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Inflation Rate in Indonesia decreased to 1.60 percent in May from 1.95 percent in April of 2025. This dataset provides - Indonesia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.
Inflation is generally defined as the continued increase in the average prices of goods and services in a given region. Following the extremely high global inflation experienced in the 1980s and 1990s, global inflation has been relatively stable since the turn of the millennium, usually hovering between three and five percent per year. There was a sharp increase in 2008 due to the global financial crisis now known as the Great Recession, but inflation was fairly stable throughout the 2010s, before the current inflation crisis began in 2021. Recent years Despite the economic impact of the coronavirus pandemic, the global inflation rate fell to 3.26 percent in the pandemic's first year, before rising to 4.66 percent in 2021. This increase came as the impact of supply chain delays began to take more of an effect on consumer prices, before the Russia-Ukraine war exacerbated this further. A series of compounding issues such as rising energy and food prices, fiscal instability in the wake of the pandemic, and consumer insecurity have created a new global recession, and global inflation in 2024 is estimated to have reached 5.76 percent. This is the highest annual increase in inflation since 1996. Venezuela Venezuela is the country with the highest individual inflation rate in the world, forecast at around 200 percent in 2022. While this is figure is over 100 times larger than the global average in most years, it actually marks a decrease in Venezuela's inflation rate, which had peaked at over 65,000 percent in 2018. Between 2016 and 2021, Venezuela experienced hyperinflation due to the government's excessive spending and printing of money in an attempt to curve its already-high inflation rate, and the wave of migrants that left the country resulted in one of the largest refugee crises in recent years. In addition to its economic problems, political instability and foreign sanctions pose further long-term problems for Venezuela. While hyperinflation may be coming to an end, it remains to be seen how much of an impact this will have on the economy, how living standards will change, and how many refugees may return in the coming years.
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Inflation Rate In the Euro Area decreased to 1.90 percent in May from 2.20 percent in April of 2025. This dataset provides the latest reported value for - Euro Area Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
A series for the GDP deflator in index form is produced by the Treasury from data provided by the Office for National Statistics (ONS) and the Office for Budget Responsibility (OBR). The GDP deflator set is updated after every ONS Quarterly National Accounts release (at the end of each quarter) and whenever the OBR updates its GDP deflator forecasts (usually twice a year).
Outturn data are the latest Quarterly National Accounts figures from the ONS, 20 December 2013. GDP deflators from 1955-56 to 2012-13 (1955 to 2012) have been taken directly from ONS Quarterly National Accounts implied deflator at market prices series http://www.ons.gov.uk/ons/datasets-and-tables/data-selector.html?cdid=L8GG&dataset=qna&table-id=N" class="govuk-link">L8GG.
Forecast data are consistent with the Autumn Statement, 05 December 2013.
The detail below aims to provide background information on the GDP deflator series and the concepts and methods underlying it.
GDP deflators can be used by anyone who has an interest in deflating current price nominal data into a “real terms” prices basis. This guide has been written with casual as well as professional users of the data in mind, using language and concepts aimed at as wide an audience as possible.
The GDP deflator can be viewed as a measure of general inflation in the domestic economy. Inflation can be described as a measure of price changes over time. The deflator is usually expressed in terms of an index, i.e. a time series of index numbers. Percentage changes on the previous year are also shown. The GDP deflator reflects movements of hundreds of separate deflators for the individual expenditure components of GDP. These components include expenditure on such items as bread, investment in computers, imports of aircraft, and exports of consultancy services.
The series allows for the effects of changes in price (inflation) to be removed from a time series, i.e. it allows the change in the volume of goods and services to be measured. The resultant series can be used to express a given time series or data set in real terms, i.e. by removing price changes.
A series for the GDP deflator in index form is produced by the Treasury from data provided by the Office for National Statistics (ONS). Forecasts are produced by the Office for Budgetary Responsibility (OBR) and are usually updated around the time of major policy announcements, namely; the Chancellor’s Autumn Statement, and the Budget.
GDP deflators for earlier years (up to and including the most recent year for which full quarterly data have been published) are presented to 3 decimal places. The index for future years has been removed as the forecasts were not as accurate as this detail would suggest. Percentage year-on-year changes are given to two decimal places for earlier years, forecast years are presented to 1 decimal place as published in the Autumn Statement and the Budget.
Gross Domestic Product (GDP) is a measure of the total domestic economic activity. It is the sum of all incomes earned by the production of goods and services within the UK economic territory. It is worth noting that where the earner of the income resides is irrelevant, so long as the goods or services themselves are produced within the UK. GDP is equivalent to the value added to the economy by this activity. Value added can be defined as income
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This dataset provides both quarterly and annual estimates of the value of the goods and services produced in Iowa as provided by the U.S. Department of Commerce, Bureau of Economic Analysis in tables SAGDP2N, SAGDP9N, SAGDP10N, SQGDP2, and SQGDP9. Annual data is available beginning in 1997, and quarterly beginning 2005. The data include breakdowns of industries' contributions. Quarterly estimates are presented as an annual rate.
Gross domestic product (GDP) is the measure of the market value of all final goods and services produced within Iowa in a particular period of time. In concept, an industry's GDP by state, referred to as its "value added", is equivalent to its gross output (sales or receipts and other operating income, commodity taxes, and inventory change) minus its intermediate inputs (consumption of goods and services purchased from other U.S. industries or imported). The Iowa GDP a state counterpart to the Nation's GDP, the Bureau's featured and most comprehensive measure of U.S. economic activity. Iowa GDP differs from national GDP for the following reasons: Iowa GDP excludes and national GDP includes the compensation of federal civilian and military personnel stationed abroad and government consumption of fixed capital for military structures located abroad and for military equipment, except office equipment; and Iowa GDP and national GDP have different revision schedules. GDP is reported in millions of current dollars.
