Inflation Rate in Philippines decreased to 0.90 percent in July from 1.40 percent in June of 2025. This dataset provides the latest reported value for - Philippines Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 2024, the Philippines’ inflation rate amounted to 3.21 percent. The Philippines are considered “newly industrialized”, but the economy relies on remittances from nationals overseas, and the services sector generates most of its GDP . Emerging and soon to develop?After switching from agriculture to services and manufacturing, the Philippines are now an emerging economy, i.e. the country has some characteristics of a developed nation but is not quite there yet. In order to transition into a developed nation, the Philippines must meet certain requirements, like being able to sustain their economic development, being very open to foreign investors, or maintaining a very high stability of the institutional framework (like law enforcement and the government). Only if these changes are irreversible can they be classified as a developed nation. The Philippines’ switch to servicesEver since the switch to services and manufacturing, employment in these areas has increased and the country is now among those with the highest employment in the tourism industry worldwide. This transition was not entirely voluntary but also due to decreasing government support, the liberalization of trade, and reform programs. Still, agriculture is important for the country: As of 2017, more than a quarter of Filipinos are still working in the agricultural sector, and urbanization has only increased very slightly over the last decade.

Philippines inflation rate for 2024 was 3.21%, a 2.77% decline from 2023.

<ul style='margin-top:20px;'>

<li>Philippines inflation rate for 2023 was <strong>5.98%</strong>, a <strong>0.16% increase</strong> from 2022.</li>
<li>Philippines inflation rate for 2022 was <strong>5.82%</strong>, a <strong>1.89% increase</strong> from 2021.</li>
<li>Philippines inflation rate for 2021 was <strong>3.93%</strong>, a <strong>1.53% increase</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.

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.

Geographic coverage notes

The data cover the following areas: Afghanistan, Armenia, Bangladesh, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Gambia, The, Guinea, Guinea-Bissau, Haiti, Indonesia, Iraq, Kenya, Lao PDR, Lebanon, Liberia, Libya, Malawi, Mali, Mauritania, Mozambique, Myanmar, Niger, Nigeria, Philippines, Senegal, Somalia, South Sudan, Sri Lanka, Sudan, Syrian Arab Republic, Yemen, Rep.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8706340%2F4cb2a12d17b4a770c483e6803fb43097%2Findia-inflation-likely-rebounded-in-november-on-higher-food-prices-reuters-poll.jpg?generation=1711815784112228&alt=media" alt=""> 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.

Geographical Coverage: Armenia, Bangladesh, Guinea, Indonesia, Kenya, Libya, Malawi, Mauritania, Philippines, Senegal, Sri Lanka, Afghanistan, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, , Gambia, The, Haiti, Lao People's Democratic Republic, Liberia, Mali, Mozambique, Myanmar, Niger, Somalia, South Sudan, Sudan, , Congo, Republic of, Iraq, Nigeria, Syrian Arab Republic, Lebanon, Guinea-Bissau

Temporal Coverage: 2008 2024 Original data: https://microdata.worldbank.org/index.php/catalog/4509/study-description

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: 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, Market Average

The USD/PHP exchange rate rose to 56.9920 on August 11, 2025, up 0.20% from the previous session. Over the past month, the Philippine Peso has weakened 0.57%, but it's up by 0.53% over the last 12 months. Philippine Peso - values, historical data, forecasts and news - updated on August of 2025.

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: Kidal, Gao, Tombouctou, Bamako, Kayes, Koulikoro, Mopti, Segou, Sikasso, Market Average

It is widely acknowledged that the Fed can control the average inflation rate over a period of time reasonably well. Because of this and the Federal Open Market Committee's (FOMC's) long-standing commitment to price stability, the author argues that the FOMC has an implicit long-run inflation objective (LIO) lower and upper bounds to the long-run inflation rate. He shows that the statements made by the FOMC in 2003 clarified the lower bound of its LIO and that the average of long-run inflation expectations responded by rising about 80 basis points. Moreover, consistent with reducing the market's uncertainty about the FOMC's LIO, long-run inflation expectations became more stable. The FOMC has recently been more specific about the upper bound of its LIO as well. The FOMC could eliminate the remaining uncertainty by establishing an explicit, numerical inflation objective.

