from the Australian taxation system. The LEED includes more than 120 million tax records over seven consecutive years between 2011-12 and 2017-18.

Please note:

• All personal income tax statistics included in LEED were provided in de-identified form with no home address or date of birth. Addresses were coded to the ASGS and date of birth was converted to an age at 30 June of the reference year prior to data provision.

• To minimise the risk of identifying individuals in aggregate statistics, perturbation has been applied to the statistics in this release. Perturbation involves small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics, while maximising the range of information that can be released. These adjustments have a negligible impact on the underlying pattern of the statistics. Some cells have also been suppressed due to low counts.

• Totals may not align with the sum of their components due to missing or unpublished information in the underlying data and perturbation.

For further information please visit the Australian Bureau of Statistics.

AURIN has made the following changes to the original data:

• Spatially enabled the original data.

• Set 'np' (not published to protect the confidentiality of individuals or businesses) values to Null.

Corporate Profits in the United States decreased to 3128.50 USD Billion in the third quarter of 2024 from 3141.56 USD Billion in the second 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.

This dataset presents information about employee income. The data covers the financial years 2011-12 to 2017-18, and is based on Statistical Area Level 2 (SA2) according to the 2016 edition of the Australian Statistical Geography Standard (ASGS).

Employee income is the total (or gross) income received as a return to labour from an employer or from a person's own incorporated business (when they are employed by this business). The data used in deriving employee income comes from both Individual Tax Returns (ITR) and payment summaries (where an individual has not lodged an ITR).

All monetary values are presented as gross pre-tax dollars, as far as possible. This means they reflect income before deductions and loses, and before any taxation or levies (e.g. the Medicare levy or the temporary budget repair levy) are applied. The amounts shown are nominal, they have not been adjusted for inflation. The income presented in this release has been categorised into income types, these categories have been devised by the Australian Bureau of Statistics (ABS) to closely align to ABS definitions of income.

The statistics in this release are compiled from the Linked Employer Employee Dataset (LEED), a cross-sectional database based on administrative data from the Australian taxation system. The LEED includes more than 120 million tax records over seven consecutive years between 2011-12 and 2017-18.

Please note:

• All personal income tax statistics included in LEED were provided in de-identified form with no home address or date of birth. Addresses were coded to the ASGS and date of birth was converted to an age at 30 June of the reference year prior to data provision.

• To minimise the risk of identifying individuals in aggregate statistics, perturbation has been applied to the statistics in this release. Perturbation involves small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics, while maximising the range of information that can be released. These adjustments have a negligible impact on the underlying pattern of the statistics. Some cells have also been suppressed due to low counts.

• Totals may not align with the sum of their components due to missing or unpublished information in the underlying data and perturbation.

For further information please visit the Australian Bureau of Statistics.

AURIN has made the following changes to the original data:

• Spatially enabled the original data.

• Set 'np' (not published to protect the confidentiality of individuals or businesses) values to Null.