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

The Gross Domestic Product (GDP) in China expanded 5.20 percent in the second quarter of 2025 over the same quarter of the previous year. This dataset provides - China GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains replication files for "The Fading American Dream: Trends in Absolute Income Mobility Since 1940" by Raj Chetty, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. For more information, see https://opportunityinsights.org/paper/the-fading-american-dream/. A summary of the related publication follows. One of the defining features of the “American Dream” is the ideal that children have a higher standard of living than their parents. We assess whether the U.S. is living up to this ideal by estimating rates of “absolute income mobility” – the fraction of children who earn more than their parents – since 1940. We measure absolute mobility by comparing children’s household incomes at age 30 (adjusted for inflation using the Consumer Price Index) with their parents’ household incomes at age 30. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Absolute income mobility has fallen across the entire income distribution, with the largest declines for families in the middle class. These findings are unaffected by using alternative price indices to adjust for inflation, accounting for taxes and transfers, measuring income at later ages, and adjusting for changes in household size. Absolute mobility fell in all 50 states, although the rate of decline varied, with the largest declines concentrated in states in the industrial Midwest, such as Michigan and Illinois. The decline in absolute mobility is especially steep – from 95% for children born in 1940 to 41% for children born in 1984 – when we compare the sons’ earnings to their fathers’ earnings. Why have rates of upward income mobility fallen so sharply over the past half-century? There have been two important trends that have affected the incomes of children born in the 1980s relative to those born in the 1940s and 1950s: lower Gross Domestic Product (GDP) growth rates and greater inequality in the distribution of growth. We find that most of the decline in absolute mobility is driven by the more unequal distribution of economic growth rather than the slowdown in aggregate growth rates. When we simulate an economy that restores GDP growth to the levels experienced in the 1940s and 1950s but distributes that growth across income groups as it is distributed today, absolute mobility only increases to 62%. In contrast, maintaining GDP at its current level but distributing it more broadly across income groups – at it was distributed for children born in the 1940s – would increase absolute mobility to 80%, thereby reversing more than two-thirds of the decline in absolute mobility. These findings show that higher growth rates alone are insufficient to restore absolute mobility to the levels experienced in mid-century America. Under the current distribution of GDP, we would need real GDP growth rates above 6% per year to return to rates of absolute mobility in the 1940s. Intuitively, because a large fraction of GDP goes to a small fraction of high-income households today, higher GDP growth does not substantially increase the number of children who earn more than their parents. Of course, this does not mean that GDP growth does not matter: changing the distribution of growth naturally has smaller effects on absolute mobility when there is very little growth to be distributed. The key point is that increasing absolute mobility substantially would require more broad-based economic growth. We conclude that absolute mobility has declined sharply in America over the past half-century primarily because of the growth in inequality. If one wants to revive the “American Dream” of high rates of absolute mobility, one must have an interest in growth that is shared more broadly across the income distribution.

Italy recorded a Government Debt to GDP of 135.30 percent of the country's Gross Domestic Product in 2024. This dataset provides - Italy Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Analysis of ‘Winter Olympics Prediction - Fantasy Draft Picks’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ericsbrown/winter-olympics-prediction-fantasy-draft-picks on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Olympic Draft Predictive Model

Our family runs an Olympic Draft - similar to fantasy football or baseball - for each Olympic cycle. The purpose of this case study is to identify trends in medal count / point value to create a predictive analysis of which teams should be selected in which order.

There are a few assumptions that will impact the final analysis: Point Value - Each medal is worth the following: Gold - 6 points Silver - 4 points Bronze - 3 points For analysis reviewing the last 10 Olympic cycles. Winter Olympics only.

All GDP numbers are in USD

My initial hypothesis is that larger GDP per capita and size of contingency are correlated with better points values for the Olympic draft.

All Data pulled from the following Datasets:

Winter Olympics Medal Count - https://www.kaggle.com/ramontanoeiro/winter-olympic-medals-1924-2018 Worldwide GDP History - https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?end=2020&start=1984&view=chart

GDP data was a wide format when downloaded from the World Bank. Opened file in Excel, removed irrelevant years, and saved as .csv.