Serbia's Inflation, GDP deflator (annualized) is 2.44% which is the 88th highest in the world ranking. Transition graphs on Inflation, GDP deflator (annualized) in Serbia and comparison bar charts (USA vs. China vs. Japan vs. Serbia), (Bulgaria vs. Lebanon vs. Serbia) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Uruguay's Inflation, GDP deflator (annualized) is 7.67% which is the 20th highest in the world ranking. Transition graphs on Inflation, GDP deflator (annualized) in Uruguay and comparison bar charts (USA vs. China vs. Japan vs. Uruguay), (Georgia vs. Bosnia vs. Herzegovina vs. Uruguay) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

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, Eastern Equatoria, Central Equatoria, Western Bahr el Ghazal, 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

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

Income, Expenditure and Consumption Surveys assume a prime importance among all household surveys undertaken by the national statistical offices all over the world. On the basis of such surveys, the standard of living of both households and individuals can be measured. Determining poverty line and setting up a basis for social welfare assistance depend on these surveys. In addition, weights for consumer price index which in turn is an important measure of inflation are derived from such surveys. Egypt has recognized the greatest importance of these surveys long time ago, the current HIECS 2004/2005 is the eighth Household Income, Expenditure and Consumption Survey that was carried out in 2004/2005, on a sample of 48000 households, among a long series of similar surveys that started back in 1955, and followed by several surveys.

The survey main objectives are: To identify expenditure levels and patterns of population as well as socio-economic and demographic differentials. To estimate the quantities, values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is an important input for national planning. Current and past demand estimates are utilized to predict future demands. To measure mean household and per-capita expenditure for various expenditure items along with socio-economic correlates. To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation. To define mean household and per-capita income from different sources. To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependent on the results of this survey. To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas. To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure. To study the relationships between demographic, geographical, housing characteristics of households and their income and expenditure for commodities and services. To provide data necessary for national accounts especially in compiling inputs and outputs tables. To identify consumers behavior changes among socio-economic groups in urban and rural areas. To identify per capita food consumption and its main components of calories, proteins and fats according to its sources and the levels of expenditure in both urban and rural areas. To identify the value of expenditure for food according to sources, either from household production or not, in addition to household expenditure for non food commodities and services. To identify distribution of households according to the possession of some appliances and equipments such as (cars, satellites, mobiles ...) in urban and rural areas. To identify the percentage distribution of income recipients according to some background variables such as housing conditions, size of household and characteristics of head of household.

It is the first time that the Household Income, Expenditure and Consumption Survey implies the following issues: 1- The use of the classification of individual consumption according to purpose (COICOP) in designing the expenditure and consumption questionnaire. 2- The inclusion of the main sales outlets of food and beverages. 3- The addition of school enrollment (6+ years) to the household schedule. 4- The inclusion of expenditure for used commodities (durables and semi durables). 5- The addition of data related to change in assets owned by the household during the reference year.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE CENTRAL AGENCY FOR PUBLIC MOBILIZATION AND STATISTICS (CAPMAS)

The sample of the Household Income, Expenditure and Consumption Survey (HIECS) of 2004/2005 is a multi-stage stratified cluster sample and self-weighted to the practical extent. Its designed size is 48000 households allocated among governorates and their urban/rural components in proportion to size. The sample was selected in three stages (the second stage is considered dummy), the first two stages is related to the Master Sample which has been drawn directly before the fieldwork of HIECS started. The third sampling stage concerns with the selection of a sample of 40 households from each Master Sample Areas (1200 areas with approximately 700 households in each).

