Annual implicit price indexes and contributions to percent change in implicit price indexes for expenditure-based gross domestic product, by province and territory, 2017=100.

The dataset represents the joint dynamics of Financial Stress Index (FSI), Consumer Price Index (CPI) calculated and provided by the National Bank of Ukraine (NBU) and Gross Domestic Product (GDP) provided by SSSU for Ukraine.

The monthly dataset range is Feb 2004-Feb 2022, the effective balanced range is Jan 2011-Dec 2021.

The daily FSI data is aggregated into monthly series as a period average. The CPI series are monthly. The quarterly GDP data is seasonally adjusted and interpolated into monthly data with the use of ARIMA model and cubic spline method accordingly, converted into year-over-year series (dGDP).

Gross Domestic Product (GDP) is one of the best known indicators of economic activity and is widely used to monitor economic performance. GDP statistics for Scotland are produced by the Scottish Government and have been designated as National Statistics. This dataset contains statistics for the output approach to GDP and growth in real terms, and includes results for the whole economy (Total GDP) and industry sectors. GDP can also be broken down using the income and expenditure approaches, which are available as separate datasets. There are two updates to the output by industry statistics each quarter. The First Estimate of GDP growth is published around 80 days after the quarter’s end, and an updated second estimate is published in the Quarterly National Accounts around 120 days after the quarter’s end. The First Estimate of GDP statistics will be published on this website as open data; the Second Estimate will not currently be available as open data, but will be available on the Scottish Government website. Results for previous periods are also open to revision each quarter. Further details on Scottish GDP statistics, including methodology notes and the revisions policy, are available. The Industry Sector dimension in this dataset contains the broad industry sectors used on GDP statistics for Scotland the UK. These are based on industry sections from the Standard Industrial Classification (SIC, 2007). Further information can be found here The Measure Type dimension in this dataset contains four GDP measures, detailed below. The index measure is rounded to 4 decimal places and the growth rate measures are rounded to 1 decimal place. It is not always possible to replicate the published growth rates using rounded data, but all results are also available unrounded in the downloadable spreadsheets from the latest publication. • 4Q-on-4Q is the percentage change (growth rate) for the latest four quarters compared to the previous four non-overlapping quarters. This rolling annual growth rate gives a smoothed measure of recent trends. This growth rate is calculated from the Index measure. • Index represents the level of output in real, or volume, terms for each industry or total GDP, relative to the base year (2019). An index value of more than 100 means that output is higher than in the base year, and a value of less than 100 means that output is lower than in the base year. • q-on-q is the percentage change (growth rate) for the latest quarter compared to the previous quarter. This quarterly growth rate is usually taken as the headline measure of GDP growth. This growth rate is calculated from the Index measure. • q-on-q year ago is the percentage change (growth rate) for the latest quarter compared to the same quarter in the previous year. This growth rate over the year is usually compared to other statistics such as earnings or price inflation. This growth rate is calculated from the Index measure. The Reference Period dimension relates to standard calendar quarters. Quarter 1 refers to the period from January to March, Quarter 2 refers to April to June, Quarter 3 refers to July to September, and Quarter 4 refers to October to December. The Reference Area dimension for this dataset only contains results for Scotland, with no breakdowns to other areas. In this dataset, all results relate to Scotland’s onshore economy and do not include the output of offshore oil and gas extraction in Scottish Adjacent Waters. Each industry sector is indexed to make them comparable. For each sector, the value during 2019 is taken as the base year, and given the value of 100. All indexed values are chainlinked volume measures, and given relative to the base year.

The Gross Domestic Product (GDP) in Vietnam was worth 476.39 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Vietnam represents 0.45 percent of the world economy. This dataset provides the latest reported value for - Vietnam GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.

EnhancedHousingMarketData.csv is an auxiliary dataset for the "Housing Prices" competition, containing key economic and demographic indicators vital for real estate market analysis. It includes data on non-farm employment, housing price index, per capita income, total quarterly wages, quantitative indexes of real GDP, total GDP, real GDP, stable population, employed individuals, and the average weekly wage in the private sector, along with the unemployment rate. This dataset aids in better understanding the factors influencing housing prices and allows for a more in-depth analysis of the real estate market.

"**TotalNonfarmEmployees**" - reflects the total number of employees working outside the agricultural sector. This figure includes workers in industries such as manufacturing, construction, trade, transportation, education, healthcare, and other non-agricultural sectors, making it a key indicator of economic activity and employment in the region.

"**HousingPriceIndex**" - represents a housing price index, reflecting changes in real estate prices in a specific region for a given month. This index can be used to analyze trends in the real estate market and assess the overall economic conditions.

"**AnnualPerCapitaIncome**" - represents the annual per capita income, measured yearly. This indicator reflects the average income per resident in a specific region over a year, serving as an important measure of the population's economic well-being.

"**QuarterlyTotalWages**" - represents the total quarterly wages, measured in dollars and adjusted for seasonal variations. This metric reflects the sum of wages paid by employers insured for unemployment insurance over a calendar quarter. It includes components such as vacation pay, bonuses, and tips.

