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 Hansen Global Forest Change version 1.7 datasets generated during and/or analysed during the current study are available in the earth engine partner’s website repository http://earthenginepartners.appspot.com/science-2013-global-forest. The datasets were developed by Hansen et al. (2013) in their paper "High-resolution global maps of 21st-century forest cover change". Science, 342 (6160), 850-853. https://doi.org/10.1126/science.1244693

The census of population in the Philippines, including the project populations, used in this study can be retrieved from the Philippine Statistics Authority (PSA) website https://psa.gov.ph/statistics/census/projected-population

The datasets were processed using an open source GIS software (QGIS version 3.16 Hannover) which can be downloaded from the QGIS website https://www.qgis.org/en/site/.

Context

The Philippine Statistics Authority (PSA) spearheads the conduct of the Family Income and Expenditure Survey (FIES) nationwide. The survey, which is undertaken every three (3) years, is aimed at providing data on family income and expenditure, including, among others, levels of consumption by item of expenditure, sources of income in cash, and related information affecting income and expenditure levels and patterns in the Philippines.

Content

Inside this data set is some selected variables from the latest Family Income and Expenditure Survey (FIES) in the Philippines. It contains more than 40k observations and 60 variables which is primarily comprised of the household income and expenditures of that specific household

Acknowledgements

The Philippine Statistics Authority for providing the publisher with their raw data

Inspiration

Socio-economic classification models in the Philippines has been very problematic. In fact, not one SEC model has been widely accepted. Government bodies uses their own SEC models and private research entities uses their own. We all know that household income is the greatest indicator of one's socio-economic classification that's why the publisher would like to find out the following:

1) Best model in predicting household income 2) Key drivers of household income, we want to make the model as sparse as possible 3) Some exploratory analysis in the data would also be useful

Employment: HWPW: With a Job, Not at Work data was reported at 450.000 Person th in Apr 2018. This records a decrease from the previous number of 512.000 Person th for Jan 2018. Employment: HWPW: With a Job, Not at Work data is updated quarterly, averaging 424.000 Person th from Jul 2003 (Median) to Apr 2018, with 60 observations. The data reached an all-time high of 1,121.000 Person th in Apr 2004 and a record low of 223.000 Person th in Jul 2007. Employment: HWPW: With a Job, Not at Work data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G009: Labour Force Survey: Employment: By Region, Age and Hours Worked.

These datasets are derived from the boundaries of the Barangays as observed at the end of April 2016 as per the Philippine Geographic Standard Code (PSGC) dataset. It has been generated on the basis of the layer created by the Philippine Statistics Authority (PSA) in the context of the 2015 population census. These datasets have been vetted by staff at The Carl Vinson Institute of Government's Office of Information Technology Outreach Services (ITOS) according to their COD assessment protocol found in the COD Technical Support Package (https://sites.google.com/site/commonoperationaldataset/geodata-preparation-manual/itos-process).
Acknowledge PSA and NAMRIA as the sources. LMB is still the source of official administrative boundaries of the Philippines. In the absence of available official administrative boundary, the IMTWG have agreed to clean and use the PSA administrative boundaries which are used to facilitate data collection of surveys and censuses. The dataset can only be considered as indicative boundaries and not official. * For administrative level 4 (Barangay) please contact the contributor (OCHA Philippines) via this page. This COD replaces https://data.humdata.org/dataset/philippines-administrative-boundaries

Philippines administrative levels:

(0) Country

(1) Region (Filipino: rehiyon)

(2) Provinces (Filipino: lalawigan, probinsiya) and independent cities (Filipino: lungsod, siyudad/ciudad, dakbayan, lakanbalen)

(3) Municipalities (Filipino: bayan, balen, bungto, banwa, ili) and component cities (Filipino: lungsod, siyudad/ciudad, dakbayan, dakbanwa, lakanbalen)

These shapefiles are suitable for database or ArcGIS joins to the sex and age disaggregated population statistics found on HDX here.

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.