Real GDP is an inflation-adjusted measure of Iowa's gross product that is based on national prices for the goods and services produced within Iowa. The real estimates of gross domestic product (GDP) are measured in millions of chained dollars.
The annual per capita real GDP is also provided and is measured in chained dollars. In calculating the per capita real GDP, the real GDP is divided by the Census Bureau’s annual midyear (July 1) population estimates for the year.
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<ul style='margin-top:20px;'>
<li>Pakistan inflation rate for 2023 was <strong>30.77%</strong>, a <strong>10.89% increase</strong> from 2022.</li>
<li>Pakistan inflation rate for 2022 was <strong>19.87%</strong>, a <strong>10.38% increase</strong> from 2021.</li>
<li>Pakistan inflation rate for 2021 was <strong>9.50%</strong>, a <strong>0.24% decline</strong> from 2020.</li>
</ul>Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used.
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Inflation Rate in Pakistan increased to 3.50 percent in May from 0.30 percent in April of 2025. This dataset provides the latest reported value for - Pakistan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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<ul style='margin-top:20px;'>
<li>Nigeria inflation rate for 2022 was <strong>18.85%</strong>, a <strong>1.89% increase</strong> from 2021.</li>
<li>Nigeria inflation rate for 2021 was <strong>16.95%</strong>, a <strong>3.71% increase</strong> from 2020.</li>
<li>Nigeria inflation rate for 2020 was <strong>13.25%</strong>, a <strong>1.85% increase</strong> from 2019.</li>
</ul>Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used.
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Inflation Rate in Argentina decreased to 43.50 percent in May from 47.30 percent in April of 2025. This dataset provides the latest reported value for - Argentina Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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The model estimated in this document uses a set of variables that are available for a wide range of countries with different levels of development, resulting in a sample of 91 countries for the period 1970-2010. The file titled “Database PLS-PM” contains the data with which is possible to estimate the human capital index (ich) calculated in the paper. The variables used and their notation is as follows: FR= Fertility Rates VAAS = value-added contributed by the agricultural sector to GDP GNI = Gross National Incomes per capita LE = Life Expectancy MR = Mortality rate for children under five years AYE = Average Years of Education SPR = Student-Professor Ratio EC = Energy Consumption per capita PP = patent applications by residents per capita Given the database is not complete for all countries or for all years, this missing data was complete through interpolation method. All variables were transformed by mean of logarithms, except GNI. In the case of EC and PP, block of returns on human capital, the manifest variables are transformed such that they may be retrieved in levels at a later stage. 2. Data to estimate the economic growth regressions Cross-section: The file titled “Database – Cross-Section” contains the data with which it is possible to estimate the results shown in tables 1-5 of the manuscript. The variables used and their notation is the following: grow = GDP per capita, rate of change log(gdp75) = lag of GDP in 1975, logarithm demo = a binary variable measuring the level of democracy in the countries contes = indicators by principal component analysis to approximate the degree of contestation inclu = indicators by principal component analysis to approximate the degree of inclusiveness lnihc = human capital index estimated through PLS-PM, logarithm lnaye = average years of education developed by Barro and Lee (2013), logarithm lninves = investment in physical capital, measured as the average share of investment real to GDP, logarithm lngov = average government consumption as a percentage of GDP, logarithm lninfla = inflation measured by consumer prices, logarithm lnpop = population growth rate, logarithm lnich70, lnich75, lnape70, lnape75 lninves70 lninves75 lnpop70 lnpop75 = lags of lnich, lnaye, lninves and lnpop dafri = dummy for African countries Panel data: The file titled “Database – Panel data” contains the data with which it is possible to estimate the results shown in tables 6-9 of the manuscript. All variables are averages for the underlying period. The variables used and their notation is the following: grow = GDP per capita, rate of change lngdp75 = initial GDP in 1975, logarithm demo = a binary variable measuring the level of democracy in the countries contes = indicators by principal component analysis to approximate the degree of contestation inclu = indicators by principal component analysis to approximate the degree of inclusiveness lnihc = human capital index estimated through PLS-PM, logarithm lnaye = average years of education developed by Barro and Lee (2013), logarithm lninves = investment in physical capital, measured as the average share of investment real to GDP, logarithm lngov = average government consumption as a percentage of GDP, logarithm lninfla = inflation measured by consumer prices, logarithm lnpop = population growth rate, logarithm dafri = dummy for African countries
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.
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Inflation Rate in Nigeria decreased to 22.97 percent in May from 23.71 percent in April of 2025. This dataset provides - Nigeria Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Inflation Rate in Poland decreased to 4 percent in May from 4.30 percent in April of 2025. This dataset provides the latest reported value for - Poland Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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Inflation Rate in Taiwan decreased to 1.55 percent in May from 2.03 percent in April of 2025. This dataset provides the latest reported value for - Taiwan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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Inflation Rate in Ethiopia remained unchanged at 14.40 percent in May. This dataset provides the latest reported value for - Ethiopia Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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Inflation Rate in Malaysia decreased to 1.20 percent in May from 1.40 percent in April of 2025. This dataset provides - Malaysia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Inflation Rate in Tanzania remained unchanged at 3.20 percent in May. This dataset provides - Tanzania Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Inflation Rate in Ghana decreased to 18.40 percent in May from 21.20 percent in April of 2025. This dataset provides - Ghana Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Inflation Rate in Singapore decreased to 0.80 percent in May from 0.90 percent in April of 2025. This dataset provides - Singapore Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Inflation Rate in Indonesia decreased to 1.60 percent in May from 1.95 percent in April of 2025. This dataset provides - Indonesia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.