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.

This data represents a central collection of Public Service Announcements (PSA), News Releases and Media Advisories for all to search and review. Dataset will be refreshed at approximately 13:15 and 17:15 each weekday.

Disclaimer:

The Release Body Part 1 and Part 2 columns are intended to provide text search capabilities and is by no means intended to reflect the full Media Release. For the official media release, please refer to the Release URL.

Police Service Areas boundaries.

Locations of grab samples with Particle Size Analysis (PSA) results from Wight-Barfleur Reef SCI (CEND 03/13). ArcGIS point feature class. Locations of grab samples with Particle Size Analysis (PSA) results across Wight-Barfleur Reef candidate Special Area of Conservation (cSAC). Grab samples were collected during the following survey: Wight-Barfleur Reef survey aboard the RV Cefas Endeavour during cruise code CEND0313, sampling between 19/03/2013 and 24/03/2013. In total,14 PSA samples were collected during CEND0313. All but one of the 14 samples were collected using a 0.1 m� mini Hamon grab(HG), the exception being a single sample (HP31) collected using the larger 0.25 m� Hamon grab (LH). A sedimentsubsample was taken from each grab sample for particle size analysis (PSA). Sediment samples were preocessed following the PSA methodology recommended by the National Marine Biological Quality Control (NMBAQC) scheme(Mason, C. 2011. NMBAQC's Best Practice Guidance. Particle Size Analysis (PSA) for Supporting Biological Analysis. National Marine Biological AQC Coordinating Committee, 72pp, December 2011.http://www.nmbaqcs.org/media/10839/nmbaqc%20best%20practice%20guidance_particle%20size%20analysis.pdf).

Abstract

The Republic of the Philippines is making great efforts to develop agriculture at a pace necessary to meet the food requirements of the fast-growing population. It has become necessary to use current agricultural statistics that will help present an accurate picture of the country's food situation. Especially important are the expected supply and consumption requirements of the people, particularly of meat products. The Commercial Livestock and Poultry Survey (CLPS) seeks to provide if but partially, such information.

The CLPS is one of the major regular activities of the Livestock and Poultry Statistics Division (LPSD) under the Economic Sector Statistics Service (ESSS) of the Philippine Statistics Authority (PSA). The CLPS is undertaken to provide an estimate on current inventory and supply and disposition of commercial livestock and poultry farms. The CLPS is done quarterly for swine, broiler, and layer while data collection for carabao, cattle, goat, duck and sheep is likewise conducted semi-annually. The information present here is related to the layer module.

The survey covers all provinces including Dinagat Islands and two (2) chartered cities (Davao City and Zamboanga City). Moreover, a separate structured questionnaire in the collection of the necessary information for each animal type is utilized. Estimates generated from the CLPS and the Backyard Livestock and Poultry Survey (BLPS) are aggregated to come up with the total Livestock and Poultry (L&P) estimates. The data generated was perceived to be useful as guide for the government and the private sector in making plans and decisions with respect to farm production and improvement of the livestock and poultry industry.

The data generated from this survey are disseminated through the country STAT website and featured in the Quarterly Commodity Special Releases and Annual Commodity Situation Reports released every May. The collection of data on this survey is undertaken by hired Statistical Researchers (SRs) while the electronic processing is done by the regular staff in the Provincial Statistical Offices (POs). The SRs are trained prior to field operations to ensure that the procedures and concepts are understood. The training includes mock interviews and dry-run exercises.

Geographic coverage

National Coverage

Analysis unit

Entreprises

Universe

The CLPS covers all livestock and poultry farms with commercial type of operation. Commercial farm refers to a farm or household operated by a farmer/household/operator that raises at least one of the following: 1. Livestock - Carabao (Water Buffaloes), Cattle, Swine and Goat 2. Poultry - Layer, Broiler and Duck

Also, it must satisfy at least one of the following criteria: 1. Livestock · at least 21 heads of adult and zero head of young · at least 41 heads of young animals and above · at least 10 heads of adult and 22 heads of young and above

1. Poultry · at least 500 layers, or 1,000 broilers and above · at least 100 layers and 100 broilers if raised in combination and above · at least 100 head of duck regardless of age

The survey also covers traders such as assemblers and distributors, etc.

Trader refers to a person or entity that buys and sells goods or commodities.

Assembler refers to a type of trader who sources and procures his/her stocks from contract growers or independent farmers in several barangays in a specific municipality, and transports the produce to a trading or market center.

