The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Inflation Rate in the United States increased to 2.40 percent in May from 2.30 percent in April of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

Inflation Rate in Turkey decreased to 35.05 percent in June from 35.41 percent in May of 2025. This dataset provides the latest reported value for - Turkey Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Inflation Rate in India decreased to 2.10 percent in June from 2.82 percent in May of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Japan decreased to 3.50 percent in May from 3.60 percent in April of 2025. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This table contains 13 series, with data from 1949 (not all combinations necessarily have data for all years). Data are presented for the current month and previous four months. Users can select other time periods that are of interest to them.

Inflation Rate in Thailand decreased by 0.25 percent in June from -0.57 percent in May of 2025. This dataset provides the latest reported value for - Thailand Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Nigeria’s inflation has been higher than the average for African and Sub-Saharan countries for years now, and even exceeded 16 percent in 2017 – and a real, significant decrease is nowhere in sight. The bigger problem is its unsteadiness, however: An inflation rate that is bouncing all over the place, like this one, is usually a sign of a struggling economy, causing prices to fluctuate, and unemployment and poverty to increase. Nigeria’s economy - a so-called “mixed economy”, which means the market economy is at least in part regulated by the state – is not entirely in bad shape, though. More than half of its GDP is generated by the services sector, namely telecommunications and finances, and the country derives a significant share of its state revenues from oil.

Because it got high

To simplify: When the inflation rate rises, so do prices, and consequently banks raise their interest rates as well to cope and maintain their profit margin. Higher interest rates often cause unemployment to rise. In certain scenarios, rising prices can also mean more panicky spending and consumption among end users, causing debt and poverty. The extreme version of this is called hyperinflation: A rapid increase of prices that is out of control and leads to bankruptcies en masse, devaluation of money and subsequently a currency reform, among other things. But does that mean that low inflation is better? Maybe, but only to a certain degree; the ECB, for example, aspires to maintain an inflation rate of about two percent so as to keep the economy stable. As soon as we reach deflation territory, however, things are starting to look grim again. The best course is a stable inflation rate, to avoid uncertainty and rash actions.

Nigeria today

Nigeria is one of the countries with the largest populations worldwide and also the largest economy in Africa, with its economy growing rapidly after a slump in the aforementioned year 2017. It is slated to be one of the countries with the highest economic growth over the next few decades. Demographic key indicators, like infant mortality rate, fertility rate, and the median age of the population, all point towards a bright future. Additionally, the country seems to make big leaps forward in manufacturing and technological developments, and boasts huge natural resources, including natural gas. All in all, Nigeria and its inflation seem to be on the upswing – or on the path to stabilization, as it were.

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.

In this paper we use GARCH-M methods to test four hypotheses about the effects of real and nominal uncertainty on average inflation and output growth in the United States from 1948 to 1996. We find no evidence that higher inflation uncertainty or higher output growth uncertainty raises the average inflation rate. We also find no support for the idea that more risky output growth is associated with a higher average real growth rate. Our key result is that in a variety of models and sample periods, inflation uncertainty significantly lowers real output growth.