Process

In RStudio utilized the following code to convert wide data to long:

install.packages("tidyverse") library(tidyverse) library(tidyr)

Converting to long data from wide

long <- newgdpdata %>% gather(year, value, -c("Country Name","Country Code"))

Completed these same steps for GDP per capita.

Primary Key Creation

Differing types of data between these two databases and there is not a good primary key to utilize. Used CONCAT to create a new key column in both combining the year and country code to create a unique identifier that matches between the datasets.

SELECT *, CONCAT(year,country_code) AS "Primary" FROM medal_count

Saved as new table "medals_w_primary"

Utilized Excel to concatenate the primary key for GDP and GDP per capita utilizing:

=CONCAT()

Saved as new csv files.

Uploaded all to SSMS.

Contingent Size

Next need to add contingent size.

No existing database had this information. Pulled data from Wikipedia.

2018 - No problem, pulled existing table. 2014 - Table was not created. Pulled information into excel, needed to convert the country NAMES into the country CODES.

Created excel document with all ISO Country Codes. Items were broken down between both formats, either 2 or 3 letters. Example:

AF/AFG

Used =RIGHT(C1,3) to extract only the country codes.

For the country participants list in 2014, copied source data from Wikipedia and pasted as plain text (not HTML).

Items then showed as: Albania (2)

Broke cells using "(" as the delimiter to separate country names and numbers, then find and replace to remove all parenthesis from this data.

We were left with: Albania 2

Used VLOOKUP to create correct country code: =VLOOKUP(A1,'Country Codes'!A:D,4,FALSE)

This worked for almost all items with a few exceptions that didn't match. Based on nature and size of items, manually checked on which items were incorrect.

Chinese Taipei 3 #N/A Great Britain 56 #N/A Virgin Islands 1 #N/A

This was relatively easy to fix by adding corresponding line items to the Country Codes sheet to account for future variability in the country code names.

Copied over to main sheet.

Repeated this process for additional years.

Once complete created sheet with all 10 cycles of data. In total there are 731 items.

Data Cleaning

Filtered by Country Code since this was an issue early on.

Found a number of N/A Country Codes:

Serbia and Montenegro FR Yugoslavia FR Yugoslavia Czechoslovakia Unified Team Yugoslavia Czechoslovakia East Germany West Germany Soviet Union Yugoslavia Czechoslovakia East Germany West Germany Soviet Union Yugoslavia

Appears to be issues with older codes, Soviet Union block countries especially. Referred to historical data and filled in these country codes manually. Codes found on iso.org.

Filled all in, one issue that was more difficult is the Unified Team of 1992 and Soviet Union. For simplicity used code for Russia - GDP data does not recognize the Soviet Union, breaks the union down to constituent countries. Using Russia is a reasonable figure for approximations and analysis to attempt to find trends.

From here created a filter and scanned through the country names to ensure there were no obvious outliers. Found the following:

Olympic Athletes from Russia[b] -- This is a one-off due to the recent PED controversy for Russia. Amended the Country Code to RUS to more accurately reflect the trends.

Korea[a] and South Korea -- both were listed in 2018. This is due to the unified Korean team that competed. This is an outlier and does not warrant standing on its own as the 2022 Olympics will not have this team (as of this writing on 01/14/2022). Removed the COR country code item.

Confirmed Primary Key was created for all entries.

Ran minimum and maximum years, no unexpected values. Ran minimum and maximum Athlete numbers, no unexpected values. Confirmed length of columns for Country Code and Primary Key.

No NULL values in any columns. Ready to import to SSMS.

SQL work

We now have 4 tables, joined together to create the master table:

SELECT [OlympicDraft].[dbo].[medals_w_primary].[year], host_country, host_city, [OlympicDraft].[dbo].[medals_w_primary].[country_name], [OlympicDraft].[dbo].[medals_w_primary].[country_code], Gold, Silver, Bronze, [OlympicDraft].[dbo].[gdp_w_primary].[value] AS GDP, [OlympicDraft].[dbo].[convertedgdpdatapercapita].[gdp_per_capita], Atheletes FROM medals_w_primary INNER JOIN gdp_w_primary ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[gdp_w_primary].[year_country] INNER JOIN contingency_cleaned ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[contingency_cleaned].[Year_Country] INNER JOIN convertedgdpdatapercapita ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[convertedgdpdatapercapita].[Year_Country] ORDER BY year DESC

