Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

This dataset was a manual combination of 3 separate datasets

Inflation Rates (Data set was manually manipulated to match the format of the other datasets) https://data.bls.gov/timeseries/CUUR0000SA0?years_option=all_years

Feds Funds Rate https://fred.stlouisfed.org/series/FEDFUNDS#

Monthly Mortgage Rate https://fred.stlouisfed.org/series/MORTGAGE30US

Additional Data Manipulation: Both the monthly feds fund rate and the monthly mortgage rate is an annualized rate. I created annualized inflation rate based on a 12 month lag. To find the inflation rate for a give month, the formula used was (current month CPI - 12-months prior CPI / 12-months prior CPI)

D'Amico, Kim, and Wei use a no-arbitrage term structure model to decompose TIPS inflation compensation into three components: inflation expectation, inflation risk premium, and TIPS liquidity premium over the 1983-present period. The model is also used to decompose nominal yields or forward rates into four components: expected real short rate, expected inflation, inflation risk premium, and real term premium.

Context

I'm creating a new website in which I need this type of data. I didn't found it easily available as I had to scrape it from an interactive graph, so now I upload it here for everyone

Content

In this dataset you can find real and nominal gold prices since 1791 to 2020. The explanation of the differences between real and nominal prices are:

· Nominal values are the current monetary values. · Real values are adjusted for inflation and show prices/wages at constant prices. · Real values give a better guide to what you can actually buy and the opportunity costs you face.

Example of real vs nominal:

· If you receive an 8% increase in your wages from £100 to £108, this is the nominal increase. · However, if inflation is 2%, then the real increase in wages is (8-2%) 6%. · The real wage is a better guide to how your living standards changes. It shows what you are actually able to buy with the extra increase in wages. · If wages increased 80%, but inflation was also 80%, the real increase in wages would be 0% – in effect, despite the monetary increase in wages of 80%, the amount of goods and services you could buy would be the same.

Hope this dataset is useful for you! Any questions or answers do not hesitate in contact me.

Context

I'm creating a new website (centralbankanalytics.com) in which I need this type of data. I didn't found it easily available as I had to scrape it from an interactive graph, so now I upload it here for everyone

Content

In this dataset you can find real and nominal silver prices since 1791 to 2020. The explanation of the differences between real and nominal prices are:

· Nominal values are the current monetary values. · Real values are adjusted for inflation and show prices/wages at constant prices. · Real values give a better guide to what you can actually buy and the opportunity costs you face.

Example of real vs nominal:

· If you receive an 8% increase in your wages from £100 to £108, this is the nominal increase. · However, if inflation is 2%, then the real increase in wages is (8-2%) 6%. · The real wage is a better guide to how your living standards changes. It shows what you are actually able to buy with the extra increase in wages. · If wages increased 80%, but inflation was also 80%, the real increase in wages would be 0% – in effect, despite the monetary increase in wages of 80%, the amount of goods and services you could buy would be the same.

Hope this dataset is useful for you! Any questions or answers do not hesitate in contact me.

This dataset provides a historical overview of key global indicators, including Gross Domestic Product (GDP), population growth, and CO2 emissions. It captures economic trends, demographic shifts, and environmental impacts over multiple decades, making it useful for researchers, analysts, and policymakers.

The dataset includes Real GDP (inflation-adjusted), allowing for economic trend analysis while accounting for inflation effects. Additionally, it incorporates CO2 emissions data, enabling studies on the relationship between economic growth and environmental impact.

This dataset is valuable for multiple research areas:

✅ Macroeconomic Analysis – Study global economic growth, recessions, and recovery trends.

✅ Inflation & Monetary Policy – Compare nominal vs. real GDP to assess inflationary trends.

✅ Climate Change Research – Analyze CO2 emissions alongside economic growth to identify sustainability challenges.

✅ Predictive Modeling – Train machine learning models for forecasting GDP, population, or emissions.

✅ Public Policy & Development – Evaluate the impact of economic and environmental policies over time.

This dataset is shared for educational and analytical purposes only.

This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

Introduction This note summarises trends in pay in London and the UK since 2010 and compares them to inflation trends. The focus is on median gross weekly earnings for all employees (full- and part-time) working in London. The counterfactual analysis is based on annual pay estimates. Notes on the data The employee pay estimates in this note do not cover self-employed jobs and come from a survey of UK businesses. There are, moreover, several discontinuities in the ONS ASHE series (e.g. in 2004, 2006, 2011 and 2021). The growth rates calculated over these periods are illustrative, not precise figures. During the pandemic earnings estimates were affected by compositional changes and the furlough scheme, making interpretation more difficult. Data collection disruption and lower response rates also mean that estimates for 2020 and 2021 are subject to greater uncertainty. Real earnings (earnings adjusted for inflation) have been calculated by adjusting nominal (unadjusted) earnings using the Consumer Prices Index including owner occupiers’ housing costs (CPIH). The CPIH is the most comprehensive measure of inflation in the UK.

