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 DATASET IS RETIRED ❌❌❌

• Go here for latest 🇵🇭 Philippine Standard Geographic Codes Aug 2025 or directly access Philippine Standard Geographic Code

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In the fourth quarter of 2024, the names of three barangays in the Philippine Standard Geographic Code (PSGC) masterlist were corrected as recommended by the Interagency Technical Working Group on Geographic Code (TWGGC). The details of updates are as follows: The compilation and updating of the PSGC is a collaborative effort between the Philippine Statistics Authority (PSA) and its interagency TWGGC, which includes the Commission on Elections (COMELEC), Department of Budget and Management (DBM), Department of the Interior and Local Government (DILG), Land Management Bureau (LMB), Land Registration Authority (LRA), NHCP, and the National Mapping and Resource Information Authority (NAMRIA).

See more at Philippine Standard Geographic Code

Geographic Levels (as of 31 December 2024)

• Region- 18
• Province- 82
• Highly Urbanized City (HUC)- 33
• Independent Component City (ICC)- 5
• Component City (ICC)- 111
• Municipality- 1493
• Barangay- 42,011

Note

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Abstract

The general objective of the survey is to generate data on per capita consumption of rice, corn, and other agricultural food commodities. Specifically, the survey aims to determine: the present average per capita consumption of rice, corn, and other basic agricultural food items; the emerging consumption patterns of Filipino households; the substitution of rice with other food commodities; and the quantity of rice and corn leftovers, wastage, and consumed by animals/pets.

Geographic coverage

National coverage

Analysis unit

Households

Universe

Households in the Philippines

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey employed a two-stage sampling design with the barangay as primary sampling unit and the household as secondary sampling unit. For the 80 provinces, province was the domain. The sample barangays were stratified based on urban-rural classification then selected systematically within each stratum based on barangay's total household population with implicit representation of highly urbanized cities (HUCs) and independent component cities (ICCs). For NCR, the region served as the domain and two sample barangays were drawn systematically from each city and municipality.

Selection of sample households in each sample barangay was done during the first survey round (August 2015). The sample households were selected and located through the right coverage procedure based on pre-assigned starting point (sp), random start (rs), and sampling interval (i). The procedure initially yielded 13,400 sample households across the country. All successfully enumerated households during the first survey round were covered in the succeeding rounds. By the end of the fourth survey round (May 2016), the survey covered a total of 12,851 sample households nationwide.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

Editing of data collected from the survey was done at the provincial offices upon submission of the accomplished questionnaires by the SRs. These activities were undertaken to ensure the quality of data that were collected.

The document on Editing Guidelines is provided in the Technical Documents.

Response rate

Response rate was 100 pecent in the first survey round. Due to circumstances such as refusal and sample respondent moved to other location, the number of responses from the original number samples decreased to 95.90 percent.

Poverty Incidence is the proportion of individuals with per capita income less than the poverty thresholds. Data on poverty is compiled by the Philippine Statistics Authority (PSA) which is made available every three (3) years. The main source of data used in coming out of the poverty estimates are the triennial Family Income and Expenditure Survey (FIES), quarterly Labour Force Survey (LFS) and Consumer Price Index (CPI), and the Annual Poverty Indicator Survey (APIS).

Abstract

The 2013 Survey on Information and Communication Technology (SICT) is one of the designated statistical activities undertaken by the Philippine Statistics Authority (PSA) to collect and generate information on the availability, distribution and access/utilization of ICT among establishments in the country.

The objectives of the 2013 SICT is to provide key measures of ICT access and use among establishments which will enable the assessment and monitoring of the digital divide in the country. Specifically, the survey aims to measure the following: - component of ICT resources and their utilization by establishments; - diffusion of ICT into establishments from various sources; - e-commerce transactions from data on e-commerce sales/revenue and purchases; - cellular mobile phone business transactions from data on sales/revenue; - estimate of the number of ICT workers in establishments; - methods of disposal of ICT equipment.

The SICT 2013 was a rider survey of the 2013 Annual Survey of Philippine Business and Industry.