This left us with the following table:

https://i.imgur.com/tpNhiNs.png" alt="Imgur">

Performed some basic cleaning tasks to ensure no outliers:

Checked GDP numbers: 1992 North Korea shows as null. Updated this row with information from countryeconomy.com - $12,458,000,000

Checked GDP per capita:

1992 North Korea again missing. Updated this to $595, utilized same source.

UPDATE [OlympicDraft].[dbo].[gdp_w_primary] SET [OlympicDraft].[dbo].[gdp_w_primary].[value] = 12458000000 WHERE [OlympicDraft].[dbo].[gdp_w_primary].[year_country] = '1992PRK'

UPDATE [OlympicDraft].[dbo].[convertedgdpdatapercapita] SET [OlympicDraft].[dbo].[convertedgdpdatapercapita].[gdp_per_capita] = 595 WHERE [OlympicDraft].[dbo].[convertedgdpdatapercapita].[year_country] = '1992PRK'

Liechtenstein showed as an outlier with GDP per capita at 180,366 in 2018. Confirmed this number is correct per the World Bank, appears Liechtenstein does not often have atheletes in the winter olympics. Performing a quick SQL search to verify this shows that they fielded 3 atheletes in 2018, with a Bronze medal being won. Initially this appears to be a good ratio for win/loss.

Finally, need to create a column that shows the total point value for each of these rows based on the above formula (6 points for Gold, 4 points for Silver, 3 points for Bronze).

Updated query as follows:

SELECT [OlympicDraft].[dbo].[medals_w_primary].[year], host_country, host_city, [OlympicDraft].[dbo].[medals_w_primary].[country_name], [OlympicDraft].[dbo].[medals_w_primary].[country_code], Gold, Silver, Bronze, [OlympicDraft].[dbo].[gdp_w_primary].[value] AS GDP, [OlympicDraft].[dbo].[convertedgdpdatapercapita].[gdp_per_capita], Atheletes, (Gold*6) + (Silver*4) + (Bronze*3) AS 'Total_Points' FROM [OlympicDraft].[dbo].[medals_w_primary] INNER JOIN gdp_w_primary ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[gdp_w_primary].[year_country] INNER JOIN contingency_cleaned ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[contingency_cleaned].[Year_Country] INNER JOIN convertedgdpdatapercapita ON [OlympicDraft].[dbo].[medals_w_primary].[primary] = [OlympicDraft].[dbo].[convertedgdpdatapercapita].[Year_Country] ORDER BY [OlympicDraft].[dbo].[convertedgdpdatapercapita].[year]

Spot checked, calculating correctly.

Saved result as winter_olympics_study.csv.

We can now see that all relevant information is in this table:

https://i.imgur.com/ceZvqCA.png" alt="Imgur">

RStudio Work

To continue our analysis, opened this CSV in RStudio.

install.packages("tidyverse") library(tidyverse) library(ggplot2) install.packages("forecast") library(forecast) install.packages("GGally") library(GGally) install.packages("modelr") library(modelr)

View(winter_olympic_study)

Finding correlation between gdp_per_capita and Total_Points

ggplot(data = winter_olympic_study) + geom_point(aes(x=gdp_per_capita,y=Total_Points,color=country_name)) + facet_wrap(~country_name)

cor(winter_olympic_study$gdp_per_capita, winter_olympic_study$Total_Points, method = c("pearson"))

Result is .347, showing a moderate correlation between these two figures.

Looked next at GDP vs. Total_Points ggplot(data = winter_olympic_study) + geom_point(aes(x=GDP,y=Total_Points,color=country_name))+ facet_wrap(~country_name)

cor(winter_olympic_study$GDP, winter_olympic_study$Total_Points, method = c("pearson")) This resulted in 0.35, statistically insignificant difference between this and GDP Per Capita

Next looked at contingent size vs. total points ggplot(data = winter_olympic_study) + geom_point(aes(x=Atheletes,y=Total_Points,color=country_name)) +

Government spending in the United States was last recorded at 39.7 percent of GDP in 2024 . This dataset provides - United States Government Spending To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in Russia was worth 2173.84 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Russia represents 2.05 percent of the world economy. This dataset provides the latest reported value for - Russia GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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

Olympic Draft Predictive Model

Our family runs an Olympic Draft - similar to fantasy football or baseball - for each Olympic cycle. The purpose of this case study is to identify trends in medal count / point value to create a predictive analysis of which teams should be selected in which order.