Real interest rates refer to the nominal interest rate adjusted for inflation, and are an important economic indicator that can have significant impacts on investment, savings, and overall economic growth. Real interest rates can affect the demand for goods and services, investment decisions, and borrowing costs, among other things.

The real interest rates per country dataset provides a comprehensive overview of the real interest rates of each country. The dataset includes information on the real interest rates, covering all countries in the world. It is compiled from various sources, including national central banks, international financial institutions such as the International Monetary Fund (IMF), and other relevant data sources.

The real interest rates per country dataset can be used by researchers, policymakers, and investors to gain insight into the economic conditions of different countries and to compare the relative levels of real interest rates across the world. It can also be used to monitor changes in real interest rates over time and to evaluate the effectiveness of monetary policies and strategies.

Overall, the real interest rates per country dataset is an important resource for understanding the economic conditions of different countries and for developing policies and strategies that promote sustainable economic growth and stability.

VITAL SIGNS INDICATOR Economic Output (EC13)

FULL MEASURE NAME Gross regional product

LAST UPDATED July 2019

DESCRIPTION Economic output is measured by the total and per-capita gross regional product and refers to the value of goods and services generated by workers and companies in a region.

DATA SOURCE Bureau of Economic Analysis: Regional Economic Accounts 2001-2017 http://www.bea.gov/regional/

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) As gross regional product data is only available on the MSA level, Bay Area data includes 10 counties (the nine core counties + San Benito County); this results in a slightly higher regional GRP as a result of additional population and business activity. Per-capita data reflects the additional population included as a result of San Benito County’s participation in the San Jose MSA. Data is inflation-adjusted by using both nominal and real data developed by BEA and appropriately escalating real GRP data in 2009 dollars to today’s dollars (2017). This inflation adjustment approach is specific to each MSA and is different from the CPI inflation approach used for other datasets on the Vital Signs website.

VITAL SIGNS INDICATOR Economic Output (EC14)

FULL MEASURE NAME Per-capita gross regional product

LAST UPDATED July 2019

DESCRIPTION Economic output is measured by the total and per-capita gross regional product and refers to the value of goods and services generated by workers and companies in a region.

DATA SOURCE Bureau of Economic Analysis: Regional Economic Accounts 2001-2017 http://www.bea.gov/regional/

California Department of Finance: Population and Housing Estimates 2001-2009 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/ Note: Table E-8

California Department of Finance: Population and Housing Estimates 2010-2017 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/ Note: Table E-5

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) As gross regional product data is only available on the MSA level, Bay Area data includes 10 counties (the nine core counties + San Benito County); this results in a slightly higher regional GRP as a result of additional population and business activity. Per-capita data reflects the additional population included as a result of San Benito County’s participation in the San Jose MSA. Data is inflation-adjusted by using both nominal and real data developed by BEA and appropriately escalating real GRP data in 2009 dollars to today’s dollars (2017). This inflation adjustment approach is specific to each MSA and is different from the CPI inflation approach used for other datasets on the Vital Signs website.

VITAL SIGNS INDICATOR Economic Output (EC13)

FULL MEASURE NAME Gross regional product

LAST UPDATED July 2019

DESCRIPTION Economic output is measured by the total and per-capita gross regional product and refers to the value of goods and services generated by workers and companies in a region.

DATA SOURCE Bureau of Economic Analysis: Regional Economic Accounts 2001-2017 http://www.bea.gov/regional/

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) As gross regional product data is only available on the MSA level, Bay Area data includes 10 counties (the nine core counties + San Benito County); this results in a slightly higher regional GRP as a result of additional population and business activity. Per-capita data reflects the additional population included as a result of San Benito County’s participation in the San Jose MSA. Data is inflation-adjusted by using both nominal and real data developed by BEA and appropriately escalating real GRP data in 2009 dollars to today’s dollars (2017). This inflation adjustment approach is specific to each MSA and is different from the CPI inflation approach used for other datasets on the Vital Signs website.

Nominal GDP is an assessment of economic production in an economy but includes the current prices of goods and services in its calculation. GDP is typically measured as the monetary value of goods and services produced.

**Real gross domestic product **(real GDP for short) is a macroeconomic measure of the value of economic output adjusted for price changes (i.e. inflation or deflation). This adjustment transforms the money-value measure, nominal GDP, into an index for quantity of total output.estions do you want to see answered?

The dataset contains information on global GDP (Gross Domestic Product) from various years. It includes details such as GDP real (adjusted for inflation), GDP growth rate, per capita GDP nominal (in current USD), population change, and world population. The data provides insights into the economic performance and demographic trends across different years.

