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The provided dataset is extracted from yahoo finance using pandas and yahoo finance library in python. This deals with stock market index of the world best economies. The code generated data from Jan 01, 2003 to Jun 30, 2023 that’s more than 20 years. There are 18 CSV files, dataset is generated for 16 different stock market indices comprising of 7 different countries. Below is the list of countries along with number of indices extracted through yahoo finance library, while two CSV files deals with annualized return and compound annual growth rate (CAGR) has been computed from the extracted data.
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F90ce8a986761636e3edbb49464b304d8%2FNumber%20of%20Index.JPG?generation=1688490342207096&alt=media" alt="">
This dataset is useful for research purposes, particularly for conducting comparative analyses involving capital market performance and could be used along with other economic indicators.
There are 18 distinct CSV files associated with this dataset. First 16 CSV files deals with number of indices and last two CSV file deals with annualized return of each year and CAGR of each index. If data in any column is blank, it portrays that index was launch in later years, for instance: Bse500 (India), this index launch in 2007, so earlier values are blank, similarly China_Top300 index launch in year 2021 so early fields are blank too.
The extraction process involves applying different criteria, like in 16 CSV files all columns are included, Adj Close is used to calculate annualized return. The algorithm extracts data based on index name (code given by the yahoo finance) according start and end date.
Annualized return and CAGR has been calculated and illustrated in below image along with machine readable file (CSV) attached to that.
To extract the data provided in the attachment, various criteria were applied:
Content Filtering: The data was filtered based on several attributes, including the index name, start and end date. This filtering process ensured that only relevant data meeting the specified criteria.
Collaborative Filtering: Another filtering technique used was collaborative filtering using yahoo finance, which relies on index similarity. This approach involves finding indices that are similar to other index or extended dataset scope to other countries or economies. By leveraging this method, the algorithm identifies and extracts data based on similarities between indices.
In the last two CSV files, one belongs to annualized return, that was calculated based on the Adj close column and new DataFrame created to store its outcome. Below is the image of annualized returns of all index (if unreadable, machine-readable or CSV format is attached with the dataset).
As far as annualised rate of return is concerned, most of the time India stock market indices leading, followed by USA, Canada and Japan stock market indices.
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F37645bd90623ea79f3708a958013c098%2FAnnualized%20Return.JPG?generation=1688525901452892&alt=media" alt="">
The best performing index based on compound growth is Sensex (India) that comprises of top 30 companies is 15.60%, followed by Nifty500 (India) that is 11.34% and Nasdaq (USA) all is 10.60%.
The worst performing index is China top300, however this is launch in 2021 (post pandemic), so would not possible to examine at that stage (due to less data availability). Furthermore, UK and Russia indices are also top 5 in the worst order.
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F58ae33f60a8800749f802b46ec1e07e7%2FCAGR.JPG?generation=1688490409606631&alt=media" alt="">
Geography: Stock Market Index of the World Top Economies
Time period: Jan 01, 2003 – June 30, 2023
Variables: Stock Market Index Title, Open, High, Low, Close, Adj Close, Volume, Year, Month, Day, Yearly_Return and CAGR
File Type: CSV file
This is not a financial advice; due diligence is required in each investment decision.
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Economic Indicator Service gathers the major economic events from a variety of regions and countries around the globe and provides an Economic Events Data feed and Economic Calendar service to our clients. This service includes all previous historic data on economic indicators that are currently available on the database.
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Labour Force Survey summary data, including employment, unemployment and economic inactivity levels and rates, UK, rolling three-monthly figures published monthly, seasonally adjusted. These are official statistics in development.
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United States FRB Cleveland: 16 % Trimmed Mean Consumer Price Index (CPI): YoY data was reported at 2.206 % in Oct 2018. This records an increase from the previous number of 2.164 % for Sep 2018. United States FRB Cleveland: 16 % Trimmed Mean Consumer Price Index (CPI): YoY data is updated monthly, averaging 2.573 % from Dec 1983 (Median) to Oct 2018, with 419 observations. The data reached an all-time high of 5.094 % in Sep 1990 and a record low of 0.749 % in Oct 2010. United States FRB Cleveland: 16 % Trimmed Mean Consumer Price Index (CPI): YoY data remains active status in CEIC and is reported by Federal Reserve Bank of Cleveland. The data is categorized under Global Database’s United States – Table US.I040: Trimmed Mean CPI.
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United States Unemployment: Age 16 to 19 data was reported at 664.000 Person th in Oct 2018. This records a decrease from the previous number of 686.000 Person th for Sep 2018. United States Unemployment: Age 16 to 19 data is updated monthly, averaging 1,154.500 Person th from Jan 1948 (Median) to Oct 2018, with 850 observations. The data reached an all-time high of 2,527.000 Person th in Jun 1983 and a record low of 228.000 Person th in Oct 1951. United States Unemployment: Age 16 to 19 data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G016: Current Population Survey: Unemployment.
