The total population in Philippines was estimated at 112.9 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - Philippines Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Philippines Population: National Capital Region (NCR) data was reported at 12,877.253 Person th in 2015. This records an increase from the previous number of 11,855.975 Person th for 2010. Philippines Population: National Capital Region (NCR) data is updated yearly, averaging 9,693.300 Person th from Dec 1975 (Median) to 2015, with 8 observations. The data reached an all-time high of 12,877.253 Person th in 2015 and a record low of 4,970.006 Person th in 1975. Philippines 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.G001: Population: Census 2010.

Abstract

The 2022 Philippines National Demographic and Health Survey (NDHS) was implemented by the Philippine Statistics Authority (PSA). Data collection took place from May 2 to June 22, 2022.

The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, fertility preferences, family planning practices, childhood mortality, maternal and child health, nutrition, knowledge and attitudes regarding HIV/AIDS, violence against women, child discipline, early childhood development, and other health issues.

The information collected through the NDHS is intended to assist policymakers and program managers in designing and evaluating programs and strategies for improving the health of the country’s population. The 2022 NDHS also provides indicators anchored to the attainment of the Sustainable Development Goals (SDGs) and the new Philippine Development Plan for 2023 to 2028.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling scheme provides data representative of the country as a whole, for urban and rural areas separately, and for each of the country’s administrative regions. The sample selection methodology for the 2022 NDHS was based on a two-stage stratified sample design using the Master Sample Frame (MSF) designed and compiled by the PSA. The MSF was constructed based on the listing of households from the 2010 Census of Population and Housing and updated based on the listing of households from the 2015 Census of Population. The first stage involved a systematic selection of 1,247 primary sampling units (PSUs) distributed by province or HUC. A PSU can be a barangay, a portion of a large barangay, or two or more adjacent small barangays.

In the second stage, an equal take of either 22 or 29 sample housing units were selected from each sampled PSU using systematic random sampling. In situations where a housing unit contained one to three households, all households were interviewed. In the rare situation where a housing unit contained more than three households, no more than three households were interviewed. The survey interviewers were instructed to interview only the preselected housing units. No replacements and no changes of the preselected housing units were allowed in the implementing stage in order to prevent bias. Survey weights were calculated, added to the data file, and applied so that weighted results are representative estimates of indicators at the regional and national levels.

All women age 15–49 who were either usual residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. Among women eligible for an individual interview, one woman per household was selected for a module on women’s safety.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires were used for the 2022 NDHS: the Household Questionnaire and the Woman’s Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to the Philippines. Input was solicited from various stakeholders representing government agencies, academe, and international agencies. The survey protocol was reviewed by the ICF Institutional Review Board.

After all questionnaires were finalized in English, they were translated into six major languages: Tagalog, Cebuano, Ilocano, Bikol, Hiligaynon, and Waray. The Household and Woman’s Questionnaires were programmed into tablet computers to allow for computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the languages for each questionnaire.

Cleaning operations

Processing the 2022 NDHS data began almost as soon as fieldwork started, and data security procedures were in place in accordance with confidentiality of information as provided by Philippine laws. As data collection was completed in each PSU or cluster, all electronic data files were transferred securely via SyncCloud to a server maintained by the PSA Central Office in Quezon City. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors while still in the area of assignment. Timely generation of field check tables allowed for effective monitoring of fieldwork, including tracking questionnaire completion rates. Only the field teams, project managers, and NDHS supervisors in the provincial, regional, and central offices were given access to the CAPI system and the SyncCloud server.

A team of secondary editors in the PSA Central Office carried out secondary editing, which involved resolving inconsistencies and recoding “other” responses; the former was conducted during data collection, and the latter was conducted following the completion of the fieldwork. Data editing was performed using the CSPro software package. The secondary editing of the data was completed in August 2022. The final cleaning of the data set was carried out by data processing specialists from The DHS Program in September 2022.

Response rate

A total of 35,470 households were selected for the 2022 NDHS sample, of which 30,621 were found to be occupied. Of the occupied households, 30,372 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 28,379 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 27,821 women, yielding a response rate of 98%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Philippines National Demographic and Health Survey (2022 NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 NDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 NDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Population pyramid
• Five-year mortality rates

See details of the data quality tables in Appendix C of the final report.