Average weekly earnings for the whole economy, for total and regular pay, in real terms (adjusted for consumer price inflation), UK, monthly, seasonally adjusted.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Badakhshan, Badghis, Baghlan, Balkh, Bamyan, Daykundi, Farah, Faryab, Paktya, Ghazni, Ghor, Hilmand, Hirat, Nangarhar, Jawzjan, Kabul, Kandahar, Kapisa, Khost, Kunar, Kunduz, Laghman, Logar, Wardak, Nimroz, Nuristan, Paktika, Panjsher, Parwan, Samangan, Sar-e-pul, Takhar, Uruzgan, Zabul, Market Average, Armavir, Ararat, Aragatsotn, Tavush, Gegharkunik, Shirak, Kotayk, Syunik, Lori, Vayotz Dzor, Yerevan, Kayanza, Ruyigi, Bubanza, Karuzi, Bujumbura Mairie, Muramvya, Gitega, Rumonge, Bururi, Kirundo, Cankuzo, Cibitoke, Muyinga, Rutana, Bujumbura Rural, Makamba, Ngozi, Mwaro, SAHEL, CASCADES, SUD-OUEST, EST, BOUCLE DU MOUHOUN, CENTRE-NORD, PLATEAU-CENTRAL, HAUTS-BASSINS, CENTRE, NORD, CENTRE-SUD, CENTRE-OUEST, CENTRE-EST, Khulna, Chittagong, Barisal, Rajshahi, Dhaka, Rangpur, Sylhet, Mymensingh, Ouaka, Mbomou, Bangui, Nana-Mambéré, Ouham, Sangha-Mbaéré, Ombella M'Poko, Mambéré-Kadéï, Vakaga, Ouham Pendé, Lobaye, Haute-Kotto, Kémo, Nana-Gribizi, Bamingui-Bangoran, Haut-Mbomou, Nord, Extrême-Nord, Ouest, Nord-Ouest, Adamaoua, Sud-Ouest, Est, Littoral, Centre, Haut-Uele, Nord-Kivu, Ituri, Tshopo, Kwilu, Kasai, Sud-Kivu, Kongo-Central, Nord-Ubangi, Sud-Ubangi, Kasai-Central, Bas-Uele, Tanganyika, Lualaba, Kasai-Oriental, Kwango, Haut-Lomami, Haut-Katanga, Maniema, Kinshasa, Equateur, Lomami, Likouala, Brazzaville, Point-Noire, Pool, Bouenza, Cuvette, Lekoumou, Nzerekore, Boke, Kindia, Kankan, Faranah, Mamou, Labe, Kanifing Municipal Council, Central River, Upper River, West Coast, North Bank, Lower River, Bafata, Tombali, Cacheu, Sector Autonomo De Bissau, Biombo, Oio, Gabu, Bolama, Quinara, North, South, Artibonite, South-East, Grande'Anse, North-East, West, North-West, SULAWESI UTARA, SUMATERA UTARA, KALIMANTAN UTARA, JAWA BARAT, NUSA TENGGARA BARAT, NUSA TENGGARA TIMUR, SULAWESI SELATAN, JAMBI, JAWA TIMUR, KALIMANTAN SELATAN, BALI, BANTEN, JAWA TENGAH, RIAU, SUMATERA BARAT, KEPULAUAN RIAU, PAPUA, SULAWESI BARAT, BENGKULU, MALUKU UTARA, DAERAH ISTIMEWA YOGYAKARTA, KALIMANTAN BARAT, KALIMANTAN TENGAH, PAPUA BARAT, SUMATERA SELATAN, MALUKU, KEPULAUAN BANGKA BELITUNG, ACEH, DKI JAKARTA, SULAWESI TENGGARA, KALIMANTAN TIMUR, LAMPUNG, GORONTALO, SULAWESI TENGAH, Anbar, Babil, Baghdad, Basrah, Diyala, Dahuk, Erbil, Ninewa, Kerbala, Kirkuk, Missan, Muthanna, Najaf, Qadissiya, Salah al-Din, Sulaymaniyah, Thi-Qar, Wassit, Coast, North Eastern, Nairobi, Rift