The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Interest Expense on Debt Outstanding dataset provides monthly and fiscal year-to-date values for interest expenses on federal government debt, that is, the cost to the U.S. for borrowing money (calculated at a specified rate and period of time). U.S. debt includes Treasury notes and bonds, foreign and domestic series certificates of indebtedness, savings bonds, Government Account Series (GAS), State and Local Government Series (SLGS) and other special purpose securities. While interest expenses are what the government pays to investors who loan money to the government, how much the government pays in interest depends on both the total federal debt and the interest rate investors charged when they loaned the money. This dataset is useful for those who wish to track the cost of maintaining federal debt.

The United States recorded a Government Debt to GDP of 124.30 percent of the country's Gross Domestic Product in 2024. This dataset provides - United States Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset shows the average interest rates for U.S. Treasury securities for the most recent month compared with the same month of the previous year. The data is broken down by the various marketable and non-marketable securities. The summary page for the data provides links for monthly reports from 2001 through the current year. Average Interest Rates are calculated on the total unmatured interest-bearing debt. The average interest rates for total marketable, total non-marketable and total interest-bearing debt do not include the U.S. Treasury Inflation-Protected Securities.

This dataset provides the daily historical yields of U.S. Treasury bonds across various maturities, ranging from 1 month to 30 years. These yields serve as a key reference point for interest rates worldwide and provide insights into the cost of borrowing for the U.S. government.

Start dates for each bond series: - US1M: Data begins from July 31, 2001. - US3M: Data begins from September 1, 1981. - US6M: Data begins from September 1, 1981. - US1Y: Data begins from January 2, 1962. - US2Y: Data begins from June 1, 1976. - US3Y: Data begins from January 2, 1962. - US5Y: Data begins from January 2, 1962. - US7Y: Data begins from July 1, 1969. - US10Y: Data begins from January 2, 1962. - US20Y: Data begins from January 2, 1962. - US30Y: Data begins from February 15, 1977.

The Average Interest Rates on U.S. Treasury Securities dataset provides average interest rates on U.S. Treasury securities on a monthly basis. Its primary purpose is to show the average interest rate on a variety of marketable and non-marketable Treasury securities. Marketable securities consist of Treasury Bills, Notes, Bonds, Treasury Inflation-Protected Securities (TIPS), Floating Rate Notes (FRNs), and Federal Financing Bank (FFB) securities. Non-marketable securities consist of Domestic Series, Foreign Series, State and Local Government Series (SLGS), U.S. Savings Securities, and Government Account Series (GAS) securities. Marketable securities are negotiable and transferable and may be sold on the secondary market. Non-marketable securities are not negotiable or transferrable and are not sold on the secondary market. This is a useful dataset for investors and bond holders to compare how interest rates on Treasury securities have changed over time.

****Dataset Overview**** This dataset contains historical macroeconomic data, featuring key economic indicators in the United States. It includes important metrics such as the Consumer Price Index (CPI), Retail Sales, Unemployment Rate, Industrial Production, Money Supply (M2), and more. The dataset spans from 1993 to the present and includes monthly data on various economic indicators, processed to show their rate of change (either percentage or absolute difference, depending on the indicator).

provenance

The data in this dataset is sourced from the Federal Reserve Economic Data (FRED) database, hosted by the Federal Reserve Bank of St. Louis. FRED provides access to a wide range of economic data, including key macroeconomic indicators for the United States. My work involved calculating the rate of change (ROC) for each indicator and reorganizing the data into a more usable format for analysis. For more information and access to the full database, visit FRED's website.

Purpose and Use for the Kaggle Community:

This dataset is a valuable resource for data scientists, economists, and analysts interested in understanding macroeconomic trends, performing time series analysis, or building predictive models. With the rate of change included, users can quickly assess the growth or contraction in these indicators month-over-month. This dataset can be used for:

• Exploratory Data Analysis (EDA): Understanding historical economic trends. -Time Series Forecasting: Building models to predict future economic conditions. -Macroeconomic Analysis: Analyzing the relationship between various economic indicators. -Machine Learning Projects: Using the data as features to predict financial or economic outcomes. -By utilizing this dataset, users can perform in-depth analysis on the impact of macroeconomic changes, compare the historical performance of various indicators, and experiment with different time series forecasting techniques.

****Column Descriptions****

Year: The year of the observation.

Month: The month of the observation (1-12).

