Inflation Rate in the United States increased to 3 percent in September from 2.90 percent in August of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Description:

This dataset provides key economic indicators from various countries between 2010 and 2023. The dataset includes monthly data on inflation rates, GDP growth rates, unemployment rates, interest rates, and stock market index values. The data has been sourced from reputable global financial institutions and is suitable for economic analysis, machine learning models, and forecasting economic trends.

Data Sources:

The data has been generated to simulate real-world economic conditions, mimicking information from trusted sources like: - World Bank for GDP growth and inflation data - International Monetary Fund (IMF) for macroeconomic data - OECD for labor market statistics - National Stock Exchanges for stock market index values

Columns:

1. Date: The specific date (in Year/Month/Day format) representing when the data was collected.
2. Country: The country the data pertains to (e.g., USA, Germany, Japan).
3. Inflation Rate (%): The rate of inflation for that country, showing how fast prices for goods and services are increasing.
4. GDP Growth Rate (%): The percentage growth of the country’s Gross Domestic Product (GDP), indicating economic expansion or contraction.
5. Unemployment Rate (%): The percentage of the working-age population that is unemployed.
6. Interest Rate (%): The central bank's interest rate, used to control inflation and influence the economy.
7. Stock Index Value: The value of the country’s main stock market index, reflecting the performance of the stock market.

Potential Uses: - Economic Analysis: Researchers and analysts can use this dataset to study trends in inflation, GDP growth, unemployment, and other economic factors. - Machine Learning: This dataset can be used to train models for predicting economic trends or market performance. Financial Forecasting: Investors and economists can leverage this data for forecasting market movements based on economic conditions. - Comparative Studies: The dataset allows comparisons across countries and regions, offering insights into global economic performance.

Cost of food in the United States increased 3.10 percent in September of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

This dataset is about books. It has 1 row and is filtered where the book is Choice in currency : a way to stop inflation. It features 7 columns including author, publication date, language, and book publisher.

About Dataset

https://www.tbsnews.net/sites/default/files/styles/big_2/public/images/2021/03/12/inflation_1.jpg" alt="Inflation hits nine-year high in June | undefined">###

Global Energy, Food, Consumer, and Producer Price Inflation: A Comprehensive Dataset for Understanding Economic Trends

Key Concepts:

1. Energy Consumer Price Inflation data.
2. Food Consumer Price Inflation data.
3. Headline Consumer Price Inflation data.
4. Official Core Consumer Price Inflation data.
5. Producer Price Inflation data.
6. 206 Countries name, Country code and IMF code.
7. 52 Years data from 1970 to 2022.

The global economy is highly complex, and understanding economic trends and patterns is crucial for making informed decisions about investments, policies, and more. One key factor that impacts the economy is inflation, which refers to the rate at which prices increase over time. The Global Energy, Food, Consumer, and Producer Price Inflation dataset provides a comprehensive collection of inflation rates across 206 countries from 1970 to 2022, covering four critical sectors of the economy.

Finally, the Global Producer Price Inflation dataset provides a detailed look at price changes at the producer level, providing insights into supply chain dynamics and trends. This data can be used to make informed decisions about investments in various sectors of the economy and to develop effective policies to manage producer price inflation.

In conclusion, the Global Energy, Food, Consumer, and Producer Price Inflation dataset provides a comprehensive resource for understanding economic trends and patterns across 206 countries. By examining this data, analysts can gain insights into the complex factors that impact the economy and make informed decisions about investments, policies, and more.

Potential User:

1. Economists and economic researchers
2. Policy makers and government officials
3. Investors and financial analysts
4. Agricultural researchers and policymakers
5. Energy analysts and policy makers
6. Food industry professionals
7. Business leaders and decision makers
8. Academics and students in economics, finance, and related fields

Acknowledgements:

The data were collected from the official website of worldbank.org

The history of inflation in Uruguay has been a constant challenge for the country's economy. Throughout much of the 20th century, Uruguay experienced high levels of inflation, especially in the 1960s and 1970s. Chronic inflation severely affected the purchasing power of citizens and eroded economic stability. However, starting in the 1990s, the country implemented measures to control inflation, including adopting an inflation targeting regime and a more prudent fiscal policy. These measures had a positive impact, achieving a significant reduction in inflation and greater economic stability in Uruguay in recent decades. Although challenges persist, the fight against inflation has been a key objective for the country, aiming to ensure sustainable growth and improve the well-being of its population.