Philippines Employment: Professionals data was reported at 2,726.000 Person th in Feb 2025. This records a decrease from the previous number of 2,762.000 Person th for Jan 2025. Philippines Employment: Professionals data is updated monthly, averaging 2,650.000 Person th from Jan 2021 (Median) to Feb 2025, with 50 observations. The data reached an all-time high of 3,019.000 Person th in Sep 2024 and a record low of 2,053.000 Person th in Aug 2021. Philippines Employment: Professionals data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G025: Labour Force Survey: Employment: by Industry, Occupation and Class.

Philippines Employment: Services: Professional, Scientific & Technical Act data was reported at 290.000 Person th in Oct 2018. This records an increase from the previous number of 280.000 Person th for Jul 2018. Philippines Employment: Services: Professional, Scientific & Technical Act data is updated quarterly, averaging 213.000 Person th from Jan 2012 (Median) to Oct 2018, with 28 observations. The data reached an all-time high of 300.000 Person th in Apr 2018 and a record low of 175.000 Person th in Jan 2013. Philippines Employment: Services: Professional, Scientific & Technical Act data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G013: Labour Force Survey: Employment: by Industry, Occupation and Class.

This dataset has been generated by combining Philippine Standard Geographic Codes (PSGC) and poverty estimates from Philippine Statistics Authority (PSA).

Employment: Industry: Water Supply, Sewerage, Waste & Remed data was reported at 75.000 Person th in Jul 2018. This records an increase from the previous number of 65.000 Person th for Apr 2018. Employment: Industry: Water Supply, Sewerage, Waste & Remed data is updated quarterly, averaging 59.000 Person th from Jan 2012 (Median) to Jul 2018, with 27 observations. The data reached an all-time high of 80.000 Person th in Jul 2017 and a record low of 39.000 Person th in Oct 2013. Employment: Industry: Water Supply, Sewerage, Waste & Remed data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G013: Labour Force Survey: Employment: by Industry, Occupation and Class.

Philippines Population Projection 2020 to 2025 by Admin3

This data is about the Philippines population projection count 2020 to 2025 by city/municipality (admin3) based on 2015 Census

Location

Philippines

Data Source

Philippines Statistics Authority

Contributor

OCHA Philippines

This dataset contains re-specified input-output tables from the input-output accounts of the Philippines for the years 2000, 2006, 2012 and 2018.

The 2000 and 2006 tables were published by the then National Statistical Coordination Board in 240-sector resolution.

The 2012 table was published by the Philippine Statistics Authority in 65-sector resolution.

The 2018 table was published by the Philippine Statistics Authority in the 16-sector resolution which was the basis of the re-specification of the 2000, 2006 and 2012 tables.

Shapefile (WGS 84) of thirty-three lakes of the Philippines with corresponding local name, based on the country shapefile from the Philippine Statistics Authority.

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.

Abstract

The Quarterly Survey of Philippine Business and Industry is a nationwide quarterly survey regularly conducted by the Philippine Statistics Authority. It aims to provide quarterly data on revenue/sales, employment and compensation for each of the identified key industries (3/5-digit level) as classified under the 2009 Philippine Standard Industrial Classification (PSIC).

Specifically, the survey data will be used by the Sectoral Statistics Office (created under RA 10625 - Philippine Statistical Act of 2013) in the generation of the Quarterly National Accounts (QNA) and in construction of the Quarterly Economic Indicators (QEI).

Geographic coverage

National and Regional

Analysis unit

Establishment

Universe

All establishments with total employment of 20 and over in the formal sector of the economy except agriculture, forestry and fishing.

Kind of data

Sample survey data [ssd]

Sampling procedure

The QSPBI frame consists of establishments, with ATE of 20 and over, as extracted from the latest available List of Establishments (LE) maintained by the Service and Industry Census Division (SICD) under Censuses and Technical Coordination Office of the PSA.

The updating of the LE involves (1) capturing and listing of characteristics of "new" establishments; (2) updating of the status and characteristics of "old" establishments; (3) de-listing "closed" establishments that should no longer form part of the LE and (4) identifying out-of-scope units on the database.

