The net job and business growth indicator measures the annual change in both the number of firms and the number of employees between 1978 and 2022. The data is categorized by the size of the firm: those with 1-19 employees, those with between 20 and 499 employees, and those with more than 500 employees.

This data contributes to the big picture of economic conditions in Champaign County. More firms and larger employment numbers are generally positive economic indicators, but any strictly economic indicator should be considered in the context of other factors.

The number of firms and number of employees show very different trends.

Historically, there have been significantly more firms with 1-19 employees than firms in the larger two size categories. The number of firms with 1-19 employees has also been relatively consistent until 2021: there were 95 fewer such firms in 2021 than 1978, and the largest year-to-year change in that 43-year period of analysis was a -3.2% decrease between 1979 and 1980. However, there were 437 fewer such firms in 2022 than 1978. There was a decrease in these firms of 12.5% from 2021 to 2022, the only double-digit year-to-year change and the largest year-to-year change over 44 years.

The larger two size categories have shown an increasing trend over the period of analysis. There were 43 more firms with 20-499 employees in 2022 than 1978, a total increase of 9%. The number of firms with more than 500 employees almost doubled, increasing by 206 firms from 212 in 1978 to 418 in 2022, a total increase of 97.2%.

The trends of employment also vary based on firm size. Firms with 1-19 employees have consistently, and unsurprisingly, accounted for less of the total employment than the larger two categories. Employment in firms with 1-19 employees has also remained relatively consistent over the period of analysis. Employment in firms with more than 500 employees saw an overall trend of growth, interrupted by brief and intermittent decreases, between 1978 and 2022. Employment in the middle category (firms with between 20 and 499 employees) was also greater in 2022 than in 1978.

This data is from the U.S. Census Bureau’s Business Dynamics Statistics Data Tables. This data is at the geographic scale of the Champaign-Urbana Metropolitan Statistical Area (MSA), which is comprised of Champaign and Piatt Counties, or a larger area than the cities or Champaign County.

Source: U.S. Census Bureau; 2022 Business Dynamics Statistics Data Tables; "BDSFSIZE - Business Dynamics Statistics: Firm Size: 1978-2022"; retrieved 21 October 2024.

Key Table Information.Table Title.Business Dynamics Statistics: Initial Firm Size: 1978-2023.Table ID.BDSTIMESERIES.BDSIFSIZE.Survey/Program.Economic Surveys.Year.2023.Dataset.ECNSVY Business Dynamics Statistics.Source.U.S. Census Bureau, Economic Surveys.Release Date.2025-09-25.Release Schedule.The Business Dynamics Statistics (BDS) is updated once a year, typically in September—2 years after the reference year.The data in this file were released in September 2025.For more information about BDS releases, see BDS Updates..Dataset Universe.The dataset universe consists of all establishments and firms in the U.S. that are located in one of the 50 U.S. states or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2017 North American Industry Classification System (NAICS).The BDS data tables are compiled from the Longitudinal Business Database (LBD). The LBD is a longitudinal database of business establishments and firms with coverage starting in 1976. The LBD is constructed by linking annual snapshot files from the Census Bureau's Business Register (BR), and incorporating edits to BR data made by the County Business Patterns program. See: About This Program and BDS Methodology for complete information on the coverage, scope, and methodology of the Business Dynamics Statistics data series..Methodology.Data Items and Other Identifying Records.This file contains data classified by Initial Employment size of firmsNumber of firmsNumber of establishmentsNumber of employees(DHS) denominatorNumber of establishments born during the last 12 monthsRate of establishments born during the last 12 monthsNumber of establishments exited during the last 12 monthsRate of establishments exited during the last 12 monthsNumber of jobs created from expanding and opening establishments during the last 12 monthsNumber of jobs created from opening establishments during the last 12 monthsNumber of jobs created from expanding establishments during the last 12 monthsRate of jobs created from opening establishments during the last 12 monthsRate of jobs created from expanding and opening establishments during the last 12 monthsNumber of jobs lost from contracting and closing establishments during the last 12 monthsNumber of jobs lost from closing establishments during the last 12 monthsNumber of jobs lost from contracting establishments during the last 12 monthsRate of jobs lost from closing establishments during the last 12 monthsRate of jobs lost from contracting and closing establishments during the last 12 monthsNumber of net jobs created from expanding/contracting and opening/closing establishments during the last 12 monthsRate of net jobs created from expanding/contracting and opening/closing establishments during the last 12 monthsRate of reallocation during the last 12 monthsNumber of firms that exited during the last 12 monthsNumber of establishments associated with firm deaths during the last 12 monthsNumber of employees associated with firm deaths during the last 12 monthsDefinitions of data items can be found in the table by clicking on the column header and selecting “Column Notes” or by accessing the BDS Glossary..Unit(s) of Observation.The units for BDS are employer establishments and firms..Geography Coverage.The data are shown at the U.S. level, by Metro/non-Metro Area, State, Metropolitan/Micropolitan Statistical Area, and County. For more information, see About This Program..Industry Coverage.The data are shown at the 2- through 4- digit NAICS codes for the United States and 2-digit NAICS sector for other geographies. For more information, see About This Program..Sampling.Business Dynamics Statistics tabulations are based on a combination of administrative and survey-collected data, and therefore no sampling is done. For more information about methodology and data limitations, see BDS Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7508369, Disclosure Review Board (DRB) approval number: CBDRB-FY25-0331).In accordance with U.S. Code, Title 13, Section 9, no data are published that would disclose the operations of an individual employer. The BDS has adapted the disclosure avoidance method of the County Business Patterns (CBP) in using Hybrid Balanced Multiplicative Noise Infusion. CBP has been released with noise-infusion since 2007; see CBP Methodology.In addition to noise infusion, cells with fewer than three firms are not presented. For more symbolic representation, see Symbols in the Table Information section below. For more information about BDS methodology, see BDS Methodology..Technical Documentation/Methodology.For detailed information on the coverage and methodology of the Business Dynamics Statistics data series, see Technical Documentation..Weights.No weighting applied to Business Dynamics Statistics....

