Preliminary estimates for 2023 show that the region of Zamboanga Peninsula had the highest poverty incidence among families in the Philippines at 24.2 percent. In comparison, the National Capital Region (NCR) had the lowest poverty incidence among families during this period. Overall, the total poverty incidence of families in the Philippines was 10.9 percent.

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 PH: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 40.100 % in 2015. This records a decrease from the previous number of 42.200 % for 2012. Philippines PH: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 42.200 % from Dec 1985 (Median) to 2015, with 11 observations. The data reached an all-time high of 46.000 % in 1997 and a record low of 40.100 % in 2015. Philippines PH: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Philippines PH: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at 1.380 % in 2015. Philippines PH: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging 1.380 % from Dec 2015 (Median) to 2015, with 1 observations. Philippines PH: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank: Poverty. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Philippines PH: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 2.430 % in 2015. Philippines PH: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 2.430 % from Dec 2015 (Median) to 2015, with 1 observations. Philippines PH: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

City and municipal-level poverty estimates for 2012, 2009, and 2006

2012 City and Municipal-Level Small Area Poverty Estimates

Source: Philippine Statistics Authority, through a national government funded project on the generation of the 2012 small area estimates on poverty
http://www.nscb.gov.ph/announce/2014/PSA-NSCB_2012MunCity_Pov.asp
Note: Region V, Sorsogon, Bacon is in 2006 and 2009 data but not the 2012 data. According to Wikipedia, Sorgoson City was formed by merging the Bacon and Sorsogon towns.

City and Municipal-Level Poverty Estimates; 2006 and 2009

Source: NSCB/World Bank/AusAID Project on the Generation of the 2006 and 2009 City and Municipal Level Poverty Estimates
http://www.nscb.gov.ph/poverty/dataCharts.asp
PDF download
Note: The 2009 city and municipal level poverty estimates for ARMM were revised to reflect on the movement/creation of municipalities and barangays which were not considered in the preliminary estimation of the 2009 city and municipal level poverty estimates published in the NSCB website last 03 August 2013.

Column Header / Description

• Prelim_*year* / Preliminary (indicated by "TRUE" or "FALSE")
• Pov_*year* / Poverty Incidence
• SE_*year* / Standard Error
• CoV_*year* / Coefficient of Variation
• Con_90lower_*year* / 90% Confidence Interval Lower Limit
• Con_90upper_*year* / 90% Confidence Interval Upper Limit

Philippines PH: Multidimensional Poverty Index: scale 0-1 data was reported at 0.071 NA in 2017. This records a decrease from the previous number of 0.104 NA for 2016. Philippines PH: Multidimensional Poverty Index: scale 0-1 data is updated yearly, averaging 0.087 NA from Dec 2016 (Median) to 2017, with 2 observations. The data reached an all-time high of 0.104 NA in 2016 and a record low of 0.071 NA in 2017. Philippines PH: Multidimensional Poverty Index: scale 0-1 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Social: Poverty and Inequality. ;Government statistical agencies. Data for EU countires are from the EUROSTAT;;

Abstract

The 2008 Annual Poverty Indicators Survey (APIS) is conducted by the National Statistics Office (NSO) as a rider to the July 2008 Labor Force Survey (LFS). The 2008 APIS is the sixth in the series of annual poverty indicators surveys conducted nationwide. Since 1998, APIS has been conducted during the years when the Family Income and Expenditures Survey (FIES) is not conducted, except in 2001 and 2005 due to budgetary constraints.

The APIS is a nationwide survey designed to provide non-income indicators related to poverty at the national and regional levels. It is designed to gather data on the socio-economic profile of families and other information that are related to their living conditions. Specifically, it generates indicators which are correlated with poverty, such as indicators regarding the ownership or possession of house and lot, the types of the materials of the roofs and walls of their housing units, their access to safe water, the types of toilet facility they use in their homes, and presence of family members of specified characteristics such as children 6-12 years old enrolled in elementary, children 13-16 years old enrolled in high school, members 18 years old and over gainfully employed, working children 5-17 years old and family members with membership in any health, life and/or pre-need insurance system.

The APIS is being undertaken by the National Statistics Office as mandataed by Commonwealth Act 591 which authorizes the then Bureau of the Census and Statistics, now NSO, "to conduct by enumeration, sampling or other methods, for statistical purposes, studies of the social and economic situation of the country" and in consonance with the provision of Executive Order 121 which designated the office as the "major statistical agency responsible for generating general purpose statistics.