The geodatabase contains boundaries for the national and first-, second-, third-, and fourth-order administrative divisions, aligned to the Large Scale International Boundaries dataset from the U.S. Department of State. The feature classes are suitable for linking to the attribute data provided.

The tabular data contain total population for 2020 (census), as well as five-year age group and sex, and information relating to ethnicity, citizenship, religion, households, housing units, and overseas workers.

Philippines PH: Population: Growth data was reported at 1.535 % in 2017. This records a decrease from the previous number of 1.564 % for 2016. Philippines PH: Population: Growth data is updated yearly, averaging 2.616 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 3.345 % in 1960 and a record low of 1.535 % in 2017. Philippines PH: Population: Growth 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: Population and Urbanization Statistics. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2017 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Total Population by Single-Year Age, Sex, Region, Province, City/Municipality and Barangay (admin 4) Census 2020. It uses the new 10-digit pcode consistent with Philippines - Subnational Administrative Boundaries.

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Philippines data available from WorldPop here.

Philippines Population Density: National Capital Region (NCR) data was reported at 20,785.000 Person/sq km in 2015. This records an increase from the previous number of 19,137.000 Person/sq km for 2010. Philippines Population Density: National Capital Region (NCR) data is updated yearly, averaging 15,645.887 Person/sq km from Dec 1975 (Median) to 2015, with 8 observations. The data reached an all-time high of 20,785.000 Person/sq km in 2015 and a record low of 7,847.791 Person/sq km in 1975. Philippines Population Density: 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.G005: Population Density.

Abstract

The 2017 Philippines National Demographic and Health Survey (NDHS 2017) is a nationwide survey with a nationally representative sample of approximately 30,832 housing units. The primary objective of the survey is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS 2017 collected information on marriage, fertility levels, fertility preferences, awareness and use of family planning methods, breastfeeding, maternal and child health, child mortality, awareness and behavior regarding HIV/AIDS, women’s empowerment, domestic violence, and other health-related issues such as smoking.

The information collected through the NDHS 2017 is intended to assist policymakers and program managers in the Department of Health (DOH) and other organizations in designing and evaluating programs and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents) and all women age 15-49 years resident in the sample household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling scheme provides data representative of the country as a whole, for urban and rural areas separately, and for each of the country’s administrative regions. The sample selection methodology for the NDHS 2017 is based on a two-stage stratified sample design using the Master Sample Frame (MSF), designed and compiled by the PSA. The MSF is constructed based on the results of the 2010 Census of Population and Housing and updated based on the 2015 Census of Population. The first stage involved a systematic selection of 1,250 primary sampling units (PSUs) distributed by province or HUC. A PSU can be a barangay, a portion of a large barangay, or two or more adjacent small barangays.

In the second stage, an equal take of either 20 or 26 sample housing units were selected from each sampled PSU using systematic random sampling. In situations where a housing unit contained one to three households, all households were interviewed. In the rare situation where a housing unit contained more than three households, no more than three households were interviewed. The survey interviewers were instructed to interview only the pre-selected housing units. No replacements and no changes of the preselected housing units were allowed in the implementing stage in order to prevent bias. Survey weights were calculated, added to the data file, and applied so that weighted results are representative estimates of indicators at the regional and national levels.

All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. Among women eligible for an individual interview, one woman per household was selected for a module on domestic violence.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used for the NDHS 2017: the Household Questionnaire and the Woman’s Questionnaire. Both questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to the Philippines. Input was solicited from various stakeholders representing government agencies, universities, and international agencies.

Cleaning operations

The processing of the NDHS 2017 data began almost as soon as fieldwork started. As data collection was completed in each PSU, all electronic data files were transferred via an Internet file streaming system (IFSS) to the PSA central office in Quezon City. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors while still in the PSU. Secondary editing involved resolving inconsistencies and the coding of openended questions; the former was carried out in the central office by a senior data processor, while the latter was taken on by regional coordinators and central office staff during a 5-day workshop following the completion of the fieldwork. Data editing was carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage, because it maximized the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for more effective monitoring. The secondary editing of the data was completed by November 2017. The final cleaning of the data set was carried out by data processing specialists from The DHS Program by the end of December 2017.

Response rate

A total of 31,791 households were selected for the sample, of which 27,855 were occupied. Of the occupied households, 27,496 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 25,690 women age 15-49 were identified for individual interviews; interviews were completed with 25,074 women, yielding a response rate of 98%.