Geographic coverage

Regional - "core" ICT and BPM industries are the regions National - "non-core" ICT industries

Analysis unit

An establishment, which is defined as an economic unit under a single ownership or control, i.e., under a single legal entity, engaged in one or predominantly one kind of economic activity at a single fixed location

Universe

The 2013 Survey on Information and Communication Technology (SICT) of Philippine Business and Industry covered all industries included in the 2013 Annual Survey of Philippine Business and Industry (ASPBI).

For the purpose of the survey, these industries were classified as core ICT industries and non-core ICT Industries. Core ICT industries were industries comprising the Information Economy (IE). The Information Economy is a term used to describe the economic and social value created through the ability to rapidly exchange information at anytime, anywhere to anyone. A distinctive characteristic of the information economy is the intensive use, by businesses of ICT for the collection, storage, processing and transmission of information. The use of ICT is supported by supply of ICT products from an ICT-producing sector through trade.

Information Economy is composed of the Information and Communication Technology Sector and Content and Media Sector. Industries comprising these two sectors are as follows: 1) Information and Communication Technology - ICT manufacturing industries - ICT trade industries - ICT service industries: - Software publishing - Telecommunication services - Computer programming, consultancy and related services - Data processing, hosting and related activities; web portals - Repair of computers and communication equipment 2) Content and Media - Publishing activities - Motion picture, video and television programme production, sound recording and music publishing activities - Programming and broadcasting activities

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2013 SICT utilized the stratified systematic sampling design with five-digit PSIC serving as industry strata (industry domain) and the employment size as the second stratification variable.

There were only two strata used for the survey, as follows: TE of 20 and over and TE of less than 20.

The industry stratification for the 2013 SICT is the 5-digit PSIC for both the core ICT industries and for the non-core ICT industries. It has the same industry strata as that of the 2013 ASPBI.

Establishments engaged in the core ICT industries were completely enumerated, regardless of employment size.

The establishments classified in the non-core ICT industries and with total employment of 20 and over were covered on a 20 percent sampling basis for each of the industry domain at the national level. The minimum sample size is set to 3 establishments and maximum of 10 establishments per cell (industry domain).

However, when the total number of establishments in the cell is less than the set minimum sample size, all establishments in that cell were taken as samples.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The scope of the study includes: - general information about the establishment - information and communication technology (ICT) resources of the establishment - network channels - use of ICT resources, Internet - website of the establishment - e-commerce via internet - e-commerce via computer networks other than the internet - use of mobile phones in selling and other business operation - purchase and disposal of ICT equipment

Cleaning operations

Manual processing took place in Provincial Offices at a number of stages throughout the processing, including: - coding of some data items - editing of questionnaires - checking completeness of entries - consistency check among variables.

Data processing was done in Field Offices and Central Office.

Field Offices were responsible for: - online data encoding and updating - completeness and consistency edits - folioing of questionnaires.

Central Office was responsible for: - online validation - completeness and consistency checks - summarization - tabulation.

Response rate

The overall response rate for the 2013 SICT was 87.04 percent (9,562 of the 10,986 sample establishments). This included receipts of "good" questionnaires, partially accomplished questionnaires, reports of closed, moved out or out of scope establishments. Sample establishments under core ICT industries reported 89.96 percent response rate ( 5,421 out of 6,026 establishments) while non-core ICT industries response rate was 83.48 percent (3,633 out of 4,352 sample establishments). On the other hand, industries classified in Business Process Management (BPM) had a response rate of 83.55 percent (508 out of 608 establishments).

Sampling error estimates

Not computed

Data appraisal

Data estimates were checked with those from other related surveys or administrative data.

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.

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

Context

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Updates

The Province of Sulu Officially Transferred to Region IX (Zamboanga Peninsula)

• Release Date: Friday, August 29, 2025
• Reference Number: 2025-325
• Description: Pursuant to Executive Order No. 91, Series of 2025 dated 30 July 2025, the Province of Sulu has been officially transferred to Region IX (Zamboanga Peninsula). The transfer includes all municipalities and barangays within the Province of Sulu. This development follows the Supreme Court’s ruling affirming the exclusion of the province from the Bangsamoro Autonomous Region in Muslim Mindanao (BARMM).