Geographic coverage

National Coverage Seventeen (17) Administrative Regions: National Capital Region (NCR) Cordillera Administrative Region (CAR) I - Ilocos II - Cagayan Valley III - Central Luzon IVA - CALABARZON IVB - MIMAROPA V - Bicol VI - Western Visayas VII - Central Visayas VIII - Eastern Visayas IX - Zamboanga Peninsula X - Northern Mindanao XI - Davao XII - SOCCSKSARGEN XIII - Caraga Autonomous Region in Muslim Mindanao (ARMM)

Analysis unit

Households

Universe

The survey covered all households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2008 APIS is a sample survey designed to provide data representative of the country and its 17 administrative regions. The survey's sample design helps ensure this representativeness. The 2008 APIS used the 2003 master sample created for household surveys on the basis of the 2000 Census of Population and Housing (CPH) results. The survey used four replicates of the master sample. For each region (domain) and stratum, a three-stage sampling scheme was used: the selection of primary sampling units (PSUs) for the first stage, of sample enumeration areas (EAs) for the second stage, and of sample housing units for the third stage. PSUs within a region were stratified based on the proportion of households living in housing units made of strong materials, proportion of households in the barangay engaged in agricultural activities and per capita income of the city/municipality.

As earlier mentioned, a three-stage sampling design was used in each stratum within a region. In the first stage, primary sampling units (PSUs) were selected with probability proportional to the number of households in the 2000 Census. PSUs consisted of a barangay or a group of contiguous barangays. In the second stage, in each sampled PSU, EAs were selected with probability proportional to the number of households in the 2000 Census. An EA is defined as an area with discernable boundaries consisting of approximately 350 contiguous households. In the third stage, from each sampled EA, housing units were selected using systematic sampling. For operational considerations, at most 30 housing units were selected per sample EA. All households in sample housing units were interviewed except for sample housing units with more than three households. In such a housing unit, three households were randomly selected with equal probability.

The 2008 APIS was conducted simultaneously with the July 2008 Labor Force Survey (LFS). All sample households of the July 2008 LFS were interviewed for the 2008 APIS. Only household members related to the household head by blood, marriage or adoption were considered as members of the sample household in APIS. Family members of the household head who are working abroad were excluded.

Sampling deviation

NA

Mode of data collection

Face-to-face [f2f]

Research instrument

Although questions on 'Changes in Welfare' were dropped and some items were modified for the 2008 APIS, most of the questions/items in the previous APISs were retained as requested by data users. Nine items were added in order to generate data that will be more useful in assessing the poverty situation in the country. The new questionnaire for the 2008 contains the abridged version of the module on entrepreneurial activities resulting to the reduction of the number of pages from 24 to 12. The decision to use the abridged version was based on the results of the study entitled “Redesigning APIS as a Poverty Monitoring Tool” undertaken by the Demographic and Social Statistics Division in 2006. The redesigned questionnaire produced results which are not statistically different from results based on the original design in 2004. The use of the redesigned questionnaire is also cost-efficient.

A round table discussion was held for the 2008 APIS before the conduct of the pretest. The redesigned APIS questionnaire based from the project's output was presented. It was agreed upon during this meeting to adopt the redesigned APIS for this round of APIS, with the addition of item on 'Hunger'.

Cleaning operations

Flow of Processing Activity

In order to implement a systematic flow of the processing activities and reduce the movement of questionnaires from one employee to another, the same processor performed the following specific activities for the same folio. 1. General screening; 2. Editing and coding of APIS questionnaires and computations of totals ; and 3. General review of edited APIS questionnaire.

Folioing

To facilitate handling during manual and machine processing, APIS questionnaires were folioed in the Provincial Office before the start of manual processing.

The APIS questionnaires for one sample barangay/EA contained in the folio was arranged consecutively according to the sample housing serial number (SHSN) from lowest to highest.

General Screening

General screening was done by going over the submitted accomplished questionnaires and checking for the completeness of the geographic identification and other information called for in the cover page.

General screening for APIS questionnaires was done to ensure that the geographic and household identification and the entire sample households are the same with the MS Form 6.

General Instructions on Manual Processing

The following instructions was observed in manual processing.

