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For a quick summary of the case study, please click "US Economy Powerpoint" and download the Powerpoint.
This dataset was inspired by rising prices for essential goods, the abnormally high inflation rate in March of 7.9 percent of this year, and the 30 trillion-dollar debt that we have. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.
This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.
I labeled all of the datasets to be self-explanatory based off of the title of the datasets. The US Economy Notebook has most of the code that I used as well as the four of the six phases of data analysis. The last two phases are in the US Economy Powerpoint. The "US Historical Inflation Rates" dataset could have also been labeled "The Inflation Of The US Dollar Month By Month". Lastly, the Average Sales of Houses in Jan is just a filtered version of "Average Sales of Houses in the US" dataset.
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The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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CNBC Economy Articles Dataset is an invaluable collection of data extracted from CNBC’s economy section, offering deep insights into global and U.S. economic trends, market dynamics, financial policies, and industry developments.
This dataset encompasses a diverse array of economic articles on critical topics like GDP growth, inflation rates, employment statistics, central bank policies, and major global events influencing the market. Designed for researchers, analysts, and businesses, it serves as an essential resource for understanding economic patterns, conducting sentiment analysis, and developing financial forecasting models.
Each record in the dataset is meticulously structured and includes:
This rich combination of fields ensures seamless integration into data science projects, research papers, and market analyses.
Interested in additional structured news datasets for your research or analytics needs? Check out our news dataset collection to find datasets tailored for diverse analytical applications.
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The Gross Domestic Product (GDP) in the United States expanded 2.10 percent in the second quarter of 2025 over the same quarter of the previous year. This dataset provides the latest reported value for - United States GDP Annual Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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This dataset contains quarterly data on the US Gross Domestic Product (GDP) and Total Public Debt from 1947 through 2020. It provides a comprehensive view into the development of debt versus GDP over the years, offering insights into how our economy has grown and changed since The Great Depression. Explore this valuable information to answer questions such as: How do debt and GDP relate to one another? Has US government spending been outpacing wealth throughout history? From what sources does our national debt originate? This dataset can be utilized by economists, governments, researchers, investors, financial institutions, journalists — anyone looking to gain a better understanding of where our economy stands today compared to past decades
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This dataset, U.S. GDP vs Debt Over Time, contains quarterly data on the Gross Domestic Product (GDP) and Total Public Debt of the United States between 1947 to 2020. This can be useful for conducting research into how the total public debt relates to economic growth in the US.
The dataset includes 4 columns: Quarter , Gross Domestic Product ($mil), Total Public Debt ($mil). The Quarter column consists of strings that represent each quarter from 1947-2020 with a corresponding number (e.g., “Q1-1947”). The Gross Domestic Product ($mil) and Total Public Debt ($mil) columns consist of numbers that indicate the respective amounts in millions for each quarter during this same time period.
By analyzing this dataset you can explore various trends over different periods as it relates to public debt versus economic growth in America and make informed decisions about how certain policies may affect future outcomes. Additionally, you could also compare these two values with other variables such as unemployment rate or inflation rate to gain deeper insights into America’s economy over time
- Comparing the quarterly growth in GDP with public debt to show the correlation between economic growth and government spending.
- Creating a bar or line visualization that compares the US’s total public debt to comparable economic powers like China, Japan, and Europe over time.
- Examining how changes in government deficit have contributed towards an increase in public debt by analyzing which quarters saw significant leaps of growth from one year to the next
If you use this dataset in your research, please credit the original authors. Data Source
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.
File: US GDP vs Debt.csv | Column name | Description | |:----------------------------------|:-------------------------------------------------------------------------------------------| | Quarter | The quarter of the year in which the data was collected. (String) | | Gross Domestic Product ($mil) | The total value of all goods and services produced by the US in a given quarter. (Integer) | | Total Public Debt ($mil) | The total amount owed by the federal government. (Integer) |
If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Charlie Hutcheson.
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TwitterIn 2024, the U.S. GDP increased from the previous year to about 29.18 trillion U.S. dollars. Gross domestic product (GDP) refers to the market value of all goods and services produced within a country. In 2024, the United States has the largest economy in the world. What is GDP? Gross domestic product is one of the most important indicators used to analyze the health of an economy. GDP is defined by the BEA as the market value of goods and services produced by labor and property in the United States, regardless of nationality. It is the primary measure of U.S. production. The OECD defines GDP as an aggregate measure of production equal to the sum of the gross values added of all resident, institutional units engaged in production (plus any taxes, and minus any subsidies, on products not included in the value of their outputs). GDP and national debt Although the United States had the highest Gross Domestic Product (GDP) in the world in 2022, this does not tell us much about the quality of life in any given country. GDP per capita at purchasing power parity (PPP) is an economic measurement that is thought to be a better method for comparing living standards across countries because it accounts for domestic inflation and variations in the cost of living. While the United States might have the largest economy, the country that ranked highest in terms of GDP at PPP was Luxembourg, amounting to around 141,333 international dollars per capita. Singapore, Ireland, and Qatar also ranked highly on the GDP PPP list, and the United States ranked 9th in 2022.