Valley, , Eastern, Central, Nyanza, Attapeu, Bokeo, Bolikhamxai, Champasack, Houaphan, Khammouan, Louangphabang, Louangnamtha, Oudomxai, Phongsaly, Salavan, Savannakhet, Sekong, Vientiane Capital, Vientiane, Xaignabouly, Xiengkhouang, Akkar, Mount Lebanon, Baalbek-El Hermel, Beirut, Bekaa, El Nabatieh, Nimba, Grand Kru, Grand Cape Mount, Gbarpolu, Grand Bassa, Rivercess, Montserrado, River Gee, Lofa, Bomi, Bong, Sinoe, Maryland, Margibi, Grand Gedeh, East, North Central, Uva, Western, Sabaragamuwa, Southern, Northern, North Western, Kidal, Gao, Tombouctou, Bamako, Kayes, Koulikoro, Mopti, Segou, Sikasso, Yangon, Rakhine, Shan (North), Kayin, Kachin, Shan (South), Mon, Tanintharyi, Mandalay, Kayah, Shan (East), Chin, Magway, Sagaing, Zambezia, Cabo_Delgado, Tete, Manica, Sofala, Maputo, Gaza, Niassa, Inhambane, Maputo City, Nampula, Hodh Ech Chargi, Hodh El Gharbi, Brakna, Adrar, Assaba, Guidimakha, Gorgol, Trarza, Tagant, Dakhlet-Nouadhibou, Nouakchott, Tiris-Zemmour, Central Region, Southern Region, Northern Region, Tillaberi, Tahoua, Agadez, Zinder, Dosso, Niamey, Maradi, Diffa, Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Adamawa, Cordillera Administrative region, Region XIII, Region VI, Region V, Region III, Autonomous region in Muslim Mindanao, Region IV-A, Region VIII, Region VII, Region X, Region II, Region IV-B, Region XII, Region XI, Region I, National Capital region, Region IX, North Darfur, Blue Nile, Nile, Eastern Darfur, West Kordofan, Gedaref, West Darfur, North Kordofan, South Kordofan, Kassala, Khartoum, White Nile, South Darfur, Red Sea, Sennar, Al Gezira, Central Darfur, Tambacounda, Diourbel, Ziguinchor, Kaffrine, Dakar, Saint Louis, Fatick, Kolda, Louga, Kaolack, Kedougou, Matam, Thies, Sedhiou, Shabelle Hoose, Juba Hoose, Bay, Banadir, Shabelle Dhexe, Gedo, Hiraan, Woqooyi Galbeed, Awdal, Bari, Juba Dhexe, Togdheer, Nugaal, Galgaduud, Bakool, Sanaag, Mudug, Sool, Warrap, Unity, Jonglei, Northern Bahr el Ghazal, Upper Nile, Central Equatoria, Western Bahr el Ghazal, Eastern Equatoria, Western Equatoria, Lakes, Aleppo, Dar'a, Quneitra, Homs, Deir-ez-Zor, Damascus, Ar-Raqqa, Al-Hasakeh, Hama, As-Sweida, Rural Damascus, Tartous, Idleb, Lattakia, Ouaddai, Salamat, Wadi Fira, Sila, Ennedi Est, Batha, Tibesti, Logone Oriental, Logone Occidental, Guera, Hadjer Lamis, Lac, Mayo Kebbi Est, Chari Baguirmi, Ennedi Ouest, Borkou, Tandjile, Mandoul, Moyen Chari, Mayo Kebbi Ouest, Kanem, Barh El Gazal, Ndjaména, Al Dhale'e, Aden, Al Bayda, Al Maharah, Lahj, Al Jawf, Raymah, Al Hudaydah, Hajjah, Amran, Shabwah, Dhamar, Ibb, Sana'a, Al Mahwit, Marib, Hadramaut, Sa'ada, Amanat Al Asimah, Socotra, Taizz, Abyan