Industrial Production: Monthly data on the total output of US factories, mines, and utilities.

Manufacturers' New Orders: Durable Goods: Measures the value of new orders placed with manufacturers for durable goods, indicating future production activity.

Consumer Price Index (CPIAUCSL): A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.

Unemployment Rate: The percentage of the total labor force that is unemployed but actively seeking employment.

Retail Sales: The total receipts of retail stores, indicating consumer spending and economic activity.

Producer Price Index: Measures the average change over time in the selling prices received by domestic producers for their output.

Personal Consumption Expenditures (PCE): A measure of the prices paid by consumers for goods and services, used in calculating inflation.

National Home Price Index: A measure of changes in residential real estate prices across the country.

All Employees, Total Nonfarm: The number of nonfarm payroll employees, an important indicator of the labor market.

Labor Force Participation Rate: The percentage of the working-age population that is either employed or actively looking for work.

Federal Funds Effective Rate: The interest rate at which depository institutions lend reserve balances to other depository institutions overnight.

Building Permits: The number of building permits issued for residential and non-residential buildings, a leading indicator of construction activity.

Money Supply (M2): The total money supply, including cash, checking deposits, and easily convertible near money.

Personal Income: The total income received by individuals from all sources, including wages, investments, and government transfers.

Trade Balance: The difference between a country's imports and exports, indicating the net trade flow.

Consumer Sentiment: The index reflecting consumer sentiment and expectations for the future economic outlook.

Consumer Confidence: A measure of how optimistic or pessimistic consumers are regarding their expected financial situation and the economy.

Notes on Interest Rates Please note that for the Federal Funds Effective Rate (FEDFUNDS), the dataset includes the absolute change in basis points (bps), not the rate of change. This means that the dataset reflects the direct change in the interest rate rather than the percentage change month-over-month. The change is represented in basis points, where 1 basis point equals 0.01%.

Total outstanding debt of the U.S. government reported daily. Includes a breakout of intragovernmental holdings (federal debt held by U.S. government) and debt held by the public (federal debt held by entities outside the U.S. government).

Dataset Description:

The myusabank.csv dataset contains daily financial data for a fictional bank (MyUSA Bank) over a two-year period. It includes various key financial metrics such as interest income, interest expense, average earning assets, net income, total assets, shareholder equity, operating expenses, operating income, market share, and stock price. The data is structured to simulate realistic scenarios in the banking sector, including outliers, duplicates, and missing values for educational purposes.

Potential Student Tasks:

1. Data Cleaning and Preprocessing:

   • Handle missing values, duplicates, and outliers to ensure data integrity.
   • Normalize or scale data as needed for analysis.

2. Exploratory Data Analysis (EDA):

   • Visualize trends and distributions of financial metrics over time.
   • Identify correlations between different financial indicators.

3. Calculating Key Performance Indicators (KPIs):

   • Compute metrics such as Net Interest Margin (NIM), Return on Assets (ROA), Return on Equity (ROE), and Cost-to-Income Ratio using calculated fields.
   • Analyze the financial health and performance of MyUSA Bank based on these KPIs.

4. Building Tableau Dashboards:

   • Design interactive dashboards to present insights and trends.
   • Include summary cards, bar charts, line charts, and pie charts to visualize financial performance metrics.

5. Forecasting and Predictive Modeling:

   • Use historical data to forecast future financial performance.
   • Apply regression or time series analysis to predict market share or stock price movements.

6. Business Insights and Reporting:

   • Interpret findings to derive actionable insights for bank management.
   • Prepare reports or presentations summarizing key findings and recommendations.

Educational Goals:

The dataset aims to provide hands-on experience in data preprocessing, analysis, and visualization within the context of banking and finance. It encourages students to apply data science techniques to real-world financial data, enhancing their skills in data-driven decision-making and strategic analysis.

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

This table is a subsidiary table for Means of Financing the Deficit or Disposition of Surplus by the U.S. Government providing a detailed view of all direct and guaranteed loan financing for federal credit programs under the Credit Reform Act of 1990. Guaranteed loan financing is issuing any debt obligation with a guarantee, insurance, or other pledge that payment of all or a part of the principal or interest will be made to the lender. This table applies to lending to non-federal borrowers by non-federal lenders that carries some form of guarantee by the federal government. Exceptions include the insurance of deposits, shares, or other withdrawable accounts in financial institutions. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

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/.