1. Prior to editing and coding of items, the questionnaires were checked if they were properly folioed. Folioing was done in the province. Regional Offices checked if folioing was done properly by the Provincial Offices.

2. All questionnaires for one folio was assigned to only one editor/coder, unless otherwise necessary (e.g., when the one who is processing a folio is absent for more than a day).

3. In general, the editors assumed that the original entries are correct. Editing was done only when an entry is obviously incorrect. A doubtful or inconsistent item was verified in the field.

Response rate

Of the 43,020 eligible sample households for the 2008 APIS, 40,613 were successfully interviewed. This translated to a response rate of 94.4 percent at the national level. Households which were not interviewed either refused to be interviewed or were not available or were away during the enumeration period.

Sampling error estimates

Sampling errors have been calculated for the following variables: 1) Percentage of Families with Own or Ownerlike Possession of House and Lot they Occupy 2) Percentage of Families Living in Houses with Roof Made of Strong Materials 3) Percentage of Families Living in Houses with Outer Walls Made of Strong Materials 4) Percentage of Families with Electricity in the Building/House They Reside in 5) Percentage of Families with Access to Safe Water Supply 6) Percentage of Families with Sanitary Toilet 7) Percentage of Families with Children 6-12 Years Old in Elementary Grades 8) Percentage of Families with Children 13-16 Years Old in High School 9) Percentage of Families with Members 18 Years Old and Over Gainfully Employed 10) Percentage of Families with Working Children 5-17 Years Old 11) Average Family Income 12) Average Family Expenditure

Data appraisal

A series of data quality tables were generated to review the quality of the data and include the following: - Age distribution of the household population - Highest grade completed versus current grade - Highest grade completed versus age - Current grade versus age - Reason for not attending school versus highest grade completed - Reason for not attending school versus current grade - Marital status versus age - Consistency of income vs. expenditure

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 PH: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data was reported at 33.700 % in 2015. This records a decrease from the previous number of 38.700 % for 2012. Philippines PH: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data is updated yearly, averaging 43.100 % from Dec 1985 (Median) to 2015, with 11 observations. The data reached an all-time high of 57.600 % in 1985 and a record low of 33.700 % in 2015. Philippines PH: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank: Poverty. Poverty headcount ratio at $3.20 a day is the percentage of the population living on less than $3.20 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

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.

Abstract

This study is an impact evaluation of the KALAHI-CIDSS (KC) program. The impact evaluation's key research questions can be divided into the following four themes:

1. Socio-Economic Effects: Does KC increase household consumption? Does KC increase labor force participation?
2. Governance Effects: Does KC increase government leader responsiveness to community needs? Does KC reduce corruption and increase transparency?
3. Community Empowerment Effects: Does KC increase participation in local governance? Does KC increase collective action and contribution to local public goods?
4. Social Capital Effects: Does KC build groups and networks? In what ways are these networks applied? Does KC enhance trust?

In order to isolate KC's effects, a randomized control trial evaluation design was chosen. The impact evaluation sample consists of 198 municipalities (with 33 to 69 percent poverty incidence), spread over 26 provinces and 12 regions. The 198 municipalities were paired based on similar characteristics (99 pairs) and then randomly assigned into treatment and control groups through public lotteries. The sample size is large enough to be able to detect MCC's projected eight percent change in household income as well as other smaller effects. As part of the impact evaluation, baseline quantitative data were collected in the study area from April to July 2012. The quantitative data came from 5,940 household surveys in 198 barangays (one from each municipality) and 198 barangay surveys implemented in these same barangays

Geographic coverage

National coverage: The sample consists of 5,940 households in 198 barangays in 198 municipalities in 26 provinces in 12 regions. The sample is representative of the KALAHI-CIDSS target population across the nation.

Analysis unit

Individuals, households, community

Universe

The study population consists of barangays (villages) from the Philippines' poorest provinces. Survey respondent were barangay captains (village captains) and randomly selected households (30 randomly selected per barangay) from the sample of 198 barangays (villages).