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This dataset contains historical quarterly data for the U.S. Real Gross Domestic Product, from the first quarter of 1947 to the Q2 2023. Real GDP is an inflation-adjusted measure that reflects the value of all goods and services produced by an economy in a given year, expressed in base-year prices, and is often considered an indicator of a country's standard of living.
The dataset has two columns:
Date: The end of the respective quarter (in MM/DD/0YYYY format). Value: The Real GDP at the end of the respective quarter.
Inspiration: Real GDP is a comprehensive measure of U.S. economic activity and a key tool for economic decision-making and forecasting. Real GDP is used by economists, policy-makers, researchers, and investors to understand the growth and performance of the U.S. economy over time.
Usability: The Real GDP data can be used for a variety of purposes:
Economic Analysis: It can be used for macroeconomic analysis and forecasting. Policy Understanding: It can help understand the impact and effectiveness of economic policies implemented by the U.S. government. Investment Analysis: GDP growth impacts financial markets, and this data can help investors understand and forecast market trends. Education: It can be used in classrooms for teaching economics, finance, and related disciplines.
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Economic growth is easy to understand: it means that people have access to goods and services of increasing quantity and quality. What is hard, however, is to measure economic growth. This chart shows two ways of doing this for US growth over the past 160 years. The purple lines represent a straightforward approach: each line tracks the share of households with access to one specific good or service. Starting from the top, you see the rising provision of basic infrastructure like running water, flush toilets, and electric power. You can also see the increasing availability of communication technology: radios, TVs, the Internet, and mobile phones. And further down, you see the rise of technologies that reduced work at home: vacuum cleaners, washing machines, dryers, and dishwashers. This approach is very concrete; it shows practical ways in which the production and consumption of specific goods increased over time. The downside is that it only captures a limited number of particular goods. Millions of goods and services are produced and consumed, and most are not recorded with such precision. A way to measure how people’s access to the full range of goods and services changes is to measure people’s incomes. This way of measuring growth is shown in the top left panel. The data on average income, here measured by GDP per capita, tells us that the average American was 13 times poorer in 1860 than in 2022 (adjusted for inflation). These two ways of measuring economic growth have pros and cons: one is concrete but not comprehensive, the other is comprehensive but quite abstract. If we want to understand what growth means for our societies, I find it helpful to combine them both.
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TwitterThe U.S. Census Bureau.s economic indicator surveys provide monthly and quarterly data that are timely, reliable, and offer comprehensive measures of the U.S. economy. These surveys produce a variety of statistics covering construction, housing, international trade, retail trade, wholesale trade, services and manufacturing. The survey data provide measures of economic activity that allow analysis of economic performance and inform business investment and policy decisions. Other data included, which are not considered principal economic indicators, are the Quarterly Summary of State & Local Taxes, Quarterly Survey of Public Pensions, and the Manufactured Homes Survey. For information on the reliability and use of the data, including important notes on estimation and sampling variance, seasonal adjustment, measures of sampling variability, and other information pertinent to the economic indicators, visit the individual programs' webpages - http://www.census.gov/cgi-bin/briefroom/BriefRm.
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Unemployment Rate in the United States increased to 4.40 percent in September from 4.30 percent in August of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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TwitterThe Economic Census is the U.S. Government's official five-year measure of American business and the economy. It is conducted by the U.S. Census Bureau, and response is required by law. In October through December of the census year, forms are sent out to nearly 4 million businesses, including large, medium and small companies representing all U.S. locations and industries. Respondents were asked to provide a range of operational and performance data for their companies.This dataset presents data on major categories of products sold/services rendered for establishments of firms with payroll by kind of business.
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This data set provides a detailed look into the US economy. It includes information on establishments and nonemployer businesses, as well as sales revenue, payrolls, and the number of employees. Gleaned from the Economic Census done every five years, this data is a valuable resource to anyone curious about where the nation was economically at the time. With columns including geographic area name, North American Industry Classification System (NAICS) codes for industries, descriptions of those codes meaning of operation or tax status, and annual payroll, this information-rich dataset contains all you need to track economic trends over time. Whether you’re a researcher studying industry patterns or an entrepreneur looking for market insight — this dataset has what you’re looking for!
For more datasets, click here.
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This dataset provides detailed US industry data by state, including the number of establishments, value of sales, payroll, and number of employees. All the data is based on the North American Industry Classification System (NAICS) code for each specific industry. This will allow you to easily analyze and compare industries across different states or regions.
- Analyzing the economic impact of a new business or industry trends in different states: Comparing the change in the number of establishments, payroll, and employees over time can give insight into how a state is affected by a new industry trend or introduction of a new service or product.