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Key Table Information.Table Title.Business Dynamics Statistics: Firm Size: 1978-2023.Table ID.BDSTIMESERIES.BDSFSIZE.Survey/Program.Economic Surveys.Year.2023.Dataset.ECNSVY Business Dynamics Statistics.Source.U.S. Census Bureau, Economic Surveys.Release Date.2025-09-25.Release Schedule.The Business Dynamics Statistics (BDS) is updated once a year, typically in September—2 years after the reference year.The data in this file were released in September 2025.For more information about BDS releases, see BDS Updates..Dataset Universe.The dataset universe consists of all establishments and firms in the U.S. that are located in one of the 50 U.S. states or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2017 North American Industry Classification System (NAICS).The BDS data tables are compiled from the Longitudinal Business Database (LBD). The LBD is a longitudinal database of business establishments and firms with coverage starting in 1976. The LBD is constructed by linking annual snapshot files from the Census Bureau's Business Register (BR), and incorporating edits to BR data made by the County Business Patterns program. See: About This Program and BDS Methodology for complete information on the coverage, scope, and methodology of the Business Dynamics Statistics data series..Methodology.Data Items and Other Identifying Records.This file contains data classified by Employment size of firmsNumber of firmsNumber of establishmentsNumber of employees(DHS) denominatorNumber of establishments born during the last 12 monthsRate of establishments born during the last 12 monthsNumber of establishments exited during the last 12 monthsRate of establishments exited during the last 12 monthsNumber of jobs created from expanding and opening establishments during the last 12 monthsNumber of jobs created from opening establishments during the last 12 monthsNumber of jobs created from expanding establishments during the last 12 monthsRate of jobs created from opening establishments during the last 12 monthsRate of jobs created from expanding and opening establishments during the last 12 monthsNumber of jobs lost from contracting and closing establishments during the last 12 monthsNumber of jobs lost from closing establishments during the last 12 monthsNumber of jobs lost from contracting establishments during the last 12 monthsRate of jobs lost from closing establishments during the last 12 monthsRate of jobs lost from contracting and closing establishments during the last 12 monthsNumber of net jobs created from expanding/contracting and opening/closing establishments during the last 12 monthsRate of net jobs created from expanding/contracting and opening/closing establishments during the last 12 monthsRate of reallocation during the last 12 monthsNumber of firms that exited during the last 12 monthsNumber of establishments associated with firm deaths during the last 12 monthsNumber of employees associated with firm deaths during the last 12 monthsDefinitions of data items can be found in the table by clicking on the column header and selecting “Column Notes” or by accessing the BDS Glossary..Unit(s) of Observation.The units for BDS are employer establishments and firms..Geography Coverage.The data are shown at the U.S. level, by Metro/non-Metro Area, State, Metropolitan/Micropolitan Statistical Area, and County. For more information, see About This Program..Industry Coverage.The data are shown at the 2- through 4- digit NAICS codes for the United States and 2-digit NAICS sector for other geographies. For more information, see About This Program..Sampling.Business Dynamics Statistics tabulations are based on a combination of administrative and survey-collected data, and therefore no sampling is done. For more information about methodology and data limitations, see BDS Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7508369, Disclosure Review Board (DRB) approval number: CBDRB-FY25-0331).In accordance with U.S. Code, Title 13, Section 9, no data are published that would disclose the operations of an individual employer. The BDS has adapted the disclosure avoidance method of the County Business Patterns (CBP) in using Hybrid Balanced Multiplicative Noise Infusion. CBP has been released with noise-infusion since 2007; see CBP Methodology.In addition to noise infusion, cells with fewer than three firms are not presented. For more symbolic representation, see Symbols in the Table Information section below. For more information about BDS methodology, see BDS Methodology..Technical Documentation/Methodology.For detailed information on the coverage and methodology of the Business Dynamics Statistics data series, see Technical Documentation..Weights.No weighting applied to Business Dynamics Statistics..Table Informatio...

