Based on preliminary results in 2023, the share of individuals in the Philippines with income below the poverty threshold was estimated at 15.5 percent, down from the estimate in 2021. In that year, the average per capita food threshold reached 23,000 Philippine pesos.

Philippines Incidence of Poor Families: CALABARZON data was reported at 6.700 % in 2015. This records a decrease from the previous number of 8.300 % for 2012. Philippines Incidence of Poor Families: CALABARZON data is updated yearly, averaging 8.800 % from Dec 1991 (Median) to 2015, with 7 observations. The data reached an all-time high of 19.100 % in 1991 and a record low of 6.700 % in 2015. Philippines Incidence of Poor Families: CALABARZON data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H025: Family Income and Expenditure Survey: Poverty Statistics and Proportion of Poor Population: By Regions.

Philippines Per Capita Poverty Threshold: Cordillera Administrative Region (CAR) data was reported at 21,770.000 PHP in 2015. This records an increase from the previous number of 19,483.000 PHP for 2012. Philippines Per Capita Poverty Threshold: Cordillera Administrative Region (CAR) data is updated yearly, averaging 13,471.500 PHP from Dec 1988 (Median) to 2015, with 10 observations. The data reached an all-time high of 21,770.000 PHP in 2015 and a record low of 5,116.000 PHP in 1988. Philippines Per Capita Poverty Threshold: Cordillera Administrative Region (CAR) data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H025: Family Income and Expenditure Survey: Poverty Statistics and Proportion of Poor Population: By Regions.

In 2023, a family of five in the Philippines had a poverty threshold of a little 13,873 Philippine pesos per month. That was higher than the monthly poverty threshold in 2018, which amounted to around about 12,000 Philippine pesos.

Philippines Incidence of Poor Families: Central Luzon data was reported at 8.900 % in 2015. This records a decrease from the previous number of 10.100 % for 2012. Philippines Incidence of Poor Families: Central Luzon data is updated yearly, averaging 13.050 % from Dec 1988 (Median) to 2015, with 10 observations. The data reached an all-time high of 29.300 % in 1988 and a record low of 8.900 % in 2015. Philippines Incidence of Poor Families: Central Luzon data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H025: Family Income and Expenditure Survey: Poverty Statistics and Proportion of Poor Population: By Regions.

Philippines Incidence of Poor Families: National Capital Region (NCR) data was reported at 2.700 % in 2015. This records an increase from the previous number of 2.600 % for 2012. Philippines Incidence of Poor Families: National Capital Region (NCR) data is updated yearly, averaging 4.100 % from Dec 1988 (Median) to 2015, with 10 observations. The data reached an all-time high of 21.600 % in 1988 and a record low of 2.100 % in 2003. Philippines Incidence of Poor Families: National Capital Region (NCR) data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H025: Family Income and Expenditure Survey: Poverty Statistics and Proportion of Poor Population: By Regions.

Unemployment Rate in Philippines decreased to 3.90 percent in May from 4.10 percent in April of 2025. This dataset provides - Philippines Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Philippines Per Capita Poverty Threshold: Cagayan Valley data was reported at 21,860.000 PHP in 2015. This records an increase from the previous number of 19,125.000 PHP for 2012. Philippines Per Capita Poverty Threshold: Cagayan Valley data is updated yearly, averaging 10,739.000 PHP from Dec 1988 (Median) to 2015, with 10 observations. The data reached an all-time high of 21,860.000 PHP in 2015 and a record low of 4,573.000 PHP in 1988. Philippines Per Capita Poverty Threshold: Cagayan Valley data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H025: Family Income and Expenditure Survey: Poverty Statistics and Proportion of Poor Population: By Regions.

Abstract

Estimated at 10 to 20 percent of the 109 million population in Philippines, Indigenous Peoples (IPs) are among the poorest and most marginalized, although they live in regions with vast natural resources. The lack of representation of IPs in official surveys and administrative data prevents an accurate assessment of their living conditions and the socio-economic challenges they face. This silences their voice, obscures state accountability towards them, and limits their agency to take on a more active role in society. The few available data and anecdotal evidence reveal that IPs face important inequalities of opportunity in several human development dimensions, which are likely aggravated by the COVID-19 pandemic. However, the extent of these inequalities is not well understood, due to scant data and research on IPs. The lack of surveys with information on ethnicity has also made it difficult to examine the type and extent of inequalities among and within ethnic groups in the country, as well as the impact of intersectionality with gender, disability, and place of residence on their inclusion. This represents a significant challenge when creating public policy at the national level or poverty reduction programs at the local level, as IPs remain unnoticed, unaccounted for, and ultimately deprived of their rights and any real benefit.

Therefore, reliable data on IPs is urgently needed, particularly in this COVID-19 era where IPs can be disproportionately disadvantaged due to legacies of inequality and exclusion. Hence, to collect data and inform evidence-based policy/decision making to better target the needs of IPs, the World Bank commissioned this first ever IP-specific household (HH) survey in Philippines to Philippine Survey and Research Center, Inc. (PSRC).