Miss America Titleholders

Miss America over the years

About this dataset

Every year, young women from across the United States compete for the title of Miss America. The competition is open to women between the ages of 17 and 25, and includes a talent portion, an interview, and a swimsuit competition (which was removed in 2018). The winner is crowned by the previous year's titleholder and goes on to tour the nation for about 20,000 miles a month, promoting her particular platform of interest.

The Miss America dataset contains information on all Miss America titleholders from 1921 to 2022. It includes columns for the year of the pageant, the name of the crowned winner, her state or district represented, awards won, talent performed, and notes about her win

How to use the dataset

This dataset contains information on Miss America titleholders from 1921 to 2022. The data includes the name of the winner, her state or district, the city she represented, her talent, and the year she won

Research Ideas

• Miss America could be used to study changes in American culture over time. For example, the decline in the swimsuit competition could be seen as a sign of increasing body positivity in the US.
• The dataset could be used to study the effect of winning Miss America has on a woman's career. Does winning lead to more opportunities?
• The dataset could be used to study geographical patterns inMiss America winners. For example, are there any states that have produced more winners than others?

Acknowledgements

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - 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.

Columns

File: miss_america_titleholders.csv | Column name | Description | |:----------------------|:-----------------------------------------------------------------------| | year | The year the Miss America pageant was held. (Integer) | | crowned | The name of the Miss America titleholder. (String) | | winner | The name of the Miss America winner. (String) | | state_or_district | The state or district represented by the Miss America winner. (String) | | city | The city represented by the Miss America winner. (String) | | awards | The awards won by the Miss America winner. (String) | | talent | The talent performed by the Miss America winner. (String) | | notes | Notes about the Miss America winner. (String) |

File: eurovision_winners.csv | Column name | Description | |:--------------|:-------------------------------------------------------------------------| | Year | The year the pageant was held. (Integer) | | Date | The date the pageant was held. (Date) | | Host City | The city where the pageant was held. (String) | | Winner | The name of the pageant winner. (String) | | Song | The song performed by the pageant winner. (String) | | Performer | The name of the performer of the pageant winner's song. (String) | | Points | The number of points the pageant winner received. (Integer) | | Margin | The margin of points between the pageant winner and runner-up. (Integer) | | Runner-up | The name of the pageant runner-up. (String) |

PPB shall coordinate and publish an annual prompt payment performance report detailing each agency's performance pursuant to Charter Section 332. PPB shall additionally make cumulative prompt payment performance statistics available upon request. All reports shall be distributed to the CCPO, OMB and Comptroller and shall be posted on the City's website in a location that is accessible by the public simultaneously with their publication.

Industrial Energy End Use in the U.S

Facility-Level Combustion Energy Data

By US Open Data Portal, data.gov [source]

About this dataset

This dataset contains in-depth facility-level information on industrial combustion energy use in the United States. It provides an essential resource for understanding consumption patterns across different sectors and industries, as reported by large emitters (>25,000 metric tons CO2e per year) under the U.S. EPA's Greenhouse Gas Reporting Program (GHGRP). Our records have been calculated using EPA default emissions factors and contain data on fuel type, location (latitude, longitude), combustion unit type and energy end use classified by manufacturing NAICS code. Additionally, our dataset reveals valuable insight into the thermal spectrum of low-temperature energy use from a 2010 Energy Information Administration Manufacturing Energy Consumption Survey (MECS). This information is critical to assessing industrial trends of energy consumption in manufacturing sectors and can serve as an informative baseline for efficient or renewable alternative plans of operation at these facilities. With this dataset you're just a few clicks away from analyzing research questions related to consumption levels across industries, waste issues associated with unconstrained fossil fuel burning practices and their environmental impacts

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How to use the dataset

This dataset provides detailed information on industrial combustion energy end use in the United States. Knowing how certain industries use fuel can be valuable for those interested in reducing energy consumption and its associated environmental impacts.

• To make the most out of this dataset, users should first become familiar with what's included by looking at the columns and their respective definitions. After becoming familiar with the data, users should start to explore areas of interest such as Fuel Type, Report Year, Primary NAICS Code, Emissions Indicators etc. The more granular and specific details you can focus on will help build a stronger analysis from which to draw conclusions from your data set.

• Next steps could include filtering your data set down by region or end user type (such as direct related processes or indirect support activities). Segmenting your data set further can allow you to identify trends between fuel type used in different regions or compare emissions indicators between different processes within manufacturing industries etc. By taking a closer look through this lens you may be able to find valuable insights that can help inform better decision making when it comes to reducing energy consumption throughout industry in both public and private sectors alike.