This dataset was generated by the National Institute of Statistic of Uruguay. They are the ones collecting the information to create the Consumer Price Index.

Their web page is: https://www.gub.uy/instituto-nacional-estadistica/datos-y-estadisticas/estadisticas/series-historicas-ipc-base-octubre-2022100

And the name of the original file is: IPC general, Total País (desde 07/1937), Montevideo e Interior (desde 12/2010), base Octubre 2022=100

ABSTRACTAdministered prices and government discretion. Administered prices during the first term of President Dilma were used as an instrument to meet inflation target, so as to subordinate industrial policies to short run macroeconomic aims. This strategy was ineffective to control inflation and distorted investment and consumption decisions. The article shows that prices tend to deviate more the larger their weight in the price index, and tend to vary consistently with the political cycles. The article concludes with policy suggestions to control the negative effect of deviations of government discretion to determine administered prices.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following areas: Afghanistan, Armenia, Bangladesh, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Gambia, The, Guinea, Guinea-Bissau, Haiti, Indonesia, Iraq, Kenya, Lao PDR, Lebanon, Liberia, Libya, Malawi, Mali, Mauritania, Mozambique, Myanmar, Niger, Nigeria, Philippines, Senegal, Somalia, South Sudan, Sri Lanka, Sudan, Syrian Arab Republic, Yemen, Rep.

Data was cleaned and prepared for a data visualization comparing the Federal Funds Rate to the 10-Year Breakeven Inflation Rate. The purpose of this project was to visualize a perspective of the Federal Reserve. With the Federal Reserve raising rates to control inflation, many are debating when will the Federal Reserve pause raising rates or cut rates. The 10-Year Breakeven Inflation Rate is still well above the Federal Reserve's FAIT (Flexible Average Inflation Targeting) of 2% for that reason the Federal Reserve still has room to play with the Funds Rate.

📂 Dataset Overview - Rows (Entries): 110 - Columns (Features): 6

Columns Description 1. Date - Format: MMM-YYYY (e.g., Jul-2025) - Monthly observations 1. Inflation_YoY (Year-on-Year Inflation %) - Inflation rate in percentage (YoY basis) - Range: 0.3% – 38% - Average: 11.6% - Can be treated as the dependent variable

1. Oil_Price_USD_Barrel
2. Global crude oil price (USD per barrel)
3. Range: 15.18 – 113.77

4. Average: 62.75

5. Exchange_Rate_PKR_USD

• Pakistani Rupee per US Dollar exchange rate
• Range: 104.6 – 304.8
• Average: 185.0

1. Interest_Rate

• State Bank of Pakistan policy rate (%)
• Range: 6.8% – 21.46%
• Average: 11.8%

1. Money_Supply_M2_Billion
2. Broad Money Supply (M2) in billion PKR
3. Range: 12,486 – 41,786
4. Average: 23,124

📊 Statistical Insights

Inflation Trends: High volatility observed between 2019–2023 (peaking at 38%), while in 2025 inflation dropped to ~3–4%.

Oil Price Relation: Fluctuations in crude oil prices appear linked with inflation movements.

Exchange Rate Impact: The depreciation of PKR from ~104 to 300+ significantly impacted inflation and interest rates.

Interest Rate Policy: Mostly ranged between 7–15%, but spiked to ~21% during currency crisis.

Money Supply Growth: Broad money consistently increased, adding long-term inflationary pressure.