Kind of data

Sample survey data [ssd]

Sampling procedure

The impact evaluation focuses on municipalities with between 33-69% poverty incidence. A total of 198 eligible municipalities were matched on poverty incidence, population, land area, and number of barangays. The paired municipalities were then randomly assigned into treatment and control groups through public lotteries. This resulted in the final sample of 198 municipalities (when determining the number of treatment and control municipalities, we used sample size of 30 households per municipality, ensuring an 8% (positive) change in income would be detectable at 95% significance and 80% power). The large number of municipalities included in the evaluation will provide a sufficient level of precision to estimate KC's impacts nationwide in municipalities with a poverty incidence between 33-69%. One barangay within each of the 198 municipalities participating in the evaluation was randomly chosen, with a weighted probability favoring barangays with the highest poverty rates. Within each municipality, IPA divided barangays into quintiles based on poverty and dropped the quintile with the lowest poverty incidence. For each municipality, the barangay to be surveyed for the sample was then randomly selected from the remaining barangays. Within each barangay, 30 households were randomly selected from among all households to comprise the household surveyed sample.

Sampling deviation

N/A

Research instrument

The baseline study included a barangay (village) questionnaire and a household questionnaire implemented in the following four different languages: Tagalog, Bisaya, Cebuano, llongo and llocano.

1. Household questionnaire: This questionnaire was composed of modules on education, labor income sources, household assets and amenities, expenditures, social networks, and other topics.

2. Barangay questionnaire: The barangay captains (village leaders were the principal respondents. The questionnaire collected data on the barangay's development projects, budget, demographics, the relationship between the existing barangay captain and its previous leadership, and other topics.

Cleaning operations

In the field, the field supervisor and data editor checked the questionnaires before the first data entry. The survey firm then conducted the second data entry in the main office and then checked the discrepancies between the first and the second data entry. The data cleaning process implemented by the survey firm included the following: 1. Naming and labelling the data 2. Checking the unique identifiers 3. Range checks and setting variable bounds 4. Check skip patterns and misisng data 5. Check logical consistency 6. Standardize string variable coding

After receiving the clean datasets from the survey firm, IPA conducted a second stage of data cleaning needed to construct variables for the analysis. This process involved carefully creating, summarizing and cross-checking key indicators.

Response rate

100 percent

Sampling error estimates

N/A

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.

Philippines Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 5.800 % in 2017. Philippines Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 5.800 % from Dec 2017 (Median) to 2017, with 1 observations. The data reached an all-time high of 5.800 % in 2017 and a record low of 5.800 % in 2017. Philippines Multidimensional Poverty Headcount Ratio: UNDP: % of total population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Social: Poverty and Inequality. The multidimensional poverty headcount ratio (UNDP) is the percentage of a population living in poverty according to UNDPs multidimensional poverty index. The index includes three dimensions -- health, education, and living standards.;Alkire, S., Kanagaratnam, U., and Suppa, N. (2023). ‘The global Multidimensional Poverty Index (MPI) 2023 country results and methodological note’, OPHI MPI Methodological Note 55, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. (https://ophi.org.uk/mpi-methodological-note-55-2/);;

Abstract

The KALAHI-CIDSS program was set up in 2002 to alleviate rural poverty in the Philippines. It provides resources to poor rural municipalities to invest in public goods and by reviving local institutions to enhance people’s participation in governance. The project targeted the poorest 25 percent of municipalities in each of the poorest 42 provinces. The government of the Philippines committed $82 million to the project, which was complemented by a $100 million loan from the World Bank. As of December 2010, the project had covered 4,583 barangays (villages) in 200 municipalities and supported 5,645 subprojects, worth Php 5.7 billion and benefiting about 1.26 million households.

The program's impact evaluation was designed in 2003 to evaluate general impacts on poverty reduction, social capital, empowerment, and governance. The team collected quantitative and qualitative data before, during, and after project implementation in a sample of KALAHI-CIDSS municipalities that received support ("treatment" municipalities) and from comparable municipalities that did not receive support ("control" municipalities). The quantitative baseline survey was carried out in September-October 2003, the quantitative midterm - in October-November 2006 and the quantitative endline survey - in February-March 2010.

Data were collected on a broad range of indicators: service delivery (access to health, education), poverty (employment, per capita consumption, self-rated poverty), empowerment and governance (group membership, participation in barangay assemblies, collective action). The quantitative sample includes 2,400 households in 135 barangays in 16 municipalities in 4 provinces.