"**TotalRealGDPChainIndex**" - represents the total annual quantitative index of real GDP, encompassing data from all private sectors and the government. It is based on the Fisher chain-weighted method, tracking changes in production volume or expenditures while eliminating the effects of price changes. This index is useful for comparing the volumes of production or expenditures across different time periods.

"**TotalGDP**" - describes the total Gross Domestic Product (GDP), measured in millions of dollars and calculated annually without seasonal adjustments. This metric encompasses all private sectors and the government, reflecting the market value of all final goods and services produced within an agglomeration. The agglomeration GDP represents the gross output minus intermediate costs, serving as a key indicator of economic activity and production volume.

"**TotalRealGDP**" - represents the total real Gross Domestic Product, measured in millions of chained 2012 dollars and calculated annually without seasonal adjustments. This metric includes data from all private sectors and the government. The real GDP for agglomerations is a measure of the gross product of each agglomeration, adjusted for inflation, and based on national prices for goods and services produced in the agglomeration.

"**StablePopulation**" - reflects the stable population, measured in thousands of people and calculated annually without seasonal adjustments. This metric represents population estimates as of July 1st each year, providing reliable data for analyzing demographic trends and planning purposes.

"**EmployedIndividuals**" - represents the number of employed individuals, measured in persons without seasonal adjustment and updated monthly. The data are derived from the Current Population Survey (CPS). Employed individuals include those who did any paid work, owned a business or farm, worked 15 hours or more as unpaid workers in a family business, or were temporarily absent from their job for various reasons. This metric is important for analyzing employment levels and the economic activity of the population.

"**AverageWeeklyWagePrivate**" - denotes the average weekly wage of private enterprise employees, measured in dollars per week and calculated quarterly without seasonal adjustment. It includes payments made by employers insured against unemployment over the quarter, encompassing vacation pay, bonuses, stock options, tips, and other components. This metric is important for assessing the level of wages in the private sector.

"**UnemploymentRate**" - represents the unemployment rate, measured in percentages and calculated monthly without seasonal adjustments. This metric indicates the proportion of the unemployed within the total labor force, providing key information about the labor market's condition and the population's economic activity.

This dataset provides statistics on real gross domestic product (GDP) and real GDP per capita for subnational regions. Real values are deflation-adjusted using the Regional Producer Price Index (ROPI), where available.

Data source and definition

Regional gross domestic product data is collected at current prices, in millions of national currency from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites.

To allow comparability over time and between countries, data at current prices are transformed into constant prices and purchasing power parity measures. Regional GDP per capita is calculated by dividing regional GDP by the average annual population of the region.

See method and detailed data sources in Regions and Cities at a Glance 2024, Annex.

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting. Correspondence between TL3 and metropolitan regions:(xlsx).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

An index that can be used to gauge broad financial conditions and assess how these conditions are related to future economic growth. The index is broadly consistent with how the FRB/US model generally relates key financial variables to economic activity. The index aggregates changes in seven financial variables: the federal funds rate, the 10-year Treasury yield, the 30-year fixed mortgage rate, the triple-B corporate bond yield, the Dow Jones total stock market index, the Zillow house price index, and the nominal broad dollar index using weights implied by the FRB/US model and other models in use at the Federal Reserve Board. These models relate households' spending and businesses' investment decisions to changes in short- and long-term interest rates, house and equity prices, and the exchange value of the dollar, among other factors. These financial variables are weighted using impulse response coefficients (dynamic multipliers) that quantify the cumulative effects of unanticipated permanent changes in each financial variable on real gross domestic product (GDP) growth over the subsequent year. The resulting index is named Financial Conditions Impulse on Growth (FCI-G). One appealing feature of the FCI-G is that its movements can be used to measure whether financial conditions have tightened or loosened, to summarize how changes in financial conditions are associated with real GDP growth over the following year, or both.

Data from 1st of June 2022. For most recent GDP data, consult dataset nama_10_gdp. Gross domestic product (GDP) is a measure for the economic activity. It is defined as the value of all goods and services produced less the value of any goods or services used in their creation. The volume index of GDP per capita in Purchasing Power Standards (PPS) is expressed in relation to the European Union average set to equal 100. If the index of a country is higher than 100, this country's level of GDP per head is higher than the EU average and vice versa. Basic figures are expressed in PPS, i.e. a common currency that eliminates the differences in price levels between countries allowing meaningful volume comparisons of GDP between countries. Please note that the index, calculated from PPS figures and expressed with respect to EU27_2020 = 100, is intended for cross-country comparisons rather than for temporal comparisons."