Inflation Rate in Russia decreased to 9.40 percent in June from 9.90 percent in May of 2025. This dataset provides - Russia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Vietnam increased to 3.57 percent in June from 3.24 percent in May of 2025. This dataset provides the latest reported value for - Vietnam Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

Energy price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes energy price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: Badakhshan, Badghis, Baghlan, Balkh, Bamyan, Daykundi, Farah, Faryab, Paktya, Ghazni, Ghor, Hilmand, Hirat, Nangarhar, Jawzjan, Kabul, Kandahar, Kapisa, Khost, Kunar, Kunduz, Laghman, Logar, Wardak, Nimroz, Nuristan, Paktika, Panjsher, Parwan, Samangan, Sar-e-pul, Takhar, Uruzgan, Zabul, Market Average, Armavir, Ararat, Aragatsotn, Tavush, Gegharkunik, Shirak, Kotayk, Syunik, Lori, Vayotz Dzor, Yerevan, Kanifing Municipal Council, Central River, Upper River, West Coast, North Bank, Lower River, Bafata, Tombali, Cacheu, Sector Autonomo De Bissau, Biombo, Oio, Gabu, Bolama, Quinara, Anbar, Babil, Baghdad, Basrah, Diyala, Dahuk, Erbil, Ninewa, Kerbala, Kirkuk, Missan, Muthanna, Najaf, Qadissiya, Salah al-Din, Sulaymaniyah, Thi-Qar, Wassit, Attapeu, Bokeo, Bolikhamxai, Champasack, Houaphan, Khammouan, Louangphabang, Louangnamtha, Oudomxai, Phongsaly, Salavan, Savannakhet, Sekong, Vientiane Capital, Vientiane, Xaignabouly, Xiengkhouang, Akkar, Mount Lebanon, Baalbek-El Hermel, North, Beirut, Bekaa, El Nabatieh, South, Nimba, Grand Kru, Grand Cape Mount, Gbarpolu, Grand Bassa, Rivercess, Montserrado, River Gee, Lofa, Bomi, Bong, Sinoe, Maryland, Margibi, Grand Gedeh, Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Adamawa, Shabelle Hoose, Juba Hoose, Bay, Banadir, Shabelle Dhexe, Gedo, Hiraan, Woqooyi Galbeed, Awdal, Bari, Juba Dhexe, Togdheer, Nugaal, Galgaduud, Bakool, Sanaag, Mudug, Sool, , Warrap, Unity, Jonglei, Northern Bahr el Ghazal, Upper Nile, Central Equatoria, Western Bahr el Ghazal, Eastern Equatoria, Western Equatoria, Lakes, Aleppo, Dar'a, Quneitra, Homs, Deir-ez-Zor, Damascus, Ar-Raqqa, Al-Hasakeh, Hama, As-Sweida, Rural Damascus, Tartous, Idleb, Lattakia, Al Dhale'e, Aden, Al Bayda, Al Maharah, Lahj, Al Jawf, Raymah, Al Hudaydah, Hajjah, Amran, Shabwah, Dhamar, Ibb, Sana'a, Al Mahwit, Marib, Hadramaut, Sa'ada, Amanat Al Asimah, Socotra, Taizz, Abyan

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

Inflation Rate in Mexico decreased to 4.32 percent in June from 4.42 percent in May of 2025. This dataset provides - Mexico Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

        A dataset of monthly food price inflation estimates (aggregated for all food products available in the data) is also available for all countries covered by this modeling exercise.

Geographic coverage notes

The data cover the following sub-national areas: Kanifing Municipal Council, Central River, Upper River, West Coast, North Bank, Lower River, Market Average

VITAL SIGNS INDICATOR Home Prices (EC7)

FULL MEASURE NAME Home Prices

LAST UPDATED August 2019

DESCRIPTION Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.

DATA SOURCE Zillow Median Sale Price (1997-2018) http://www.zillow.com/research/data/

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1997-2018; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Median housing price estimates for the region, counties, cities, and zip code come from analysis of individual home sales by Zillow. The median sale price is the price separating the higher half of the sales from the lower half. In other words, 50 percent of home sales are below or above the median value. Zillow defines all homes as single-family residential, condominium, and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that you own in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums where the homeowners own shares in the corporation that owns the building, not the actual units themselves.

For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Home sales prices are not reliably available for Houston, because Texas is a non-disclosure state. For more information on non-disclosure states, see: http://www.zillow.com/blog/chronicles-of-data-collection-ii-non-disclosure-states-3783/

Inflation-adjusted data are presented to illustrate how home prices have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself.