Inflation Rate in Canada decreased to 2.20 percent in October from 2.40 percent in September of 2025. This dataset provides - Canada Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Australia increased to 3.80 percent in the fourth quarter of 2025 from 3.20 percent in the third quarter of 2025. This dataset provides the latest reported value for - Australia Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Price quote data (for locally collected data only) and consumption segment indices that underpin consumer price inflation statistics, giving users access to the detailed data that are used in the construction of the UK’s inflation figures. The data are being made available for research purposes only and are not an accredited official statistic. From October 2024, private school fees and part-time education classes have been included in the consumption segment indices file. For more information on the introduction of consumption segments, please see the Consumer Prices Indices Technical Manual, 2019. Note that this dataset was previously called the consumer price inflation item indices and price quotes dataset.

This dataset provides values for INFLATION EXPECTATIONS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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.

Context

Happiness and well-being are essential indicators of societal progress, often influenced by economic conditions such as GDP and inflation. This dataset combines data from the World Happiness Index (WHI) and inflation metrics to explore the relationship between economic stability and happiness levels across 148 countries from 2015 to 2023. By analyzing key economic indicators alongside social well-being factors, this dataset provides insights into global prosperity trends.

Content

This dataset is provided in CSV format and includes 16 columns, covering both happiness-related features and economic indicators such as GDP per capita, inflation rates, and corruption perception. The main columns include:

Happiness Score & Rank (World Happiness Index ranking per country) Economic Indicators (GDP per capita, inflation metrics) Social Factors (Freedom, Social Support, Generosity) Geographical Information (Country & Continent)

Acknowledgements

The dataset is created using publicly available data from World Happiness Report, Gallup World Poll, and the World Bank. It has been structured for research, machine learning, and policy analysis purposes.

Inspiration

How do economic factors like inflation, GDP, and corruption affect happiness? Can we predict a country's happiness score based on economic conditions? This dataset allows you to analyze these relationships and build models to predict well-being trends worldwide.

This article describes the new RPIJ measure of Consumer Price Inflation. RPIJ is a Retail Prices Index (RPI) based measure that will use a geometric (Jevons) formula in place of one type of arithmetic formula (Carli). It is being launched in response to the National Statistician's conclusion that the RPI does not meet international standards due to the use of the Carli formula in its calculation. The accompanying Excel file includes a back series for RPIJ from 1997 to 2012. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: New RPIJ measure of Consumer Price Inflation

Inflation Rate in Philippines remained unchanged at 1.70 percent in October. This dataset provides the latest reported value for - Philippines Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Replication Files for “Inflation and Trading”

Codes: • 01a_rep_survey_data cleaning.do: cleaning raw survey data • 02a_rep_survey_data prep.do: preparing final survey dataset • 03a_rep_survey_data analysis.do: produces Figures 1-6 and Tables 1-5 and 8 • 02b_rep_bank_data prep.do: preparing final bank dataset • 03b_rep_bank_data analysis.do: produces Tables 6-7

Datasets: The folder 02_data contains survey and bank data. From the survey, we include pseudo data with the same structure as the original data needed to run the do-files 01a, 02a, and 03a, but the dataset contains only a random subsample of 300 respondents with random noise added to each continuous response. The original dataset is not available because it includes confidential information on customers of our partnering bank. • rep_survey_data raw.dta: raw survey data for a random subsample of 300 respondents and with added noise to each continuous variable. We also exclude open-ended responses at the beginning and end of survey for confidentiality reasons. These responses do not feature in the main analysis of the paper • rep_survey_data clean.dta: survey data after transformation of the raw variables • rep_survey_data final.dta: preparation of final survey dataset

From the bank, we include a dataset with the same structure as the original data that allows the do-files 01b and 02b to run. The dataset includes only the necessary variables needed for the analysis, and we select a subsample of customers to match the 300 respondents randomly drawn from the set of survey respondents. The original datasets are not available since they use proprietary information from the partnering bank. • rep_bank_data sum stat pf.dta: portfolio summary statistics, coming from confidential portfolio data from the bank, and used for Table 1 • rep_bank_data sum stat trading: trading summary statistics, coming from tab6a • rep_bank_data tab1.dta: demographics data from bank • rep_bank_data tab6a: trading data from bank • rep_bank_data final.dta: final dataset from bank, which combines tab1, tab6a, and select variables from the survey for the subsample of survey respondents

Runtime: We run the codes on a MacBook Pro laptop with Stata 19 MP. Runtime is below 10 minutes on real data and below one minute on pseudo data.

Monthly and long-term Peru Inflation data: historical series and analyst forecasts curated by FocusEconomics.

Inflation Expectations in the United States decreased to 3.20 percent in October from 3.40 percent in September of 2025. This dataset provides - United States Consumer Inflation Expectations- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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 Sumpter. 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 Sumpter, while the Census reported a median income of $19,541 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the city of Sumpter was not possible.