Explore the Index of Real Gross Domestic Product data by kind of economic activity at constant prices for Saudi Arabia. Find information on Mining & Quarrying, Manufacturing, Construction, Agriculture, Finance, Insurance, Real Estate, and more in this quarterly dataset.Follow data.kapsarc.org for timely data to advance energy economics research.Important notes:2022,2023,2024: Preliminary Data.The methodology of chain-linking represents a non-additive model, thus the subcomponents do not correspond to the aggregates.Data were revised from 1970 to 2009

Annual and quarterly data for UK gross domestic product (GDP) estimates, in chained volume measures and current market prices.

Indicator: 8.1.1The annual growth rate of real GDP per capita.The equation used to calculate the results is:The annual growth rate of real Gross Domestic Product (GDP) per capita is calculated as follows:a. Convert annual real GDP in domestic currency at 2010 prices for a country or area to US dollars at 2010 prices using the 2010 exchange rates.b. Divide the result by the population of the country or area to obtain annual real GDP per capita in constant US dollars at 2010 prices.c. Calculate the annual growth rate of real GDP per capita in year t+1 using the following formula: [(G(t+1) – G(t))/G(t)] x 100, where G(t+1) is real GDP per capita in 2010 US dollars in year t+1 and G(t) is real GDP per capita in 2010 US dollars in year t.Note : GDP Constant (2010 = 100)*Data Source:Planning & Statistics Authority, National Accounts Bulletin

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.

Chile CL: PPP Conversion Factor: to Market Exchange Rate: Price Level Ratio data was reported at 0.520 Ratio in 2023. This records an increase from the previous number of 0.500 Ratio for 2022. Chile CL: PPP Conversion Factor: to Market Exchange Rate: Price Level Ratio data is updated yearly, averaging 0.599 Ratio from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 0.720 Ratio in 2011 and a record low of 0.437 Ratio in 2002. Chile CL: PPP Conversion Factor: to Market Exchange Rate: Price Level Ratio data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chile – Table CL.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. Price level ratio is the ratio of a purchasing power parity (PPP) conversion factor to an exchange rate. It provides a measure of the differences in price levels between countries by indicating the number of units of the common currency needed to buy the same volume of the aggregation level in each country. At the level of GDP, they provide a measure of the differences in the general price levels of countries.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;;

Costa Rica CR: PPP Conversion Factor: to Market Exchange Rate: Price Level Ratio data was reported at 0.603 Ratio in 2023. This records an increase from the previous number of 0.526 Ratio for 2022. Costa Rica CR: PPP Conversion Factor: to Market Exchange Rate: Price Level Ratio data is updated yearly, averaging 0.511 Ratio from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 0.709 Ratio in 2013 and a record low of 0.364 Ratio in 1990. Costa Rica CR: PPP Conversion Factor: to Market Exchange Rate: Price Level Ratio data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. Price level ratio is the ratio of a purchasing power parity (PPP) conversion factor to an exchange rate. It provides a measure of the differences in price levels between countries by indicating the number of units of the common currency needed to buy the same volume of the aggregation level in each country. At the level of GDP, they provide a measure of the differences in the general price levels of countries.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;;

Recently, the economy in Guangdong province has ranked first in the country, maintaining a good growth momentum. The prediction of Gross Domestic Product (GDP) for Guangdong province is an important issue. Through predicting the GDP, it is possible to analyze whether the economy in Guangdong province can maintain high-quality growth. Hence, to accurately forecast the economy in Guangdong, this paper proposed an Elman neural network combining with wavelet function. The wavelet function not only stimulates the forecast ability of Elman neural network, but also improves the convergence speed of Elman neural network. Experimental results indicate that our model has good forecast ability of regional economy, and the forecast accuracy reach 0.971. In terms of forecast precision and errors, our model defeats the competitors. Moreover, our model gains advanced forecast results to both individual economic indicator and multiple economic indicators. This means that our model is independently of specific scenarios in regional economic forecast. We also find that the investment in education has a major positive impact on regional economic development in Guangdong province, and the both surges positive correlation. Experimental results also show that our model does not exhibit exponential training time with the augmenting of data volume. Consequently, we propose that our model is suitable for the prediction of large-scale datasets. Additionally, we demonstrate that using wavelet function gains more profits than using complex network architectures in forecast accuracy and training cost. Moreover, using wavelet function can simplify the designs of complexity network architectures, reducing the training parameter of neural networks.

Comoros KM: PPP Conversion Factor: to Market Exchange Rate: Price Level Ratio data was reported at 0.410 Ratio in 2023. This records an increase from the previous number of 0.401 Ratio for 2022. Comoros KM: PPP Conversion Factor: to Market Exchange Rate: Price Level Ratio data is updated yearly, averaging 0.479 Ratio from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 0.643 Ratio in 2008 and a record low of 0.360 Ratio in 2000. Comoros KM: PPP Conversion Factor: to Market Exchange Rate: Price Level Ratio data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Comoros – Table KM.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. Price level ratio is the ratio of a purchasing power parity (PPP) conversion factor to an exchange rate. It provides a measure of the differences in price levels between countries by indicating the number of units of the common currency needed to buy the same volume of the aggregation level in each country. At the level of GDP, they provide a measure of the differences in the general price levels of countries.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;;