**Source: The data is sourced from Worldometer's GDP page, which provides comprehensive statistics on global GDP.

Date: The data spans multiple years, from the early 1960s to the latest available data.**

VITAL SIGNS INDICATOR
Rent Payments (EC8)

FULL MEASURE NAME
Median rent payment

LAST UPDATED
January 2023

DESCRIPTION
Rent payments refer to the cost of leasing an apartment or home and serves as a measure of housing costs for individuals who do not own a home. The data reflect the median monthly rent paid by Bay Area households across apartments and homes of various sizes and various levels of quality. This differs from advertised rents for available apartments, which usually are higher. Note that rent can be presented using nominal or real (inflation-adjusted) dollar values; data are presented inflation-adjusted to reflect changes in household purchasing power over time.

DATA SOURCE
U.S. Census Bureau: Decennial Census - https://nhgis.org
Count 2 (1970)
Form STF1 (1980-1990)
Form SF3a (2000)

U.S. Census Bureau: American Community Survey - https://data.census.gov/
Form B25058 (2005-2021; median contract rent)

Bureau of Labor Statistics: Consumer Price Index - https://www.bls.gov/data/
1970-2021

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Rent data reflects median rent payments rather than list rents (refer to measure definition above). American Community Survey 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

1970 Census data for median rent payments has been imputed from quintiles using methodology from California Department of Finance as the source data only provided the mean, rather than the median, monthly rent. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Inflation-adjusted data are presented to illustrate how rent payments have grown relative to overall price increases; that said, the use of the Consumer Price Index (CPI) does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself.

This dataset supports the research exploring the impact of monetary policy instruments on the Colombian economy, focusing on the classical dichotomy and monetary neutrality. The analysis delves into how monetary policy, including instruments such as interest rates and money supply, influences both nominal and real variables in the economy. It also highlights the relationship between monetary policy and economic stability, particularly how central banks manage inflation and economic growth. Key sections explore the separation between nominal and real variables as explained by the classical dichotomy, and the principle of monetary neutrality, which argues that changes in money supply affect nominal variables without impacting real economic factors.

The dataset is structured around a combination of theoretical insights and simulations that analyze the effectiveness of monetary neutrality in the Colombian context, given both domestic and international economic challenges such as the war in Ukraine and agricultural sector disruptions. Through simulations, the dataset demonstrates the effects of monetary expansion on variables like inflation, production, and employment, providing a framework for understanding current economic trends and proposing solutions to socio-economic challenges in Colombia.

This paper is aimed at investigating the effects of government intervention through unemployment benefits on macroeconomic dynamics in an agent based decentralized matching framework. The major result is that the presence of such a public intervention in the economy stabilizes the aggregate demand and the financial conditions of the system at the cost of a modest increase of both the inflation rate and the ratio between public deficit and nominal GDP. The successful action of the public sector is sustained by the central bank which is committed to buy outstanding government securities.

A synthetic, event-aware monthly spending dataset in Indian rupees spanning January 2020 — September 2025 (69 rows). Designed for exploratory data analysis, time-series modeling, and demonstrative analysis of personal finance patterns, the dataset encodes realistic macro and life events:

COVID-19 impact (Mar 2020 – Dec 2021): reduced income and temporary changes in discretionary spending.

Salary hikes: step increases applied in April 2021, April 2023, and April 2024.

Marriage: November 2023 marks a household change — higher rent and household expenses thereafter.

EMI / loan period: a vehicle/home loan EMI between Jan 2024 and Jan 2025.

Inflation & seasonality: gradually rising nominal values and seasonal spikes in shopping/dining during festival months (e.g., Oct–Nov) and occasional variations for monsoon/holiday months.

This dataset is synthetic (generated programmatically) and contains no personal or sensitive real-world data. It is intended for teaching, demos, model prototyping, visualization, and algorithm testing. See the “Column descriptors” section for details on each field and the assumptions used to generate them.

License: CC0 Citation: Rajat. Monthly Spending Dataset (2020–2025) (synthetic)

This dataset contains the average real wages for Fulton County, metro Atlanta and the U.S. calculated semi-annually. Real wages represent actual (nominal) wages adjusted for inflation using the Consumer Price Index (CPI). Real wages are expressed in terms of 1982-1984 dollars. Semi-annual numbers are used because local area wages are reported by the Bureau of Labor Statistics (BLS) quarterly and CPI is reported every two months; therefore, reporting periods coincide twice a year. Real wages are reported as moving 12-month averages in order to better illustrate the general trend over time. Nominal wages and CPI are from the BLS; semi-annual real wages are calculated by the Fulton County Strategy & Performance Management Office.