Geographic coverage

Four provinces: Albay, Capiz, Zamboanga del Sur and Agusan del Sur

Analysis unit

• households,
• barangays (villages)

Kind of data

Sample survey data [ssd]

Sampling procedure

The quantitative sample includes 2,400 households in 135 barangays (villages) in 16 municipalities in 4 provinces. The team used cluster analysis to select two pairs of comparison and treatment municipalities in each of four provinces. The pairs with the best match were selected.

Sampling deviation

The sample size was reduced from 2,400 households during the baseline survey to a little less than 1,900 households during the endline survey, mostly due to migration and deaths. One of the original control municipalities in Albay (Malinao) ended up being included in the PODER project, a KALAHI-CIDSS-type program supported by the Spanish aid agency. As a result, baseline data had to be collected in a replacement control municipality (Oas).

Mode of data collection

Face-to-face [f2f]

In 2024, the total population of the Philippines was at approximately 114.17 million inhabitants. For the foreseeable future, the Filipino population is expected to increase slightly, despite a current overall downward trend in population growth. The dwindling Filipino population For now, the population figures in the Philippines still show a steady increase and the country is still one of the most densely populated countries in the Asia-Pacific region, however, all signs point to a decline in the number of inhabitants in the long run: Just like the population growth rate, the country’s fertility rate, for example, has also been decreasing for years now, while the death rate has been increasing simultaneously.   Poor healthcare to blame One of the reasons for the downward trend is the aging population; fewer babies are born each year, while life expectancy at birth has been steady over the years. Another reason is poor healthcare in the country: The Philippines have a high tuberculosis incidence rate, a highly infectious disease, and are among the countries with a high probability of death from noncommunicable diseases as well.

Abstract

The World Bank Group is interested in gauging the views of clients and partners who are either involved in development in the Philippines or who observe activities related to social and economic development. The World Bank Country Assessment Survey is meant to give the World Bank Group's team that works in the Philippines, greater insight into how the Group's work is perceived. This is one tool the World Bank Group uses to assess the views of its critical stakeholders. With this understanding, the World Bank Group hopes to develop more effective strategies, outreach and programs that support development in the Philippines.

The survey was designed to achieve the following objectives: - Assist the World Bank in gaining a better understanding of how stakeholders in the Philippines perceive the Bank; - Obtain systematic feedback from stakeholders in the Philippines regarding: · Their views regarding the general environment in the Philippines; · Their overall attitudes toward the World Bank in the Philippines; · Overall impressions of the World Bank's effectiveness and results, knowledge and research, and communication and information sharing in the Philippines; and · Perceptions of the World Bank's future role in the Philippines. - Use data to help inform the Philippines country team's strategy.

Geographic coverage

National

Analysis unit

Stakeholder

Universe

Stakeholders of the World Bank in Philippines

Kind of data

Sample survey data [ssd]

Sampling procedure

In October and November 2012, 1,536 stakeholders of the World Bank in the Philippines were invited to provide their opinions on the Bank's assistance to the country by participating in a country survey. Participants in the survey were drawn from among civil society organizations (NGOs, community-based organizations, faith-based organizations, academia/think tanks, and trade unions); donors (bilateral or multilateral development agencies); government (House of Representatives member or staff, Senate member or staff, judicial branch official or staff, local government unit officials or staff, national executive branch officials or staff, and project management units (PMUs) for a World Bank-supported project official or staff); government-owned corporation or financial institution official or staff; the media (press, radio, TV, web, etc.); and the private sector (banks/financial sector, private organizations or business, and consultants or contractors).

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The Questionnaire consists of 8 Sections:

A. Background Information: Respondents were asked to describe their current organization and identify their specialization, with which agency within the World Bank Group they primarily work, their exposure to the Bank in the Philippines, and their geographic location.

B. General Issues facing the Philippines: Respondents were asked to indicate whether the Philippines was headed in the right or wrong direction, the most important development priorities, and which areas would contribute most to reducing poverty and generating economic growth in the Philippines.

C. Overall Attitudes toward the World Bank: Respondents were asked to rate their familiarity with the World Bank (IBRD/IDA) and IFC, the World Bank and IFC’s effectiveness in the Philippines, World Bank and IFC staff preparedness, agreement with various statements regarding the Bank’s work, and the extent to which the Bank is an effective development partner. Respondents were also asked to indicate the sectoral areas on which it would be most productive for the Bank to focus its resources, the Bank’s greatest values and greatest weaknesses in its work, the most effective instruments in helping to reduce poverty in the Philippines, and how they attribute slow or failed reform efforts.