- Full-time workers, aged 15 years and older: In Sumpter, for full-time, year-round workers aged 15 years and older, the Census Bureau did not report the median income for both males and females due to an insufficient number of sample observations.

As income data for both males and females was unavailable, conducting a comprehensive analysis of gender-based pay disparity in the city of Sumpter was not possible.

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 Sumpter median household income by race. You can refer the same 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 Otway. 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 Otway, the Census Bureau did not report the median income for all workers aged 15 years and older, regardless of work hours, for both males and females due to an insufficient number of sample observations.

Due to unavailability of income data for both males and females, conducting a comprehensive analysis of gender-based pay disparity in the village of Otway was not possible.

- Full-time workers, aged 15 years and older: In Otway, for full-time, year-round workers aged 15 years and older, the Census reported a median income of $73,000 for females, while data for males was unavailable due to an insufficient number of sample observations.

As there was no available median income data for males, conducting a comprehensive assessment of gender-based pay disparity in Otway was not feasible.

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 Otway median household income by race. You can refer the same 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 Grace City. 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 Grace City, the Census Bureau did not report the median income for all workers aged 15 years and older, regardless of work hours, for both males and females due to an insufficient number of sample observations.

Due to unavailability of income data for both males and females, conducting a comprehensive analysis of gender-based pay disparity in the city of Grace City was not possible.

- Full-time workers, aged 15 years and older: In Grace City, for full-time, year-round workers aged 15 years and older, the Census reported a median income of $63,750 for females, while data for males was unavailable due to an insufficient number of sample observations.

As there was no available median income data for males, conducting a comprehensive assessment of gender-based pay disparity in Grace City was not feasible.

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 Grace City median household income by race. You can refer the same here

Inflation Rate in Norway decreased to 3.10 percent in October from 3.60 percent in September of 2025. This dataset provides - Norway Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Ukraine decreased to 10.90 percent in October from 11.90 percent in September of 2025. This dataset provides the latest reported value for - Ukraine Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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 Miller'S Cove. 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 Miller'S Cove, the Census Bureau did not report the median income for all workers aged 15 years and older, regardless of work hours, for both males and females due to an insufficient number of sample observations.

Due to unavailability of income data for both males and females, conducting a comprehensive analysis of gender-based pay disparity in the town of Miller'S Cove was not possible.

- Full-time workers, aged 15 years and older: In Miller'S Cove, for full-time, year-round workers aged 15 years and older, while the Census reported a median income of $31,875 for males, while data for females was unavailable due to an insufficient number of sample observations.

As there was no available median income data for females, conducting a comprehensive assessment of gender-based pay disparity in Miller'S Cove was not feasible.

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 Miller'S Cove median household income by race. You can refer the same here

Core Inflation Rate MoM in the United States decreased to 0.20 percent in September from 0.30 percent in August of 2025. This dataset includes a chart with historical data for the United States Core Inflation Rate MoM.

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 Obert. 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 Obert, while the Census reported a median income of $36,563 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the village of Obert was not possible.

- Full-time workers, aged 15 years and older: In Obert, for full-time, year-round workers aged 15 years and older, the Census Bureau did not report the median income for both males and females due to an insufficient number of sample observations.

As income data for both males and females was unavailable, conducting a comprehensive analysis of gender-based pay disparity in the village of Obert was not possible.

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 Obert median household income by race. You can refer the same here

This dataset contains monthly retail price data for a wide range of consumer products sold in various Canadian provinces over several years. It has been enriched with tax, category, and classification metadata for deeper insights.

Usefulness of the Dataset

This dataset can be used for:

Use Case	Description
Price Trend Analysis	Track price movements over time, province, and product category.
Inflation Studies	Examine inflation on essentials vs non-essentials over time.
Regional Price Comparison	Analyze cost disparities for the same goods across provinces.
Tax Policy Impact	Understand how tax laws affect consumer pricing by region.
Budget Optimization	Identify high-cost vs low-cost essentials for better planning.
Machine Learning Integration	Use in models for price prediction or consumer segmentation.

Purpose and Use Cases

This dataset is ideal for:

🏛️ Policy Analysis

Understand how federal and provincial taxes shape price access — especially for essentials like milk, bread, or medications.

🧍‍♀️ Consumer Insights

See how costs for personal care, food, and baby goods evolve month-over-month in each region.

💸 Inflation & Seasonality

Analyze how monthly or yearly trends (e.g., holiday spikes or inflation events) affect product pricing.

🌍 Social Impact Studies

Measure product accessibility gaps between provinces for low-income consumers or high-tax regions.

🛍️ Retail & Budget Planning

Guide families, retailers, or policymakers on where and when to buy or subsidize certain products.