📈**Possible Analyses for Kaggle**

1. Trend Analysis

2. Monthly inflation, oil price, exchange rate visualization.

3. Correlation Study

4. Inflation vs Oil Prices

5. Inflation vs Exchange Rate

6. Inflation vs Interest Rate

7. Forecasting Models

8. Time-Series forecasting (ARIMA, Prophet)

9. Regression models using oil prices, exchange rate, and money supply as predictors

10. Economic Insights

• Impact of global oil shocks on Pakistan’s inflation
• Role of monetary policy in inflation control
• Currency depreciation vs domestic inflation

Experimental evolution studies can be used to explore genomic response to artificial and natural selection. In such studies, loci that display larger allele frequency change than expected by genetic drift alone are assumed to be directly or indirectly associated with traits under selection. However, such studies report surprisingly many loci under selection, suggesting that current tests for allele frequency change may be subject to p-value inflation and hence be anti-conservative. One factor known from genome wide association (GWA) studies to cause p-value inflation is population stratification, such as relatedness among individuals. Here we suggest that by treating presence of an individual in a population after selection as a binary response variable, existing GWA methods can be used to account for relatedness when estimating allele frequency change. We show that accounting for relatedness like this effectively reduces false positives in tests for allele frequency change in simulated data with varying levels of population structure. However, once relatedness has been accounted for, the power to detect causal loci under selection is low. Finally, we demonstrate the presence of p-value inflation in allele frequency change in empirical data spanning multiple generations from an artificial selection experiment on tarsus length in two wild populations of house sparrow, and correct for this using genomic control. Our results indicate that since allele frequencies in large parts of the genome may change when selection acts on a heritable trait, such selection is likely to have considerable and immediate consequences for the eco-evolutionary dynamics of the affected populations.

This research note reconsiders the question of whether central bank independence (CBI) and fixed exchange rates (FIX) function as substitutes or complements. We argue that these monetary institutions have neither served as substitutes nor performed as complements for either inflation control or exchange rate stability. In terms of their substitutability, our statistical evidence shows that while CBI has been used for inflation control, FIX has been more directed toward exchange rate stability using updated datasets with these monetary institutions measured both on a de jure and de facto basis with nearly global country/year coverage from 1970 to 2020. In terms of their complementarity, our results also demonstrate that CBI was not more effective at reducing inflation when paired with greater FIX, and FIX was not more effective at promoting exchange rate stability when paired with greater CBI. If anything, both are less effective when paired with the other monetary institution.

Included is a Real GDP dataset created from Kaggle datasets located here and here combined using standard data analysis methodology which can be found here.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Monitor charter township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Monitor charter township, the median income for all workers aged 15 years and older, regardless of work hours, was $54,320 for males and $32,413 for females.

These income figures highlight a substantial gender-based income gap in Monitor charter township. Women, regardless of work hours, earn 60 cents for each dollar earned by men. This significant gender pay gap, approximately 40%, underscores concerning gender-based income inequality in the township of Monitor charter township.

- Full-time workers, aged 15 years and older: In Monitor charter township, among full-time, year-round workers aged 15 years and older, males earned a median income of $70,717, while females earned $60,288, resulting in a 15% gender pay gap among full-time workers. This illustrates that women earn 85 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the township of Monitor charter township.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Monitor charter township.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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.

Inspiration

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/.

Recommended for further research

This dataset is a part of the main dataset for Monitor charter township median household income by race. You can refer the same here