D. World Bank Effectiveness and Results: Respondents were asked to rate the Bank’s level of effectiveness across twelve key development areas in the Philippines, the extent to which the Bank’s work helps achieve sustainable development results in the Philippines, and the extent to which the World Bank Group meets the Philippines’ need for financial instruments, knowledge services, and financial products.

E. The World Bank’s Knowledge: Respondents were asked to indicate how frequently they consult Bank knowledge and research in the work they do and to rate the effectiveness and quality of the Bank’s knowledge and research, including how significant a contribution it makes to development results and its technical quality.

F. Working with the World Bank: Respondents were asked to rate their level of agreement with a series of statements regarding working with the Bank, such as the World Bank’s “Safeguard Policy” requirements being reasonable and the Bank disbursing funds promptly.

G. The Future Role of the World Bank in the Philippines: Respondents were asked to rate how significant a role the Bank should play in the Philippines’ development in the near future and how effectively the different agencies within the World Bank Group collaborate. Respondents were also asked to indicate what the Bank should do to make itself of greater value in the Philippines.

H. Communication and Information Sharing: Respondents were asked to indicate how they get information about economic and social development issues, how they prefer to receive information from the Bank, their access to the Internet, their usage and evaluation of the Bank’s websites, and their usage and evaluation of the Bank’s KDCs and online resource centers. Respondents were asked about their awareness of the Bank’s and IFC’s Access to Information policies, past information requests from the Bank, and their level of agreement that they use more data from the World Bank as a result of the Bank’s Open Data policy. Respondents were also asked to indicate their level of agreement that they know how to find information from the Bank and that the Bank is responsive to information requests.

Response rate

A total of 328 stakeholders participated in the country survey (21%).

The share of the urban population in the Philippines has continued to rise over the years. In 2022, the urban population accounted for roughly 48 percent of the entire population. In the Philippines, urbanized areas were primarily found in Metro Manila, located in the National Capital Region (NCR).

 Urban population growth in the Philippines

Urban areas in the Philippines have a high influx of people due to better infrastructure and employment opportunities available. From 2011 to 2015, the urban population growth rate was over two percent. However, from 2016 to 2020, the population growth rate decreased and has been at around 1.9 percent since the Philippine government introduced “Back to the Province” program to reduce overcrowding in Manila.

Lack of affordable housing in the urbanized areas in the Philippines

Poverty has been one of the reasons for slum dwellings in the Philippines. Despite better infrastructures in urban areas, there is also a lack of affordable housing for people living below the poverty level in urban areas. As a result, 43 percent of the urban population live in slums in the Philippines, one of the highest urban population living in slums across the Asia Pacific.

Abstract

This impact evaluation (IE) was designed to evaluate the integration of sanitation into the Pantawid Pamilyang Pilipino Program (4Ps), with an overall aim to test the effectiveness of a combination of hardware and financial subsidies to encourage adoption of improved sanitation facilities among the poorest households in rural areas of the Philippines. While the original research endeavored to measure health and nutrition outcomes, the final evaluation primarily focused on the upgrade and construction of latrines with the goal of achieving improved sanitation at the household level.

Geographic coverage

For this evaluation we included 17 municipalities in the provinces of Negros Oriental, Cebu and Bohol (Region 7), and Leyte and Eastern Samar (Region 8). We selected those municipalities based on the levels of poverty, open defecation, unimproved sanitation and inclusion in the ZOD coverage area.

Figure 11 of the annex to the survey report (see report under the resources tab) shows the geographic distribution of study barangays in one of the five study provinces (see Appendix for additional maps of other provinces).

Analysis unit

Household

Universe

All study participants were 4Ps beneficiaries, who are required to attend Family Development Sessions (FDS) that included a module on sanitation promotion.

Kind of data

Sample survey data [ssd]

Sampling procedure

In each barangay, we used the National Household Targeting System (NHTS-PR) list to randomly select 15 4Ps beneficiary households to be included in our sample. At baseline 4,080 households from 272 sample barangays were interviewed. The sample was reduced at the endline survey by 30% due to budget constraints. Therefore, the endline survey included 2,849 households from 190 barangays. Additional sampling details are provided in the Study Design Annex of the report provided under the resources tab.