The 2015 ULE involved the complete enumeration of selected barangays where "no matched" establishments (establishments listed in other sources but not in the LE) from prioritized secondary sources are located. Also covered are barangays with new shopping malls, barangays having the highest number of establishments from the typhoon Yolanda affected cities/municipalities, barangays where there exist an establishment having an employment of 100 and over, and barangays with highest count of establishments for some provinces. Other "no matched" establishments, including those located in distant barangays, were covered using mail inquiry.

Other sources of updates are the survey feedbacks from the 2015 Quarterly Survey of Philippine Business and Industry (QSPBI) and 2015 Monthly Integrated Survey of Selected Industries (MISSI); list of branches and subsidiaries from the 2014 Annual Survey of Philippine Business and Industry and 2014 Survey of Tourism Establishments in the Philippines (STEP).

Mode of data collection

Other [oth]

Cleaning operations

To determine the completeness, consistency and reasonableness of entries in the accomplished questionnaires, the field office staff field edited and verified the accomplished reports based on specified editing and consistency checks instructions.

Doubtful entries were resolved immediately at the Provincial Office through phone calls or personal visits by defining or clarifying problems regarding the establishments' reports.

Response rate

For 1st quarter 2015 QSPBI, 95.3% response rate.

For 2nd quarter 2015 QSPBI, 91.4% response rate.

For 3rd quarter 2015, 91.4% response rate.

For 4th quarter 2015, 92.6% response rate.

For 1st quarter 2016 QSPBI, 95.7% response rate.

For 2nd quarter 2016 QSPBI, 95.4% response rate.

For 3rd quarter 2016, 94.4% response rate.

For 4th quarter 2016, 90.8% response rate.

Sampling error estimates

The current sample selection procedure of the QSPBI is not probability sampling, hence no sampling error estimates are computed.

Data appraisal

Data Evaluation:

Evaluation of the reports from establishments is done by comparing the growth rates of the variables in the current quarter report with the previous quarter report. That is, the ratio of the two succeeding (consecutive) reports for each of the data items should be within a specified range. These set ranges are based on the observed movements or trends from the historical reports of the establishments within the same industry groups. Reports that deviate from these ranges need to be verified with the establishment/respondent for correction or explanation.

Field Awards:

The Field Awards is an incentive system for the Philippine Statistics Authority regional and provincial offices to motivate the field offices to perform quality outputs in mandated activities and to conduct programs to support and promote its mission and vision. It also aims to increase PSA visibility not only among sub-national and local government agencies but also with the private sectors.

The Field Awards centers on efficiency, innovativeness, creativity and productivity of field offices. The Field Awards is dynamic and changes in criteria, weights and documentation requirements depend on the priorities of the office.

This dataset contains:

• windspeeds of Typhoon Nina
• rainfall of Typhoon Nina
• Priority Index of Typhoon Nina

The predicted priority index of Typhoon Nina is produced by a machine learning algorithm that was trained on five past typhoons: Haiyan, Melor, Hagupit and Rammasun and Haima, It uses base line data for the whole country, combined with impact data of windspeeds and rains, and trained on counts by the Philippine government on houses damaged and completely destroyed.

The output is a weighted index between partially damaged and completely damaged, where partially damaged is counted as 25% of the completely damaged. This has proven to give he highest accuracy.

The absolute number of houses damaged / people affected is insufficiently validated at the moment, and should just be used for further trainng and ground-truthing.