• if exploring specific trends within industry is not something that’s of particular interest to you but rather understanding general patterns among large emitters across regions then it may be beneficial for your analysis to group like-data together and take averages over larger samples which better represent total production across an area or multiple states (timeline varies depending on needs). This approach could open up new possibilities for exploring correlations between economic productivity metrics compared against industrial energy use over periods of time which could lead towards more formal investigations about where efforts are being made towards improved resource efficiency standards among certain industries/areas of production compared against other more inefficient sectors/regionsetc — all from what's already present here!

By leveraging the information provided within this dataset users have access to many opportunities for finding all sorts of interesting yet practical insights which can have important impacts far beyond understanding just another singular statistic alone; so happy digging!

Research Ideas

• Analyzing the trends in combustion energy uses by region across different industries.
• Predicting the potential of transitioning to clean and renewable sources of energy considering the current end-uses and their magnitude based on this data.
• Creating an interactive web map application to visualize multiple industrial sites, including their energy sources and emissions data from this dataset combined with other sources (EPA’s GHGRP, MECS survey, etc)

Acknowledgements

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

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication](https://creativecommons...

These rates are the daily secondary market quotation on the most recently auctioned Treasury Bills for each maturity tranche (4-week, 13-week, 26-week, and 52-week) that Treasury currently issues new Bills. Market quotations are obtained at approximately 3:30 PM each business day by the Federal Reserve Bank of New York. The Bank Discount rate is the rate at which a Bill is quoted in the secondary market and is based on the par value, amount of the discount and a 360-day year. The Coupon Equivalent, also called the Bond Equivalent, or the Investment Yield, is the bill's yield based on the purchase price, discount, and a 365- or 366-day year. The Coupon Equivalent can be used to compare the yield on a discount bill to the yield on a nominal coupon bond that pays semiannual interest.

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad,

Point of Interest (POI) is defined as an entity (such as a business) at a ground location (point) which may be (of interest). We provide high-quality POI data that is fresh, consistent, customizable, easy to use and with high-density coverage for all countries of the world.

This is our process flow:

Our machine learning systems continuously crawl for new POI data
Our geoparsing and geocoding calculates their geo locations
Our categorization systems cleanup and standardize the datasets
Our data pipeline API publishes the datasets on our data store

A new POI comes into existence. It could be a bar, a stadium, a museum, a restaurant, a cinema, or store, etc.. In today's interconnected world its information will appear very quickly in social media, pictures, websites, press releases. Soon after that, our systems will pick it up.

POI Data is in constant flux. Every minute worldwide over 200 businesses will move, over 600 new businesses will open their doors and over 400 businesses will cease to exist. And over 94% of all businesses have a public online presence of some kind tracking such changes. When a business changes, their website and social media presence will change too. We'll then extract and merge the new information, thus creating the most accurate and up-to-date business information dataset across the globe.

We offer our customers perpetual data licenses for any dataset representing this ever changing information, downloaded at any given point in time. This makes our company's licensing model unique in the current Data as a Service - DaaS Industry. Our customers don't have to delete our data after the expiration of a certain "Term", regardless of whether the data was purchased as a one time snapshot, or via our data update pipeline.

Customers requiring regularly updated datasets may subscribe to our Annual subscription plans. Our data is continuously being refreshed, therefore subscription plans are recommended for those who need the most up to date data. The main differentiators between us vs the competition are our flexible licensing terms and our data freshness.

Data samples may be downloaded at https://store.poidata.xyz/us

This dataset provides annual personal income estimates for State of Iowa counties produced by the U.S. Bureau of Economic Analysis beginning in 1997. Data includes the following estimates: personal income and per capita personal income.

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 is the income received by, or on behalf of all persons residing in the Iowa county, 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 for the county.

More terms and definitions are available on https://apps.bea.gov/regional/definitions/. Less

The International Bank for Reconstruction and Development (IBRD) loans are public and publicly guaranteed debt extended by the World Bank Group. IBRD loans are made to, or guaranteed by, countries that are members of IBRD. IBRD may also make loans to IFC. IBRD lends at market rates. Data are in U.S. dollars calculated using historical rates. This dataset contains the latest available snapshot of the Statement of Loans. The World Bank complies with all sanctions applicable to World Bank transactions.