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.

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.

Philippines Magnitude of Poor Population: National Capital Region (NCR) data was reported at 494,630.000 Person in 2015. This records an increase from the previous number of 460,831.000 Person for 2012. Philippines Magnitude of Poor Population: National Capital Region (NCR) data is updated yearly, averaging 563,179.000 Person from Dec 1988 (Median) to 2015, with 10 observations. The data reached an all-time high of 1,909,886.000 Person in 1988 and a record low of 346,747.000 Person in 2003. Philippines Magnitude of Poor Population: 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.

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.

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: 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.

Preliminary figures for 2021 suggest that the poverty incidence among farmers in the Philippines was at 30 percent, a significant decrease from the 2015 values. Across the country, palay farms in CALABARZON had the highest average daily wage rate in 2019.

Based on Republic Act 8425, otherwise known as Social Reform and Poverty Alleviation Act, dated 11 December 1997, the poor refers to individuals and families whose income fall below the poverty threshold as defined by the government and/or those that cannot afford in a sustained manner to provide their basic needs of food, health, education, housing and other amenities of life. It may be estimated in terms of percentages (poverty incidence) and total number of poor families (magnitude of poor families). Also, this dataset has been generated by combining Philippine Standard Geographic Codes (PSGC) and poverty estimates from Philippine Statistics Authority (PSA).

For more details, please refer to the following documents:

https://psa.gov.ph/poverty-press-releases/references https://psa.gov.ph/poverty-press-releases/technotes https://psa.gov.ph/poverty-press-releases/glossary https://psa.gov.ph/sites/default/files/Technical%20Notes%20on%202015%20SAE.pdf

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.

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.

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

Philippines Incidence of Poor Families: Cordillera Administrative Region (CAR) data was reported at 14.900 % in 2015. This records a decrease from the previous number of 17.500 % for 2012. Philippines Incidence of Poor Families: Cordillera Administrative Region (CAR) data is updated yearly, averaging 25.950 % from Dec 1988 (Median) to 2015, with 10 observations. The data reached an all-time high of 51.000 % in 1994 and a record low of 14.900 % in 2015. Philippines Incidence of Poor Families: 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.

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Philippines PH: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data was reported at 64.200 % in 2015. This records a decrease from the previous number of 67.000 % for 2012. Philippines PH: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data is updated yearly, averaging 69.200 % from Dec 1985 (Median) to 2015, with 11 observations. The data reached an all-time high of 82.700 % in 1985 and a record low of 64.200 % in 2015. Philippines PH: Poverty Headcount Ratio at $5.50 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.WDI: Poverty. Poverty headcount ratio at $5.50 a day is the percentage of the population living on less than $5.50 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.

Philippines PH: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 11.700 % in 2021. This records a decrease from the previous number of 12.900 % for 2018. Philippines PH: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 15.850 % from Dec 2000 (Median) to 2021, with 8 observations. The data reached an all-time high of 17.100 % in 2006 and a record low of 11.700 % in 2021. Philippines PH: Proportion of People Living Below 50 Percent Of Median Income: % 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 percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. 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.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Philippines PH: Poverty Gap at $5.50 a Day: 2011 PPP: % data was reported at 26.600 % in 2015. This records a decrease from the previous number of 29.800 % for 2012. Philippines PH: Poverty Gap at $5.50 a Day: 2011 PPP: % data is updated yearly, averaging 32.700 % from Dec 1985 (Median) to 2015, with 11 observations. The data reached an all-time high of 43.200 % in 1985 and a record low of 26.600 % in 2015. Philippines PH: Poverty Gap at $5.50 a Day: 2011 PPP: % 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 gap at $5.50 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $5.50 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.; ; 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.

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

The 2000 Family Income and Expenditute Survey had the following objectives:

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

1. t o determine the sources of income and income distribution, levels of living and spending patterns, and the degree of inequality among families;

2. to provide benchmark information to update weights in the estimation of consumer price index (CPI); and

3. to provide inputs in 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 2000 FIES has as its target population, all households and members of households nationwide. Institutional population is not within the scope of the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design of the 2000 FIES adopted that of the Integrated Survey of Households (ISH). Starting July 1996, the sampling design of the ISH uses the new master sample design. The multi-stage sampling design of the master sample consists of 3,416 sample barangays in the expanded sample for provincial level estimates with a sub-sample of 2,247 Primary Sampling Units (PSUs) designated as core master sample for regional level estimates. The 2000 FIES was based on the expanded sample.

1. Domains: The domains for the new master sample are similar to that of the previous ISH design with an addition of 23 newly created domains. The urban and rural areas of cities and municipalities with a population of 150,000 or more are considered as separate domains. The other urban and rural areas in each of the 77 provinces are likewise treated as separate domains. In view of the creation of ARMM and the separation of Marawi City and Cotabato City from Lanao del Sur and Maguindanao, respectively, the urban and rural areas of the two cities also form separate domains.