Replication Package – Decompressing to Prevent Unrest David Altman, Pontificia Universidad Católica de Chile 1. Description This dataset accompanies the article: Altman, David. “Decompressing to prevent unrest: political participation through citizen-initiated mechanisms of direct democracy” (2025), Social Movement Studies. It contains the data and code necessary to replicate all statistical analyses and tables presented in the article. 2. Coverage Time frame: 1970–2019 Countries: 116 democracies worldwide (electoral and liberal, according to V-Dem v14) Unit of analysis: Country-year 3. Data Sources V-Dem v14 (Coppedge et al., 2024): direct democracy indices (CIC-DPVI, TOC-DPVI), civil society participation index. NAVCO 1.3 (Chenoweth & Shay, 2020): violent and nonviolent resistance campaigns (dependent variable). World Bank, World Development Indicators: GDP per capita (constant 2015 US$), inflation. Author’s coding: harmonization and cleaning of datasets, construction of dependent variable (excluding self-determination/secession cases). 4. Variables accepted: dichotomous dependent variable (1 if violent or nonviolent regime-change/“other” campaign occurred in a given year; 0 otherwise). CIC_DPVI: citizen-initiated component of V-Dem’s Direct Popular Vote Index. TOC_DPVI: top-down component of direct democracy (plebiscites, obligatory referenda). pc_GDP: GDP per capita (constant 2015 US$). Inflation: annual inflation (%). v2x_cspart: Civil Society Participation Index (V-Dem). country, year: identifiers. 5. Files Included data.dta / data.csv – panel dataset used in the article. master.do – Stata do-file to reproduce all analyses. tables.do – generates Tables 1–2. figures.do – generates Figure 1 (coefficient plot). ReadMe.txt – this document. 6. Instructions Open master.do in Stata (v17 or later). Set working directory to the folder containing the replication package. Run the file. This will: Load data.dta Estimate the models (fixed-effects and random-effects logit with lagged IVs) Produce Tables 1–2 in /results/ Produce Figure 1 in /figures/ 7 Citation If you use this dataset, please cite: Altman, David (2025). Replication data for: Decompressing to Prevent Unrest: Political Participation through Citizen-Initiated Mechanisms of Direct Democracy. Harvard Dataverse. DOI: [to be added]

About this dataset

Context

The dataset illustrates the median household income in Monitor charter township, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2023, the median household income for Monitor charter township increased by $3,052 (4.09%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 5 years and declined for 8 years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

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.

Inspiration

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/.

Recommended for further research

This dataset is a part of the main dataset for Monitor charter township median household income. You can refer the same here

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Monitor charter township. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Monitor charter township. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2021

In terms of income distribution across age cohorts, in Monitor charter township, the median household income stands at $97,837 for householders within the 45 to 64 years age group, followed by $97,231 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $56,748.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

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.

Inspiration

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/.

Recommended for further research

This dataset is a part of the main dataset for Monitor charter township median household income by age. You can refer the same here

Expression divergence is thought to be a hallmark of functional diversification between homologs post duplication. Modification in regulatory elements has been invoked to explain expression divergence after duplication for several MADS-box genes, however, verification of reciprocal loss of cis-regulatory elements is lacking in plants. Here, we report that the evolution of MPF2-like genes has entailed degenerative mutations in a core promoter CArG-box and an auxin response factor (ARF) binding element in the large 1st intron in the coding region. Previously, MPF2-like genes were duplicated into MPF2-like-A and -B through genome duplication in Withania and Tubocapsicum (Withaninae). The calyx of Withania grows exorbitantly after pollination unlike Tubocapsicum, where it degenerates. Besides inflated calyx syndrome formation, MPF2-like transcription factors are implicated in functions both during the vegetative and reproductive development as well as in phase transition. MPF2-like-A of Withania (WSA206) is strongly expressed in sepals, while MPF2-like-B (WSB206) is not. Interestingly, their combined expression patterns seem to replicate the pattern of their closely related hypothetical progenitors from Vassobia and Physalis. Using phylogenetic shadowing, site-directed mutagenesis and motif swapping, we could show that the loss of a conserved CArG-box in MPF2-like-B of Withania is responsible for impeding its expression in sepals. Conversely, loss of an ARE in MPF2-like-A relaxed the constraint on expression in sepals. Thus, the ARE is an active suppressor of MPF2-like gene expression in sepals, which in contrast is activated via the CArG-box. The observed expression divergence in MPF2-like genes due to reciprocal loss of cis-regulatory elements has added to genetic and phenotypic variations in the Withaninae and enhanced the potential of natural selection for the adaptive evolution of ICS. Moreover, these results provide insight into the interplay of floral developmental and hormonal pathways during ICS development and add to the understanding of the importance of polyploidy in plants.

This national survey focuses on attitudes toward economic conditions, causes of inflation, and wage-price controls.Questions include personal financial status, equity of various price increases, overall performance and effectiveness of Pay Board and Price Commission, prices of food and other goods, wage increases, wage- price freeze. There are also some current events questions that focus on the upcoming presidential election and include rating of Richard Nixon, Spiro Agnew, and George McGovern.