Sampling deviation

A small number of barangays were replaced due to safety concerns.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Baseline and questionnaires are structured and provided in English as related resources. The questionnaire differed at baseline and endline, based on feedback from 4P project staff for reasons provided in the data appraisal section.

Response rate

At endline 2,849 study households were sampled, but 154 baseline households were not reached, because their members had permanently moved to another location or the household refused to participate in the endline survey. This represents a response rate of 95%.

Data appraisal

Data quality was checked using spot-checks and back-checks.

The IE originally intended to look at child diarrhea prevalence as a secondary outcome. However due to the shift in the focus of the evaluation and the different methodology of collecting the child health roster in each survey round, diarrhea was dropped as an outcome of the study. At baseline households were asked about each child’s health status separately, while at endline households were asked about the aggregate incidence of diarrhea and other symptoms for all children under 5 years old in the household. This prevents us from supplementing the definition of child diarrhea using multiple symptoms, like blood or mucus in the stool. This is important as some primary caregivers are unable to accurately diagnose diarrhea in children. Additionally, our statistical power reduces by almost two thirds, because the number individual data points goes down from 2246 at baseline to 831 at endline.

Additional information on data appraisal is provided in the Study Design annex of the survey report provided under the resources tab.

Abstract

The 2003 Family Income and Expenditure Survey (FIES) had the following primary objectives:

1) to gather data on family income and family expenditure and related information affecting income and expenditure levels and patterns in the Philippines;

2) to determine the sources of income and income distribution, levels of living and spending patterns, and the degree of inequality among families;

3) to provide benchmark information to update weights for the estimation of consumer price index; and

4) to provide information for the estimation of the country's poverty threshold and incidence.

Geographic coverage

National coverage

Analysis unit

Household Consumption expenditure item Income by source

Universe

The 2003 FIES has as its target population, all households and members of households nationwide. A household is defined as an aggregate of persons, generally but not necessarily bound by ties of kinship, who live together under the same roof and eat together or share in common the household food. Household membership comprises the head of the household, relatives living with him such as his/her spouse, children, parent, brother/sister, son-in-law/daughter-in-law, grandson/granddaughter and other relatives. Household membership likewise includes boarders, domestic helpers and non-relatives. A person who lives alone is considered a separate household.

Institutional population is not within the scope of the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2003 MS considers the country's 17 administrative regions as defined in Executive Orders (EO) 36 and 131 as the sampling domains. A domain is referred to as a subdivision of the country for which estimates with adequate level of precision are generated. It must be noted that while there is demand for data at the provincial level (and to some extent municipal and barangay levels), the provinces were not treated as sampling domains because there are more than 80 provinces which would entail a large resource requirement. Below are the 17 administrative regions of the country:

National Capital Region Cordillera Administrative Region Region I - Ilocos Region II - Cagayan Valley Region III - Central Luzon Region IVA - CALABARZON Region IVB - MIMAROPA Region V - Bicol Region VI - Western Visayas Region VII - Central Visayas Region VIII - Eastern Visayas Region IX - Zamboanga Peninsula Region X - Northern Mindanao Region XI - Davao Region XII - SOCCSKSARGEN Region XIII - Caraga Autonomous Region in Muslim Mindanao

As in most household surveys, the 2003 MS made use of an area sample design. For this purpose, the Enumeration Area Reference File (EARF) of the 2000 Census of Population and Housing (CPH) was utilized as sampling frame. The EARF contains the number of households by enumeration area (EA) in each barangay.

This frame was used to form the primary sampling units (PSUs). With consideration of the period for which the 2003 MS will be in use, the PSUs were formed/defined as a barangay or a combination of barangays with at least 500 households.

The 2003 MS considers the 17 regions of the country as the primary strata. Within each region, further stratification was performed using geographic groupings such as provinces, highly urbanized cities (HUCs), and independent component cities (ICCs). Within each of these substrata formed within regions, the PSUs were further stratified, to the extent possible, using the proportion of strong houses (PSTRONG), indicator of engagement in agriculture of the area (AGRI), and a measure of per capita income (PERCAPITA) as stratification factors.

The 2003 MS consists of a sample of 2,835 PSUs. The entire MS was divided into four sub-samples or independent replicates, such as a quarter sample contains one fourth of the total PSUs; a half sample contains one-half of the four sub-samples or equivalent to all PSUs in two replicates.