The household response rate is slightly lower in urban areas than in rural areas (98% and 99%, respectively); however, there is no difference by urban-rural residence in response rates among women (98% for each).

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the Philippines National Demographic and Health Survey (NDHS) 2017 to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the NDHS 2017 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the NDHS 2017 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

See details of the data quality tables in Appendix C of the survey final report.

Number of persons with disabilities by type of Disability and by region from national census 2010

The dataset is based on Statistics Canada census data spanning four census periods (2001, 2006, 2016, and 2021). The dataset captures population statistics disaggregated by ethnicity at the Dissemination Area (DA) level—the smallest standard geographic unit for census data dissemination, covering approximately 400-700 people per unit. For Toronto, this encompasses approximately 3,700 DAs, providing high spatial resolution for analyzing urban dynamics. The dataset includes detailed population counts for the five largest ethnic groups in Toronto: China, India, Philippines, Portugal, and Sri Lanka. The features are also extracted from census datasets and 298 socioeconomic and demographic features from the census data, organized into 12 categories:Demographics: Population age structure, household composition, and family sizeHousing: Dwelling types, ownership status, housing values, and maintenance needsFamily Structure: Marriage patterns, presence of children, household typesIncome: Median household and individual income, income sourcesEmployment: Labor force participation, employment/unemployment ratesMobility & Migration: Internal and external migration patterns, non-permanent residentsVisible Minorities: Population distribution by visible minority statusLanguage: Official language use, mother tongue, and multilingual capabilitiesOccupation: Employment categories across economic sectorsReligion: Religious affiliations and practicesIndustry: Distribution across industry sectorsPlace of Birth: Country of origin information

Canada was home to almost 4 million individuals who identified themselves as visible minorities in 2001, accounting for 13.4% of the total population. The proportion of the visible minority population has increased steadily over the past 20 years. In 1981, 1.1 million visible minorities accounted for 4.7% of the total population; by 1996, 3.2 million accounted for 11.2%.

Unemployment Rate in Philippines increased to 5.30 percent in July from 3.70 percent in June of 2025. This dataset provides - Philippines Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The 2006 Census estimated 5.1 million individuals who belonged to a visible minority. The Employment Equity Act defines visible minorities as 'persons, other than Aboriginal peoples, who are non-Caucasian in race or non-white in colour’. The visible minority population has grown steadily over the last 25 years. In 1981, when data for the four Employment Equity designated groups were first derived, the estimated 1.1 million visible minorities represented 4.7% of Canada's total population. In 1991, 2.5 million people were members of the visible minority population, 9.4% of the total population. The visible minority population further increased to 3.2 million in 1996, or 11.2% of the total population. By 2001, their numbers had reached an estimated 3.9 million or 13.4% of the total population. In 2006, the visible minorities accounted for 16.2% of Canada’s total population. This map shows the percentage of visible minorities (Filipino population) by census subdivisions.

This study was originally conducted through the Population Institute at the University of the Philippines, Bureau of the Census and Statistics as a part of the National Demographic Survey in 1973. Later, Donald Treiman from the Sociology department at UCLA obtained the data and coded the responses with prestige scores to examine the respondents' occupations . The final results of the study were one of the many data sets used to construct an International Socio-Economic Index of occupational status (ISEI), comparing social and economic mobility patterns in various nations.

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Philippines PH: Population: Total data was reported at 104,918,090.000 Person in 2017. This records an increase from the previous number of 103,320,222.000 Person for 2016. Philippines PH: Population: Total data is updated yearly, averaging 59,618,536.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 104,918,090.000 Person in 2017 and a record low of 26,273,025.000 Person in 1960. Philippines PH: Population: Total 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: Population and Urbanization Statistics. Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Sum; Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis.

Philippines PH: Fertility Rate: Total: Births per Woman data was reported at 2.925 Ratio in 2016. This records a decrease from the previous number of 2.958 Ratio for 2015. Philippines PH: Fertility Rate: Total: Births per Woman data is updated yearly, averaging 4.476 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 7.148 Ratio in 1960 and a record low of 2.925 Ratio in 2016. Philippines PH: Fertility Rate: Total: Births per Woman 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: Health Statistics. Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average; Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children.