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Corporate Profits in the United States increased to 3259.41 USD Billion in the second quarter of 2025 from 3252.44 USD Billion in the first quarter of 2025. 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.

The global big data and business analytics (BDA) market was valued at ***** billion U.S. dollars in 2018 and is forecast to grow to ***** billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around ** billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate **** ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around **** billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

Debt Collection Complaints (India)

Investigating the Consumer Experience

By Lewis Kirvan [source]

About this dataset

The Consumer Financial Protection Bureau (CFPB) Complaint Database is a treasure trove of over 675,845 complaints, demonstrating the issues that accompany consumer financial products and services. Dating back to July 2011, this data has been used to help connect consumers with the companies they need in order to rectify various debt and lending problems. By analysing this data, CFPB can pinpoint trends in the marketplace which will allow them to do a better job in enforcing federal consumer financial laws as well as provide insight for rule writing.

This comprehensive dataset contains all manner of information about each complaint - from date of submission and product it regards, through to how the company responded and whether or not the consumer disputed that response. It also contains additional details such as location (ZIP code and state) as well particular tags associated with each complaint. Above all else, though, it includes an intimate account of what happened from the perspective of the customer themselves - something which provides context and invaluable insight into their experience of dealing with these large companies.

With this wealth of data at our fingertips we have been able to uncover some truly eye-opening results about both our economy’s banking systems - giving us an understanding far deeper than ever before about how big businesses interact with their customers when it comes time for repayment or loans management.. Furthermore since these details regarding specific complaints are so detailed we can be sure that any conclusions reached are more accurate than ever before!

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How to use the dataset

Research Ideas

• Utilizing this dataset to analyze and visualize customer trends in financial services industries, such as identifying issues related to mortgages, credit cards and bank accounts; or tracking how complaints differ by geographical location or product type.
• Leveraging the data to create interactive complaint dashboards that provide insights on customer satisfaction levels with different companies. This could be expanded into a predictive model which extrapolates future customer satisfaction trends based on historical complaints data.
• Predictive analytics that utilize this data set to identify at risk customers before they submit a complaint, through classifying users based on demographics, products used and past interactions with the company's website/services

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: full_dataset.csv | Column name | Description | |:---------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Date received | The date the complaint was received by the CFPB. (Date) | | Product | The type of product the complaint is about. (Text) | | Sub-product | The specific sub-product the complaint is about. (Text) | | Issue | The issue the cons...

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• Verified and updated records using NCOA (National Change of Address), CASS certification, and monthly hygiene processes.
• Targeting flexibility with dozens of demographic and lifestyle selects: age, gender, marital status, income, net worth, dwelling type, presence of children, hobbies, and more.
• Multi-channel delivery options including postal, email, and telemarketing data, ready for cross-channel B2B campaigns.
• Compliance support with Do-Not-Call (DNC) scrubbing and suppression processes.