There are a few assumptions that will impact the final analysis: Point Value - Each medal is worth the following: Gold - 6 points Silver - 4 points Bronze - 3 points For analysis reviewing the last 10 Olympic cycles. Winter Olympics only.

All GDP numbers are in USD

My initial hypothesis is that larger GDP per capita and size of contingency are correlated with better points values for the Olympic draft.

All Data pulled from the following Datasets:

Winter Olympics Medal Count - https://www.kaggle.com/ramontanoeiro/winter-olympic-medals-1924-2018 Worldwide GDP History - https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?end=2020&start=1984&view=chart

GDP data was a wide format when downloaded from the World Bank. Opened file in Excel, removed irrelevant years, and saved as .csv.

Process

In RStudio utilized the following code to convert wide data to long:

install.packages("tidyverse") library(tidyverse) library(tidyr)

Converting to long data from wide

long <- newgdpdata %>% gather(year, value, -c("Country Name","Country Code"))

Completed these same steps for GDP per capita.

Primary Key Creation

Differing types of data between these two databases and there is not a good primary key to utilize. Used CONCAT to create a new key column in both combining the year and country code to create a unique identifier that matches between the datasets.

SELECT *, CONCAT(year,country_code) AS "Primary" FROM medal_count

Saved as new table "medals_w_primary"

Utilized Excel to concatenate the primary key for GDP and GDP per capita utilizing:

=CONCAT()

Saved as new csv files.

Uploaded all to SSMS.

Contingent Size

Next need to add contingent size.

No existing database had this information. Pulled data from Wikipedia.

2018 - No problem, pulled existing table. 2014 - Table was not created. Pulled information into excel, needed to convert the country NAMES into the country CODES.

Created excel document with all ISO Country Codes. Items were broken down between both formats, either 2 or 3 letters. Example:

AF/AFG

Used =RIGHT(C1,3) to extract only the country codes.

For the country participants list in 2014, copied source data from Wikipedia and pasted as plain text (not HTML).

Items then showed as: Albania (2)

Broke cells using "(" as the delimiter to separate country names and numbers, then find and replace to remove all parenthesis from this data.

We were left with: Albania 2

Used VLOOKUP to create correct country code: =VLOOKUP(A1,'Country Codes'!A:D,4,FALSE)

This worked for almost all items with a few exceptions that didn't match. Based on nature and size of items, manually checked on which items were incorrect.

Chinese Taipei 3 #N/A Great Britain 56 #N/A Virgin Islands 1 #N/A

This was relatively easy to fix by adding corresponding line items to the Country Codes sheet to account for future variability in the country code names.

Copied over to main sheet.

Repeated this process for additional years.

Once complete created sheet with all 10 cycles of data. In total there are 731 items.

Data Cleaning

Filtered by Country Code since this was an issue early on.

Found a number of N/A Country Codes:

Serbia and Montenegro FR Yugoslavia FR Yugoslavia Czechoslovakia Unified Team Yugoslavia Czechoslovakia East Germany West Germany Soviet Union Yugoslavia Czechoslovakia East Germany West Germany Soviet Union Yugoslavia

Appears to be issues with older codes, Soviet Union block countries especially. Referred to historical data and filled in these country codes manually. Codes found on iso.org.

Filled all in, one issue that was more difficult is the Unified Team of 1992 and Soviet Union. For simplicity used code for Russia - GDP data does not recognize the Soviet Union, breaks the union down to constituent countries. Using Russia is a reasonable figure for approximations and analysis to attempt to find trends.

From here created a filter and scanned through the country names to ensure there were no obvious outliers. Found the following:

Olympic Athletes from Russia[b] -- This is a one-off due to the recent PED controversy for Russia. Amended the Country Code to RUS to more accurately reflect the trends.