Distributor refers to a trader who sells commodities to other traders and consumers.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE SELECTION PROCEDURE The sampling design used for each animal type are the same but are treated independently. The sampling design depends on the total number of commercial farms and the corresponding maximum housing capacities of the farms in the province. In provinces with less than 21 farms, all farms are completely enumerated. However, provinces with a large number of farms or those with 21 or more farms, stratification is applied using the Dalenius-Hodges method of stratification with the maximum housing capacity as stratification variable. The number of strata per province ranges from two (2) to four (4) depending on the heterogeneity or homogeneity of the maximum housing capacity. Sample allocation for each stratum is done using the Neyman procedure with coefficient of variation set at five percent (5%). A minimum of five (5) samples per stratum is allocated. A stratum may have less than 5 samples only if the total number of farms in that stratum is less than 5. Selection of samples from each stratum is done using simple random sampling.

The sample selection procedure is discussed as follows: 1. Rank all farms in ascending order according to their maximum housing capacity; 2. Delineate the stratum boundaries using Dalenius-Hodges method (unique stratum boundaries for each province are derived); 3. Determine the total number of commercial farms per stratum; 4. Allocate sample size for each stratum using Neyman procedure (a five percent (5%) coefficient of variation is assumed and a minimum of five (5) samples are taken when Nh = 5). For stratum with Nh<5, all farms in that stratum shall be enumerated; and 5. Select the required number of sample farms using the simple random sampling method.

For provinces where stratified sampling is employed, in case of non-response, adjustment of expansion factor is implemented by stratum and by animal type using the status of the sample commercial farms.

Comprehensive discussion on the estimation procedure is found in page 10 of the CLPS manual found in Related Materials.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

For CLPS, editing is done in two (2) stage. The first stage of editing is done during the data collection. The Statistical Researcher, before leaving the premises of the sample commercial farm, shall do field editing. This activity involves assuring that all data items in the questionnaires are asked and that the answers were written down correctly. The second stage of editing is conducted by the supervisor upon the submission of accomplished questionnaires/forms by the SR called manual editing.

The system used in processing the data collected from this survey was developed by the Systems Development Division (SDD) of PSA. CSPro, the software used in most of the surveys of PSA, is utilized.

Using a pre-formatted template, consolidated estimates are generated through the Provincial Summary Worksheets (PSW-C). This worksheet presents data for each sample commercial farm, raw provincial total data and expanded provincial total estimates.

These estimates are transferred manually into an excel-based validation sheet called the "Supply-Disposition Worksheets" where the PSO, together with the L&P focal person, act as data analysts. To ensure the quality of data, the generated outputs shall undergo data review and validation. Data review involves internal checks of the data collected, consistency and completeness check of data items and detection and correction of identified errors. Data validation, on the other hand, ensures that the estimates generated are truly reflective of the current industry situation. It involves a thorough analysis of the generated estimates using auxiliary information. Auxiliary information includes animal dispersal from government programs, weather condition, price trends, import and export among others. Data review and validation is supported by the Electronic Data Review Workbook (EDRW) Compilation System. This is a tool used in reviewing and validating the L&P statistics and commonly termed as "Supply-Disposition (S-D) Technique".

The outputs of the CLPS together with BLPS undergo three (3) levels of data review and validation. The first stage is at the Provincial level known as the Provincial Data Review (PDR) followed by the second level which takes place at the RSSOs, known as the Regional Data Review (RDR). During the RDR, the RSSOs shall likewise review and validate the outputs of the provinces under its jurisdiction.

The third level of data review and validation and is the final level is conducted at the Central Office. All outputs sent by the RSSOs shall be consolidated by the LPSD commodity specialists to generate the final livestock and poultry statistical tables as input in the preparation of reports.

Response rate

The response rate for the survey ranged from 85-90%.

Data appraisal

To ensure the quality of its statistical services, the PSA has mainstreamed in its statistical system for generating production statistics, a quarterly data review and validation process. This is undertaken at the provincial, regional and national levels to incorporate the impact of events not captured in the survey. The data review process starts at the data collection stage and continues up to the processing and tabulation of results. However, data examination is formalized during the provincial data review since it is at this stage where the data at the province-level is analyzed as a whole. The process involves analyzing the survey data in terms of completeness, consistency among variables, trend and concentration of the data and presence of extreme observations. Correction of spotted errors in the data is done afterwards. The output of the process is a clean data file used in the re-computation of survey estimates. The estimates generated from the clean data set are thoroughly analyzed and validated with auxiliary information to incorporate the impact of information and events not captured by the survey. This

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.

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.