Scoring The model has an best r2 score of 0.794933727 and an accuracy of 0.699470899

Data sources:

Administrative boundaries (P_Codes) - Philippines Government; Published by GADM and UN OCHA (HDX)
Census 2015 (population) - Philippine Statistics Authority; received from UN OCHA (HDX)
Avg. wind speed (mph) - University College London
Typhoon path - University College London
Houses damaged - NDRRMC
Rainfall - GPM
Poverty - Pantawid pamilyang pilipino program (aggregated)
Roof and wall materials
New geographical features

All the columns with feat_ indicates the importance of that feature, if not present that feature was not used.

learn_matrix name of the learning matrix with the 5 typhoons
run_name unique run name (pickle files and csv files have this name for this model)
typhoon_to_predict name of a new typhoon to predict
val_accuracy accuracy based on 10 categories of damage 0% 10% 20% …
val_perc_down perc of underpredicted categories
val_perc_up perc of overpredicted categories
Val_best_score best r2 score

Val_stdev_best_score error on best score based on the CV
Val_score_test r2 score on the test set (this should be around +- 5% of the previus number to not overfit

Val_mean_error_num_houses average error on the number of houses
val_median_error_num_houses median
val_std_error_num_houses std deviation of the errors (lower is better)

Algorithm developed by 510.global the data innovation initiative of the Netherlands Red Cross.

Philippines Tourism Employment (TE) data was reported at 48,185.046 Person th in 2023. This records an increase from the previous number of 46,886.683 Person th for 2022. Philippines Tourism Employment (TE) data is updated yearly, averaging 37,107.000 Person th from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 48,185.046 Person th in 2023 and a record low of 28,294.000 Person th in 2000. Philippines Tourism Employment (TE) data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G049: Tourism Industry Employment.

Philippines Hours Worked Per Week (HWPW): At Work data was reported at 48,753.000 Person th in Feb 2025. This records an increase from the previous number of 48,075.000 Person th for Jan 2025. Philippines Hours Worked Per Week (HWPW): At Work data is updated monthly, averaging 47,289.500 Person th from Jan 2021 (Median) to Feb 2025, with 50 observations. The data reached an all-time high of 50,191.000 Person th in Dec 2023 and a record low of 41,037.000 Person th in Jan 2021. Philippines Hours Worked Per Week (HWPW): At Work data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G027: Labour Force Survey: Employment: Hours Worked Per Week.

Philippines administrative level 0-4 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.

NOTE: See COD-PS caveat about treatment of National Capital (Manila) data. OCHA acknowledges PSA and the National Mapping and Resource Information Authority (NAMRIA) as the sources. LMB is the source of official administrative boundaries of the Philippines. In the absence of available official administrative boundary, the IMTWG have agreed to clean and use the PSA administrative boundaries which are used to facilitate data collection of surveys and censuses. The dataset can only be considered as indicative boundaries and not official. Its updated to reflect the new areas within BARMM; It uses the new 10-digit pcode consistent with government PSGC as of 2023

This COD-AB was most recently reviewed for accuracy and necessary changes in April 2024. The COD-AB does not require any update.

Sourced from National Mapping and Resource Information Authority (NAMRIA), Philippines Statistics Authority (PSA)

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

This COD-AB is suitable for database or GIS linkage to the Philippines COD-PS.

As this is an island country, no edge-matched (COD-EM) version of this COD-AB is required.

Please see the COD Portal.

Administrative level 1 contains 17 feature(s). The normal administrative level 1 feature type is ""currently not known"".

Administrative level 2 contains 88 feature(s). The normal administrative level 2 feature type is ""currently not known"".

Administrative level 3 contains 1,642 feature(s). The normal administrative level 3 feature type is ""currently not known"".

Administrative level 4 contains 42,048 feature(s). The normal administrative level 4 feature type is ""currently not known"".

Recommended cartographic projection: Asia South Albers Equal Area Conic

This metadata was last updated on January 13, 2025.

Philippines Number of Establishments in Operation: Large: CALABARZON data was reported at 818.000 Number in 2023. This records an increase from the previous number of 737.000 Number for 2022. Philippines Number of Establishments in Operation: Large: CALABARZON data is updated yearly, averaging 635.000 Number from Dec 1999 (Median) to 2023, with 25 observations. The data reached an all-time high of 818.000 Number in 2023 and a record low of 442.000 Number in 2002. Philippines Number of Establishments in Operation: Large: 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.S008: Number of Establishments: Micro, Small, & Medium Enterprises (MSMEs): by Region.