Key Features

• Verified Data Quality: All records undergo monthly updates, NCOA processing, and CASS certification to ensure accuracy and deliverability.
• Decision-Maker Focused: Connect directly with CEOs, executives, and business owners who control purchasing decisions for their companies.
• Multi-Channel Marketing Ready: Use the data across postal, email, and telemarketing campaigns, with flexible delivery options including CSV, API, or SFTP.
• Deep Demographic & Lifestyle Selects: Filter by age, income, gender, marital status, net worth, dwelling type, presence of children, and lifestyle preferences.
• Geographic Precision: Target nationally or narrow by state, city, ZIP code, or DMA to match regional campaign needs.

Schema Preview

Business_Name, First_Name, Last_Name, Address, City, State, ZIP, Email_Address, Phone_Number, Executive_Level, Age, Gender, Income, Marital_Status, DNC_Flag

Use Cases

The Business Owners & Executives at Home Database is ideal for B2B and B2C marketers who want to:

• Run direct mail campaigns promoting office supplies, technology, furniture, or professional services.
• Launch email marketing campaigns with personalized offers to CEOs and executives at home.
• Power telemarketing outreach with accurate decision-maker phone data.
• Support digital marketing and audience targeting by enriching programmatic and social campaigns with executive data.
• Expand lead generation and sales funnels with verified contacts who make purchasing decisions from home.

Industries That Benefit

This database supports a wide range of industries, including:

• Software & Technology: Promote SaaS tools, CRM platforms, and IT services for home-based executives.
• Office Supplies & Equipment: Reach business owners who frequently purchase office products and technology.
• Financial Services & Insurance: Market loans, credit, investment opportunities, and insurance plans.
• Consulting & Marketing Services: Target executives seeking strategic growth, branding, or advertising solutions.
• Telecom & Utilities: Offer high-speed internet, phone services, and communication packages for remote offices.
• Luxury & Lifestyle Brands: Engage high-income executives with offers tailored to their lifestyle.

Why AmeriList?

For more than 20 years, AmeriList has been a trusted leader in direct marketing data solutions. Our commitment to accuracy, compliance, and ROI ensures that every dataset we deliver helps brands achieve measurable results. With monthly updates, advanced hygiene processes, and a focus on regulatory compliance (TCPA, CAN-SPAM, DNC suppression), AmeriList empowers marketers to launch effective, responsible campaigns.

Maximize Your Marketing ROI

This dataset isn’t just a list, it’s a proven tool for lead generation, customer acquisition, and B2B growth. Reach verified home-based executives, target decision-makers, and unlock new marketing opportunities with AmeriList today.