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Graph and download economic data for Employment Level - Persons At Work 1-34 Hours, Economic Reasons, All Industries (LNU02032194) from May 1955 to Sep 2025 about hours, 16 years +, household survey, employment, industry, and USA.
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Increase by 2 percentage points the share of total household income earned by low income South Australians.
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Economically Active Population Survey: Population 16 years of age and over by the relation with the economic activity, sex and age group. Annual. National.
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Economically Active Population Survey: Population 16 years of age and over by the relation with the economic activity, sex and province. Quarterly. Provinces.
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Living Conditions Survey (LCS): Persons, by income decile per consumption unit and relationship with economic activity (persons aged 16 years old and over). Annual. National.
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Context
The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Economy: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Income brackets:
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 median household income by age. You can refer the same here
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Economically Active Population Flows: Population aged 16 and over by Autonomous Community and relationship with economic activity in the current quarter according to their relationship with economic activity in the previous quarter. Quarterly. Autonomous Communities and Cities.
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Economically Active Population Survey: Population 16 years old and over in relation with the economic activity, sex and age group, by Autonomous Community. Quarterly. Autonomous Communities and Cities.
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🇪🇸 스페인 English Exploitation of microdata, tabulation, production and dissemination of data corresponding to Aragon of the EPA carried out by the INE.
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TwitterLoretta J. Mester-President and Chief Executive Officer-Federal Reserve Bank of Cleveland-The Global Interdependence Center Central Banking Series, Dublin, Ireland , May 16, 2023, 8:15 a.m. EDT
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kommun ra_det-fo_r-fra_mjande-av-kommunala-analyser-kolada region
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Disability, Independence and Dependency Situations Survey: Percentage of persons with disabilities, 16 to 64 years old, by province, relation with economic activity and sex. Province.
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The provided dataset is extracted from yahoo finance using pandas and yahoo finance library in python. This deals with stock market index of the world best economies. The code generated data from Jan 01, 2003 to Jun 30, 2023 that’s more than 20 years. There are 18 CSV files, dataset is generated for 16 different stock market indices comprising of 7 different countries. Below is the list of countries along with number of indices extracted through yahoo finance library, while two CSV files deals with annualized return and compound annual growth rate (CAGR) has been computed from the extracted data.
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F90ce8a986761636e3edbb49464b304d8%2FNumber%20of%20Index.JPG?generation=1688490342207096&alt=media" alt="">
This dataset is useful for research purposes, particularly for conducting comparative analyses involving capital market performance and could be used along with other economic indicators.
There are 18 distinct CSV files associated with this dataset. First 16 CSV files deals with number of indices and last two CSV file deals with annualized return of each year and CAGR of each index. If data in any column is blank, it portrays that index was launch in later years, for instance: Bse500 (India), this index launch in 2007, so earlier values are blank, similarly China_Top300 index launch in year 2021 so early fields are blank too.
The extraction process involves applying different criteria, like in 16 CSV files all columns are included, Adj Close is used to calculate annualized return. The algorithm extracts data based on index name (code given by the yahoo finance) according start and end date.
Annualized return and CAGR has been calculated and illustrated in below image along with machine readable file (CSV) attached to that.
To extract the data provided in the attachment, various criteria were applied:
Content Filtering: The data was filtered based on several attributes, including the index name, start and end date. This filtering process ensured that only relevant data meeting the specified criteria.
Collaborative Filtering: Another filtering technique used was collaborative filtering using yahoo finance, which relies on index similarity. This approach involves finding indices that are similar to other index or extended dataset scope to other countries or economies. By leveraging this method, the algorithm identifies and extracts data based on similarities between indices.
In the last two CSV files, one belongs to annualized return, that was calculated based on the Adj close column and new DataFrame created to store its outcome. Below is the image of annualized returns of all index (if unreadable, machine-readable or CSV format is attached with the dataset).
As far as annualised rate of return is concerned, most of the time India stock market indices leading, followed by USA, Canada and Japan stock market indices.
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F37645bd90623ea79f3708a958013c098%2FAnnualized%20Return.JPG?generation=1688525901452892&alt=media" alt="">
The best performing index based on compound growth is Sensex (India) that comprises of top 30 companies is 15.60%, followed by Nifty500 (India) that is 11.34% and Nasdaq (USA) all is 10.60%.
The worst performing index is China top300, however this is launch in 2021 (post pandemic), so would not possible to examine at that stage (due to less data availability). Furthermore, UK and Russia indices are also top 5 in the worst order.
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F58ae33f60a8800749f802b46ec1e07e7%2FCAGR.JPG?generation=1688490409606631&alt=media" alt="">
Geography: Stock Market Index of the World Top Economies
Time period: Jan 01, 2003 – June 30, 2023
Variables: Stock Market Index Title, Open, High, Low, Close, Adj Close, Volume, Year, Month, Day, Yearly_Return and CAGR
File Type: CSV file
This is not a financial advice; due diligence is required in each investment decision.