The final number of sample PSUs for each domain was determined by first classifying PSUs as either self-representing (SR) or non-self-representing (NSR). In addition, to facilitate the selection of sub-samples, the total number of NSR PSUs in each region was adjusted to make it a multiple of 4.

SR PSUs refers to a very large PSU in the region/domain with a selection probability of approximately 1 or higher and is outright included in the MS; it is properly treated as a stratum; also known as certainty PSU. NSR PSUs refers to a regular too small sized PSU in a region/domain; also known as non-certainty PSU. The 2003 MS consists of 330 certainty PSUs and 2,505 non-certainty PSUs.

To have some control over the sub-sample size, the PSUs were selected with probability proportional to some estimated measure of size. The size measure refers to the total number of households from the 2000 CPH. Because of the wide variation in PSU sizes, PSUs with selection probabilities greater than 1 were identified and were included in the sample as certainty selections.

At the second stage, enumeration areas (EAs) were selected within sampled PSUs, and at the third stage, housing units were selected within sampled EAs. Generally, all households in sampled housing units were enumerated, except for few cases when the number of households in a housing unit exceeds three. In which case, a sample of three households in a sampled housing unit was selected at random with equal probability.

An EA is defined as an area with discernable boundaries within barangays consisting of about 150 contiguous households. These EAs were identified during the 2000 CPH. A housing unit, on the other hand, is a structurally separate and independent place of abode which, by the way it has been constructed, converted, or arranged, is intended for habitation by a household.

The 2003 FIES involved the interview of a national sample of about 51,000 sample households deemed sufficient to gather data on family income and family expenditure and related information affecting income and expenditure levels and patterns in the Philippines at the national and regional level. The sample households covered in the survey were the same households interviewed in the July 2003 and January 2004 round of the LFS.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The 2003 FIES questionnaire contains about 800 data items and a summary for comparing income and expenditures. The questionnaires were subjected to a rigorous manual and machine edit checks for completeness, arithmetic accuracy, range validity and internal consistency.

The major steps in the machine processing are as follows: 1. Data Entry 2. Completeness Check 3. Matching of visit records 4. Consistency and Macro Edit (Big Edit) 5. Generation of the Public Use File 6. Tabulation

Steps 1 to 2 were done right after each visit. The remaining steps were carried out only after the second visit had been completed.

Steps 1 to 4 were done at the Regional Office while Steps 5 and 6 were completed in the Central Office.

After completing Steps 1 to 4, data files were transmitted to the Central Office where a summary file was generated. The summary file was used to produce the consistency tables as well as the preliminary and textual tables.

When the generated tables showed inconsistencies, selected data items were subjected to further scrutiny and validation. The cycle of generation of consistency tables and data validation were done until questionable data items were verified.

The FAME (FIES computer-Aided Consistency and Macro Editing), an interactive Windows-based application system was used in data processing. This system was used starting with the 2000 FIES round. The interactive module of FAME enabled the following activities to be done simultaneously. a) Matching of visit records b) Consistency and macro edit (big edit) c) Range check

The improved system minimized processing time as well as minimized, if not eliminated, the need for paper to generate the reject listing.

Note: For data entry, CSPro Version 2.6 was used.

Response rate

The response rate for this survey is 95.7%. The response rate is the ratio of the total responding households to the total number of eligible households. Eligible households include households who were completely interviewed, refused to be interviewed or were temporarily away or not at home or on vacation during the survey period.

Sampling error estimates

As in all surveys, two types of non-response were encountered in the 2003 FIES: interview non-response and item non-response. Interview non-response refers to a sample household that could not be interviewed. Since the survey requires that the sample households be interviewed in both visits, households that transferred to another dwelling unit, temporarily away, on vacation, not at home, household unit demolished, destroyed by fire/typhoon and refusal to be interviewed in the second visit contributed to the number of interview non-response cases.

Item non-response, or the failure to obtain responses to particular survey items, resulted from factors such as respondents being unaware of the answer to a particular question, unwilling to provide the requested information or ENs’ omission of questions during the interview. Deterministic imputation was done to address item nonresponse. This imputation is a process in which proper entry for a particular missing item was deduced from other items of the questionnaire where the non-response item was observed. Notes and remarks indicated in the questionnaire were likewise used as basis for imputation.

Data appraisal

Refer to the