Key Table Information.Table Title.Business Dynamics Statistics: Geography: 1978-2023.Table ID.BDSTIMESERIES.BDSGEO.Survey/Program.Economic Surveys.Year.2023.Dataset.ECNSVY Business Dynamics Statistics.Source.U.S. Census Bureau, Economic Surveys.Release Date.2025-09-25.Release Schedule.The Business Dynamics Statistics (BDS) is updated once a year, typically in September—2 years after the reference year.The data in this file were released in September 2025.For more information about BDS releases, see BDS Updates..Dataset Universe.The dataset universe consists of all establishments and firms in the U.S. that are located in one of the 50 U.S. states or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2017 North American Industry Classification System (NAICS).The BDS data tables are compiled from the Longitudinal Business Database (LBD). The LBD is a longitudinal database of business establishments and firms with coverage starting in 1976. The LBD is constructed by linking annual snapshot files from the Census Bureau's Business Register (BR), and incorporating edits to BR data made by the County Business Patterns program. See: About This Program and BDS Methodology for complete information on the coverage, scope, and methodology of the Business Dynamics Statistics data series..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsNumber of employees(DHS) denominatorNumber of establishments born during the last 12 monthsRate of establishments born during the last 12 monthsNumber of establishments exited during the last 12 monthsRate of establishments exited during the last 12 monthsNumber of jobs created from expanding and opening establishments during the last 12 monthsNumber of jobs created from opening establishments during the last 12 monthsNumber of jobs created from expanding establishments during the last 12 monthsRate of jobs created from opening establishments during the last 12 monthsRate of jobs created from expanding and opening establishments during the last 12 monthsNumber of jobs lost from contracting and closing establishments during the last 12 monthsNumber of jobs lost from closing establishments during the last 12 monthsNumber of jobs lost from contracting establishments during the last 12 monthsRate of jobs lost from closing establishments during the last 12 monthsRate of jobs lost from contracting and closing establishments during the last 12 monthsNumber of net jobs created from expanding/contracting and opening/closing establishments during the last 12 monthsRate of net jobs created from expanding/contracting and opening/closing establishments during the last 12 monthsRate of reallocation during the last 12 monthsNumber of firms that exited during the last 12 monthsNumber of establishments associated with firm deaths during the last 12 monthsNumber of employees associated with firm deaths during the last 12 monthsDefinitions of data items can be found in the table by clicking on the column header and selecting “Column Notes” or by accessing the BDS Glossary..Unit(s) of Observation.The units for BDS are employer establishments and firms..Geography Coverage.The data are shown at the U.S. level, by Metro/non-Metro Area, State, Metropolitan/Micropolitan Statistical Area, and County. For more information, see About This Program..Industry Coverage.The data are shown at the 2- through 4- digit NAICS codes for the United States and 2-digit NAICS sector for other geographies. For more information, see About This Program..Sampling.Business Dynamics Statistics tabulations are based on a combination of administrative and survey-collected data, and therefore no sampling is done. For more information about methodology and data limitations, see BDS Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7508369, Disclosure Review Board (DRB) approval number: CBDRB-FY25-0331).In accordance with U.S. Code, Title 13, Section 9, no data are published that would disclose the operations of an individual employer. The BDS has adapted the disclosure avoidance method of the County Business Patterns (CBP) in using Hybrid Balanced Multiplicative Noise Infusion. CBP has been released with noise-infusion since 2007; see CBP Methodology.In addition to noise infusion, cells with fewer than three firms are not presented. For more symbolic representation, see Symbols in the Table Information section below. For more information about BDS methodology, see BDS Methodology..Technical Documentation/Methodology.For detailed information on the coverage and methodology of the Business Dynamics Statistics data series, see Technical Documentation..Weights.No weighting applied to Business Dynamics Statistics..Table Information.FTP Download.https://www2.census.gov/programs-surveys/bds/data...

Analyzing Customer Spending Habits to Improve Sales Performance

A Cross-Country Study

By Vineet Bahl [source]

About this dataset

This Sales Data dataset offers a unique insight into the spending habits of customers from various countries across the globe. With detailed information on customer age, gender, product category, quantity, unit cost and price, as well as revenue generated through sales of products listed in this dataset, you can explore and discover patterns in consumer behavior. Analyze shifts in consumer trends with qualitative data like customer age and gender to know what drives customers’ decisions when shopping online or offline. Compare different markets to analyze pricing strategies for new product launches or promotional campaigns. Also with this dataset you can gain valuable insights about the changes in consumer demand for specific products over time – find out which Products had better margin or however see how different promotions impacted overall sales performance from different categories and sub-categories! Analyzing consumer behavior is key to success when it comes to commerce business models so this Sales Data offers powerful ways into understanding your customer base better!

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How to use the dataset

This dataset presents a great opportunity to actively analyze customer spending habits on products and services to improve sales performance. The data contains information about the date of purchase, year, month, customer age, gender, country, state and product category. Further analysis can reveal insights into different customer segments based on their demographic characteristics such as age and gender as well as location (country & state).

The dataset also includes 3 additional columns at the end: quantity purchased in each transaction, unit cost and unit price for each product or service purchased which can be used to determine if customers are purchasing items in bulk or buying more expensive items than usual. Likewise any discrepancies between the unit cost & price can help establish whether discounts were applied during those transactions which could potentially point towards loyalty or reward programs being put in place for returning customers. Lastly the final column shows total revenue generated from those purchases which we can use to identify any patterns whereby certain groups of customers show higher purchasing power than others based on their spends (unit cost & quantity combination) over various periods/months/years of sales interactions with them.