The main objective of this quantitative household survey is to improve the current understanding about the Indigenous Peoples (IPs) population in the Philippines, and eventually, craft strategies when addressing inequality or representation of the group. Specifically, the study aims to: 1. Collect as much data and inform evidence-based policy/decision making to better target the needs of IPs; 2. Gauge a better view/understanding of the IPs’ poverty, socio-economic condition and the exercise of their rights; 3. Add current knowledge into the ongoing World Bank Advisory Services and Analytics on the Indigenous Peoples of the Philippines.

Geographic coverage

Selected IP areas in the Philippines

Analysis unit

Indigenous Peoples (IPs) and Non-Indigenous Peoples (NIPs) with the following specifications: • Male/Female • 18 years old and above

Kind of data

Sample survey data [ssd]

Sampling procedure

Stratified multi-stage area probability sampling was employed as follows: • Stratification: The sample was stratified by region and by locale (i.e., urban/rural) • First Stage: Independent random selection of PSUs (barangays) within the strata • Second Stage: Selection of SSUs (households) within the sampled PSUs

A household is defined as a social unit consisting of a person or a group of persons who sleep in the same housing unit and have a common arrangement in the preparation and consumption of food. Household is often comprised of individuals related by blood . Other members of households not related to the household head by blood such as helper, borders, and non relatives can be included as member of the household as long they prepare and consume food together and do not go home to the fa mily more than once in a week. The domain was the Philippines’ 17 administrative regions. Using the Indigenous Peoples (IP) household population from the 2020 Census, the following information were computed: • Proportion of IP household population from Census for each region • Proportion of each region out of total IP household population

To manage cost and timings, sampling coverage was focused on regions greater than or about 3% from both information above and IP household population of at least 250,000. The 2,400 sample was then split into these regions to arrive at the desired area cuts: • CAR • Other Selected Luzon Regions (Cagayan Valley, MIMAROPA) • Western Visayas • Other Mindanao Regions (Zamboanga Peninsula, Northern Mindanao, Davao, SOCCSKSARGEN, CARAGA) • BARMM

From the sample of 2,400, there were 240 PSUs across the 10 covered regions. The 240 PSUs covered were split proportionately across the 10 regions using the IP household population. The table below shows the sampling breakdown at regional level.

Mode of data collection

Face-to-face [f2f]

Research instrument

Available in the English language but conducted in either English or Tagalog

Response rate

To achieve 2,400 completed interviews, 12,131 households were approached/ knocked. Of the 12,131 households knocked, 2,273 refused, 3,028 gave no reply or door locked, and 4,430 were not eligible given the specific requirements that we were looking for. The response rate among eligible interviews is 51%. Response rate is higher in Luzon and lowest in Mindanao.

The raster dataset consists of a 500 m score grid for slaughterhouse industry facilities, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multi-criteria Decision Analysis for value chain infrastructure location.

The analysis is based on livestock production potential using proximity to cities, crop production, and pig density.

The score is achieved by processing sub-model outputs that characterize production, logistical, and socioeconomic factors:

Supply - Feed and pig distribution.

Infrastructure - Transportation network (accessibility).

Philippines Statistics Authority 2023.

It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”Pig Density” * 0.4) + ("Poverty" * 0.3) + (“Major Cities Accessibility” * 0.2) + ("Crop Production" * 0.1)

Data publication: 2024-07-29

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Justeen De Ocampo

Data lineage:

Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets:

1. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units 2020. https://mapspam.info/data/
2. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2). https://data.apps.fao.org/catalog/iso/47457ad7-ed00-4346-81e2-85aacd0e6d91
3. OpenStreetMap.
4. Poverty - Philippines Statistics Authority 2023.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Zipped raster TIF file for Slaughterhouse Location Score: Pigs (Philippines - ~ 500 m)

The 500 m raster dataset represents selected top location score areas filtered by exclusive criteria: access to finance, distance to major roads, access to IT. The layer was produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location.

The location score is achieved by processing sub-model outputs characterizing logistical factors for the slaughterhouse facilities siting: Demand, Supply, Infrastructure/accessibility.

Access to finance, distance to roads, and urban areas are defined using a linear distance threshold:

• Banks - approx. 5km

• Major roads - approx. 2km

• Access to IT is characterized by applying the mobile broadband coverage map.

Data publication: 2024-07-29

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Justeen De Ocampo

Data lineage:

Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets:

1. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units 2020. https://mapspam.info/data/
2. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2)
3. OpenStreetMap.
4. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021 https://data.apps.fao.org/catalog/dataset/mobile-broadband-coverage-global-1km .
5. Poverty - Philippines Statistics Authority 2023.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Zipped raster TIF file for Slaughterhouse Final Location: Pigs (Philippines - ~ 500 m)

The Gross Domestic Product (GDP) in Philippines expanded 5.40 percent in the first quarter of 2025 over the same quarter of the previous year. This dataset provides - Philippines GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.