The Master Sample (1200 areas) has been allocated among the governorates of Egypt, with its urban/rural components, in proportion with the estimated size of households of every stratum (governorate) and substratum (urban/rural populations). At the first sampling stage, the shiakha in urban and village in rural are considered the smallest administrative divisions for which census data are available. Therefore such divisions were considered Primary Sampling Units (PSUs) for urban and rural samples of all governorates respectively. Small towns which are not further subdivided into smaller administrative units are dealt with as urban PSUs. While the larger shiakhas or towns were subdivided into several PSUs using the 1996 census data. At the contrary, a village with less than 600 households in 1996 (700 households at present) was joined to the adjacent village so as to make certain that all PSUs are greater than 600 households in 1996. Subsequently, the sampling frames of the first stage sample of urban/rural substrata for all governorates were formed. Implicit stratification was introduced to both urban and rural frames. At the second stage of sampling, a single area segment was selected following the equal probability selection method. A field operation has been carried out for the purpose of creating a household list for each selected second stage sample segment. In the third sampling stage representing the final stage, 40 households were selected from each area segment selected in the second sampling stage of the master sample. With the aim of reducing the field efforts it was deemed efficient to limit the spread of the household sample over the entire area segments by sampling clusters of 5 households each instead of sampling individual households directly. It is worth mentioning that the method of systematic selection will jeopardize the property of equal probability selection as each household in the list still has 40 chances of being selected in the sample.

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among the documentation materials published in both Arabic and English.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires have been designed as following: 1- Expenditure and consumption questionnaire. 2- Diary questionnaire for expenditure and consumption. 3- Income questionnaire.

In designing the questionnaires of expenditure, consumption and income, we were taking into our consideration the following: - Using the recent concepts and definitions of International Labor Organization approved in the International Convention of Labor Statisticians held in Geneva, 2003. - Using the recent Classification of Individual Consumption according to Purpose (COICOP). - Using more than one approach of expenditure measurement to serve many purposes of the survey.

A brief description of each questionnaire is given next:

1- Expenditure and Consumption Questionnaire

This questionnaire comprises 14 tables in addition to identification and geographic data of household on the cover page. The questionnaire is divided into two main sections.

Section one: Household schedule and other information. It includes: - Demographic characteristics and basic data for all household individuals consisting of 16 questions for every person. - Members of household who are currently working abroad. - The household ration card. - The main outlets that provide food and beverage. - Domestic and foreign tourism. - The housing conditions including 15 questions. - Means of transportation used to go to work or school. - The household possession of appliances and means

One United States dollar was worth over ****** Indonesian rupiah in May 2024, the highest value in a comparison of over 50 different currencies worldwide. All countries and territories shown here are based on the Big Mac Index - a measurement of how much a single Big Mac is worth across different areas in the world. This exchange rate comparison reveals a strong position of the dollar in Asia and Latin America. Note, though, that several of the top currencies shown here do not rank among the most traded. The quarterly U.S. dollar exchange rate against the ten biggest forex currencies only contains the Korean won and the Japanese yen.

Zimbabwe's Inflation, GDP deflator (annualized) is -4.04% which is the 183rd highest in the world ranking. Transition graphs on Inflation, GDP deflator (annualized) in Zimbabwe and comparison bar charts (USA vs. China vs. Japan vs. Zimbabwe), (Chad vs. Guinea vs. Zimbabwe) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Consumer Price Index CPI in Philippines increased to 127.70 points in July from 127.30 points in June of 2025. This dataset provides the latest reported value for - Philippines Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Guyana's Consumer Price Index (annualized) is 2.09% which is the 84th highest in the world ranking. Transition graphs on Consumer Price Index (annualized) in Guyana and comparison bar charts (USA vs. China vs. Japan vs. Guyana), (Djibouti vs. Bhutan vs. Guyana) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

The benchmark interest rate in Philippines was last recorded at 5.25 percent. This dataset provides the latest reported value for - Philippines Interest 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: Nimba, Grand Kru, Grand Cape Mount, Gbarpolu, Grand Bassa, Rivercess, Montserrado, River Gee, Lofa, Bomi, Bong, Sinoe, Maryland, Margibi, Grand Gedeh, Market Average

Mozambique's Consumer Price Index (annualized) is 2.78% which is the 60th highest in the world ranking. Transition graphs on Consumer Price Index (annualized) in Mozambique and comparison bar charts (USA vs. China vs. Japan vs. Mozambique), (Malaysia vs. Ghana vs. Mozambique) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.