Korea[a] and South Korea -- both were listed in 2018. This is due to the unified Korean team that competed. This is an outlier and does not warrant standing on its own as the 2022 Olympics will not have this team (as of this writing on 01/14/2022). Removed the COR country code item.

Confirmed Primary Key was created for all entries.

Ran minimum and maximum years, no...

The Gross Domestic Product (GDP) in Poland was worth 914.70 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Poland represents 0.86 percent of the world economy. This dataset provides the latest reported value for - Poland GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Gross Domestic Product (GDP) In the Euro Area expanded 0.10 percent in the second quarter of 2025 over the previous quarter. This dataset provides - Euro Area GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info

The tourism sector GDP share in Tunisia was forecast to continuously increase between 2023 and 2028 by in total 4.8 percentage points. The share is estimated to amount to 16.8 percent in 2028. While the share was forecast to increase significant in the next years, the increase will slow down in the future.Depited is the economic contribution of the tourism sector in relation to the gross domestic product of the country or region at hand.The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the tourism sector GDP share in countries like Morocco and the Sudan.

The Gross Domestic Product (GDP) in Australia was worth 1752.19 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Australia represents 1.65 percent of the world economy. This dataset provides - Australia GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in Pakistan was worth 373.07 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Pakistan represents 0.35 percent of the world economy. This dataset provides - Pakistan GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Jun 2025 about savings, personal, rate, and USA.

Context

The World Happiness Report may be a point of interest survey of the state of worldwide bliss. The primary report was distributed in 2012, the second in 2013, the third in 2015, and the fourth within the 2016 Upgrade. The World Joy 2017, which positions 155 nations by their bliss levels, was discharged at the Joined together Countries at an occasion celebrating Universal Day of Joy on Walk 20th. The report proceeds to pick up worldwide acknowledgment as governments, organizations and respectful society progressively utilize joy pointers to educate their policy-making choices. Driving specialists over areas – financial matters, brain research, overview investigation, national insights, wellbeing, open approach and more – depict how estimations of well-being can be used effectively to evaluate the advance of countries. The reports survey the state of bliss within the world nowadays and appear how the modern science of bliss clarifies individual and national varieties in bliss.

Content

The joy scores and rankings utilize information from the Gallup World Survey. The scores are based on answers to the most life evaluation address inquired within the survey. This address, known as the Cantril step, asks respondents to think of a step with the most excellent conceivable life for them being a 10 and the most exceedingly bad conceivable life being a and to rate their claim current lives on that scale. The scores are from broadly agent tests for the a long time 2013-2016 and utilize the Gallup weights to create the gauges agent. The columns taking after the bliss score assess the degree to which each of six variables – financial generation, social back, life anticipation, flexibility, nonattendance of debasement, and liberality – contribute to making life assessments higher in each nation than they are in Dystopia, a theoretical nation that has values rise to to the world’s least national midpoints for each of the six variables. They have no affect on the full score detailed for each nation, but they do exp

This file contains the Happiness Score for 153 countries along with the factors used to explain the score.

The Happiness Score is a national average of the responses to the main life evaluation question asked in the Gallup World Poll (GWP), which uses the Cantril Ladder.

The Happiness Score is explained by the following factors:

GDP per capita Healthy Life Expectancy Social support Freedom to make life choices Generosity Corruption Perception Residual error The data is described in much more detail here: link

Acknowledgements

I did not create this data, only sourced it. The credit goes to the original Authors:

Editors: John Helliwell, Richard Layard, Jeffrey D. Sachs, and Jan Emmanuel De Neve, Co-Editors; Lara Aknin, Haifang Huang and Shun Wang, Associate Editors; and Sharon Paculor, Production Editor

Citation: Helliwell, John F., Richard Layard, Jeffrey Sachs, and Jan-Emmanuel De Neve, eds. 2020. World Happiness Report 2020. New York: Sustainable Development Solutions Network

The Gross Domestic Product (GDP) in India was worth 3912.69 billion US dollars in 2024, according to official data from the World Bank. The GDP value of India represents 3.69 percent of the world economy. This dataset provides the latest reported value for - India GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Greece recorded a Government Debt to GDP of 153.60 percent of the country's Gross Domestic Product in 2024. This dataset provides the latest reported value for - Greece Government Debt to GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.