In summary this dataset allows us to explore numerous dimensions related to ascertaining superior sales performance by studying how its various attributes play out together when it comes down to driving profitability through improved customer acquisition strategies as well increasing purchase rates from existing ones minus any discounts available in-between!

Research Ideas

• Analyzing customer demographics by countries and states to better target future marketing campaigns.

• Tracking changes in customers’ spending habits over time for different product categories.

• Identifying which product categories have the highest average revenue per sale to help prioritize resources for those products or services

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: SalesForCourse_quizz_table.csv | Column name | Description | |:---------------------|:--------------------------------------------------| | Date | Date of the sale. (Date) | | Year | Year of the sale. (Integer) | | Month | Month of the sale. (Integer) | | Customer Age | Age of the c...

Financial Access and Usage

Global Comparative Ratios Across 189 Jurisdictions

By International Monetary Fund [source]

About this dataset

This dataset provides an unprecedented opportunity to explore global financial access and usage trends from 2004-2016 from 189 of the world's reporting jurisdictions—which cover over 99 percent of the total adult population. With 152 time series and 47 indicator ratios, this Financial Access Survey gives insight into ways that access to and usage of financial services differ by households vs small/medium enterprises, life vs non-life insurance, deposits & microfinance institutions as well as credit unions & financial cooperatives. Utilizing this data, we can gain a better understanding of how policies or shifts in the global economy may influence or relate to access or utilization of services in certain regions while having comparable cross-economy comparisons. The IMF Monetary and Financial Statistics Manual Compilation Guide is utilized for all methodologies used in accumulating these datasets, while all data is available “as-is” with no guarantee provided either express or implied. Are you looking for ways to implement insightful macroeconomic analyses? Download FAS 2004–2016 now!

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How to use the dataset

The Financial Access Survey provides global supply-side data on access to and usage of financial services by households and firms for 189 reporting jurisdictions, covering 99 percent of the world’s adult population. With a robust selection of time series in this dataset, users can make meaningful insights into trends over time or across countries concerning various indicators related to access and usage of financial services. To help users navigate this large dataset, the following guide explains how to use the data most effectively.

Understanding The Dataset Columns

The columns in the dataset provide information about each indicator such as country name, indicator name, code for that indicator, its attribute (i.e., rate/ratio), when data is available for that particular indicator. Once you have identified an interesting measure/indicator whether it be credit union density or life insurance penetration rate measure in a given country during a certain year period then you can look up those numbers from the rows provided in this dataset .

Understanding The Different Years Available & Comparing Numbers Over Time

It is useful for users to compare different indicators over time by looking at specific years within this dataset which will allow us to see if rates are increasing or decreasing worldwide patterns across these trends among different countries based on these various measures listed provided in this survey such as mortgage lending rate or ratio GDP per capita etc that have been collected . We can therefore make use of our knowledge off economic changes that have occurred over time within certain parts of world , no matter if they are longer term economic effects due increases certain capabilities within a geographical area or shorter term changes due taxation laws by governments etc driving some people either towards using or away from using certain kinds financial products .

#### Comparing Between Countries

This datasets allows us direct comparisons between different countries with regards how many people are currently making use particular types off finances services , we certainly be able analyse current international relationships between services providers as well customers where ever concerned about particular attributes mentioned above whether being deposit interest rates small business credits terms tenders so forth . Knowing more about related dynamics helps build better user experiences with providers who understand needs risks impacts generating larger customer bases globally which often beneficial both parties involved exchange relationship so not forget always keep cross border motif whenever eye process from afar !

Research Ideas

• Comparing the access to and usage of financial services in different countries to better inform research policy decisions.
• Analyzing trends in financial access and usage by jurisdiction over time, to identify areas needing improvement in order to promote financial inclusion and stability.
• Cross-referencing FAS data with macroeconomic indicators such as GDP information to measure the potential impact of changes in level of access on economic growth or other metrics specific to a country or region of interest

Acknowledgements

If you use this dataset in yo...

NFIB Business Optimism Index in the United States decreased to 98.20 points in October from 98.80 points in September of 2025. This dataset provides - United States Nfib Business Optimism Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Business Confidence in the United States decreased to 48.20 points in November from 48.70 points in October of 2025. This dataset provides the latest reported value for - United States ISM Purchasing Managers Index (PMI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.