- Estimating customer sales potential for businesses: This dataset can be used to estimate the potential customer base for businesses in different geographic areas. By analyzing total business done by non-employers in an area along with its estimated population can help estimate how much overall sales potential exists for a given region.
- Tracking competitor performance: By looking at shipments, receipts, and value of business done across industries in different regions or even cities, companies can track their competitors’ performance and compare it to their own to better assess their strategies going forward
If you use this dataset in your research, please credit the original authors. Data Source
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.
File: 2012 Industry Data by Industry and State.csv | Column name | Description | |:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------| | Geographic area name | The name of the geographic area the data is for. (String) | | NAICS code | The North American Industry Classification System (NAICS) code for the industry. (String) | | Meaning of NAICS code | The description of the NAICS code. (String) | | Meaning of Type of operation or tax status code | The description of the type of operation or tax status code. (String) ...
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United States Gross Value Added (GVA): saar data was reported at 19,931.717 USD bn in Mar 2018. This records an increase from the previous number of 19,699.332 USD bn for Dec 2017. United States Gross Value Added (GVA): saar data is updated quarterly, averaging 5,305.278 USD bn from Mar 1959 (Median) to Mar 2018, with 237 observations. The data reached an all-time high of 19,931.717 USD bn in Mar 2018 and a record low of 517.130 USD bn in Mar 1959. United States Gross Value Added (GVA): saar data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB074: Integrated Macroeconomic Accounts: Total Economy and Sectors: Selected Aggregates.
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Context
The dataset tabulates the Economy population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Economy. The dataset can be utilized to understand the population distribution of Economy by age. For example, using this dataset, we can identify the largest age group in Economy.
Key observations
The largest age group in Economy, PA was for the group of age 60-64 years with a population of 883 (9.75%), according to the 2021 American Community Survey. At the same time, the smallest age group in Economy, PA was the 80-84 years with a population of 232 (2.56%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Economy Population by Age. You can refer the same here
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TwitterThe U.S. Census Bureau.s economic indicator surveys provide monthly and quarterly data that are timely, reliable, and offer comprehensive measures of the U.S. economy. These surveys produce a variety of statistics covering construction, housing, international trade, retail trade, wholesale trade, services and manufacturing. The survey data provide measures of economic activity that allow analysis of economic performance and inform business investment and policy decisions. Other data included, which are not considered principal economic indicators, are the Quarterly Summary of State & Local Taxes, Quarterly Survey of Public Pensions, and the Manufactured Homes Survey. For information on the reliability and use of the data, including important notes on estimation and sampling variance, seasonal adjustment, measures of sampling variability, and other information pertinent to the economic indicators, visit the individual programs' webpages - http://www.census.gov/cgi-bin/briefroom/BriefRm.
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Government spending in the United States was last recorded at 39.7 percent of GDP in 2024 . This dataset provides - United States Government Spending To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.
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TwitterEconomic production - Gross Domestic Product.
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United States US: GDP: % of Manufacturing: Medium and High Tech Industry data was reported at 41.166 % in 2015. This stayed constant from the previous number of 41.166 % for 2014. United States US: GDP: % of Manufacturing: Medium and High Tech Industry data is updated yearly, averaging 49.199 % from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 51.786 % in 1998 and a record low of 38.398 % in 1996. United States US: GDP: % of Manufacturing: Medium and High Tech Industry data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Gross Domestic Product: Share of GDP. The proportion of medium and high-tech industry value added in total value added of manufacturing; ; United Nations Industrial Development Organization (UNIDO), Competitive Industrial Performance (CIP) database; ;
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Part Time Employment in the United States increased to 29034 Thousand in August from 28437 Thousand in July of 2025. This dataset provides - United States Part Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.
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TwitterTThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.
Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.
Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI
Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:
Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America
Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada
Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;
Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;
Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore
BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies
Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union
USMCA/8 Canada, Mexico, United States
Europe and Central Asia/9 Europe, Former Soviet Union
Middle East and North Africa/10 Middle East and North Africa
Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam
Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay
Indicator Source
Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.
Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.
GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.
Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.
Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.
Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.
Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.
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For a quick summary of the case study, please click "US Economy Powerpoint" and download the Powerpoint.
This dataset was inspired by rising prices for essential goods, the abnormally high inflation rate in March of 7.9 percent of this year, and the 30 trillion-dollar debt that we have. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.
This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.
I labeled all of the datasets to be self-explanatory based off of the title of the datasets. The US Economy Notebook has most of the code that I used as well as the four of the six phases of data analysis. The last two phases are in the US Economy Powerpoint. The "US Historical Inflation Rates" dataset could have also been labeled "The Inflation Of The US Dollar Month By Month". Lastly, the Average Sales of Houses in Jan is just a filtered version of "Average Sales of Houses in the US" dataset.