2. Sampling Units: The multi-stage sampling design of the master sample involves the selection of the sample barangays for the first stage, selection of sample enumeration areas for the second stage, and the selection of sample households for the third stage in each stratum for every domain.

The frame for the first and second stages of sample selection was based mainly on the results of the 1995 Census of Population (POPCEN). The 1995 POPCEN list of barangays with the household and population counts is used in the first stage of sample selection. The stratification of barangays included in the frame, however, are based on the 1990 Census of Population and Housing (CPH) and other administrative reports from field offices of the NSO. An enumeration area (EA) is a physical delineated portion of the barangay. For barangays that were not divided into EAs, the barangay was treated as an EA.

The enumeration areas which constitute the secondary stage sampling units are those that were formed during the 1995 POPCEN. The sample barangays were selected systematically with probability proportional to size from the list of barangays that were implicitly stratified.

Isolated barangays and/or barangays that are difficult and expensive to reach are excluded from the sampling frame. However, critical areas or barangays with peace and order problem, which is generally temporary in nature, are included in the frame.

The frame for the third stage of sample selection is the list of the households from the 1995 POPCEN. The selection of sample household for the third stage was done systematically from the 1995 POPCEN List of the Households.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire has four main parts consisting of the following: Part I. Identification and Other Information (Geographic Identification, Other Information and Particulars about the Family)

Part II. Expenditures Section A. Food, Alcoholic Beverages and Tobacco Section B. Fuel, Light and Water, Transportation and Communication, Household Operations Section C. Personal Care and Effects, Clothing Footwear and Other Wear Section D. Education, Recreation, and Medical Care Section E. Furnishings and Equipment Section F. Taxes Section G. Housing, House Maintenance and Minor Repairs Section H. Miscellaneous Expenditures Section I. Other Disbursements

Part III. Income Section A. Salaries and Wages from Employment Section B. Net Share of Crops, Fruits and Vegetables Produced and/or Livestock and Poultry Raised by Other Households Section C. Other Sources of Income Section D. Other Receipts Section F. Family Sustenance Activities

Part IV. Entrepreneurial Activities Section A1. Crop Farming and Gardening Section A2. Livestock and Poultry Section A3. Fishing Section A4. Forestry and hunting Section A5. Wholesale and Retail Section A6. Manufacturing Section A7. Community, Social, Recreational and Personal Services Section A8. Transportation, Storage and Communication Services Section A9. Mining and Quarrying Section A10. Construction Section A11. Entrepreneurial Activities Not Elsewhere Classified

A guide for comparing disbursements against receipts is found on the last page.

The general design of the questionnaire also includes codes inside the box usually located at the top of the framed questions. These codes are for automatic data processing purposes. Ignore them during the interview process. Take note that the paging of the questionnaire is located outside the frame on each page.

Cleaning operations

The 2000 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. Structural, Range Edit and Consistency Edit (Minor Edit) 3. Completeness Check 4. Matching of visit records 5. Generation of the Binary file 6. Consistency and Macro Edit (Big Edit) 7. Expansion 8. Tabulation 9. Generation of CPI 10. Variance Analysis 11. Generation of the Public Use File (PUF)

Steps 1 to 3 were done right after each visit. The remaining steps were carried out only after the second visit had been completed.

Steps 1 to 6 were done at the Regional Office where Steps 4-6 were accomplished only after finishing the second visit. Steps 7 to 11 were completed in the Central Office.

After completing Steps 1 to 6, 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.

Where 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.

Innovations for the 2000 FIES machine processing were carried out by the Information Technology System and Research Division of the NSO by introducing the FIES Integrated Processing System (FIPS). This is a Windows application system which facilitated data encoding, completeness and validity check.

The 2000 FIES machine processing was further enhanced using an interactive Windows-based system named FAME (FIES computer-Aided Consistency and Macro Editing). The interactive module of FAME enabled the following activities to be done simultaneously: a) Matching of visit records b) Generation of Binary files c) Consistency and Macro Edit (Big Edit) d) Range Check

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

Response rate

The response rate for the 2000 FIES is 96.6%

Philippines PH: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data was reported at 1.415 % in 2015. This records an increase from the previous number of 1.294 % for 2012. Philippines PH: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data is updated yearly, averaging 0.975 % from Dec 1997 (Median) to 2015, with 7 observations. The data reached an all-time high of 1.415 % in 2015 and a record low of 0.613 % in 2000. Philippines PH: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % 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. Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure, expressed as a percentage of a total population of a country; ; Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017.; Weighted Average;

The province of Cebu topped the ranking of the wealthiest provinces in the Philippines, with assets amounting to approximately 310 billion Philippine pesos in 2023. Following by a large margin were the provinces of Rizal and Camarines Sur.