See more at Philippine Standard Geographic Code

Geographic Levels (as of 29 August 2025)

• Region- 18
• Province- 82
• Highly Urbanized City (HUC)- 33
• Independent Component City (ICC)- 5
• Component City (ICC)- 111
• Municipality- 1493
• Barangay- 42,011

Note

Image generated with Bing Image Generator

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/.

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.

Abstract

The Palay Production Survey is one of the two modules of the Palay and Corn Production Survey (PCPS), formerly known as the Rice and Corn Production Survey (RCPS).

The Palay Production Survey (PPS) 2016 is a quarterly survey conducted by the Philippine Statistics Authority (PSA). It aims to generate estimates on palay production, area and yield and other related information at the provincial level. The four rounds are conducted in January, April, July and October. Each round generates estimates for the immediate past quarter and forecasts for the next two quarters. Results of the survey serve as inputs to planners and policy makers on matters concerning the rice industry.

Geographic coverage

National

Analysis unit

• Farming households
• Palay areas operated by farming households

Universe

Farming households in palay producing barangays.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling procedure used in the Palay Production Survey 2016 (PPS 2016) was first implemented in 1994. This is a replicated two-stage stratified sampling design with province as the domain, barangay as the primary sampling unit (psu) and farming household as the secondary sampling unit (ssu).

The results of the 1991 Census of Agriculture and Fisheries (CAF 1991) serve as sampling frame at the psu and ssu levels. In the said census, the largest barangay in a municipality is taken with certainty while a 50 percent sampling rate is used for selecting the remaining barangays in the municipality. This scheme effectively resulted in the generation of two sub-universes: a sub universe of barangays with probability of selection equal to one (these barangas are called 'certainty barangays') and another sub-universe of barangays with probability of selection equal to 0.5. This characteristic of the CAF 1991 data is used in the selection of sample barangays for the PPS.

The barangays are arrayed in ascending order based on palay area which are stratified such that the aggregate palay area of the barangays belonging to one stratum is more or less equal to the aggregate palay area of the barangays in any other stratum. Ten strata are formed for major palay producing provinces and five for minor producing provinces. In all these provinces, the last stratum consisted of the certainty barangays per CAF 1991 design.

For each stratum, four (4) sample barangays are drawn independently using probability proportional to size (pps) sampling with the barangay's palay area as size measure. This resulted with four (4) independent sets of barangays (i.e., four replicates) for the province. Systematic sampling is used in drawing the sample farming households in each sample barangay.

For economic reasons, sample size per barangay is limited to a minimum of four (4) and a maximum of twenty five (25). To correct for this limitation of the design, the use of household weights is instituted. A detailed discussion of weighting in the PPS is included in the survey's estimation procedure attached as a Technical Document.

In November 2007, an updating of the list of farming households in all palay sample barangays nationwide is done to address the problem of non-response due to transfer of residence, stoppage of farm operation, passing away of operator etc. Consequently, a new set of sample households is drawn.

Respondents who refused to be interviewed, not a home, unknown and transferred to another barangay are treated as missing and are replaced at the Central Office for the next quarter's survey. The replacement samples are taken from the list of replacements (farming households) for the barangay and are reflected in the list of sample households for the next round.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for Palay Production Survey (PPS) 2016 is written in English. It evolves from modifications in 2012 based on the commitment of making available to the public the reliable statistics in palay and continuous efforts in developing approaches and methodologies in estimating such statistics particularly improving the survey questionnaires. The Technical Working Group on Cereals Statistics of the Bureau reviewed simultaneously the PPS and CPS questionnaires and came up with sets of user-friendly survey instruments. The major features of the new PPS questionnaire are: shift from barangay level to farm level questionnaire i.e., from a maximum of five (5) households to one (1) household per questionnaire; change in questionnaire format; more detailed sample status categories; defined types of ecosystem; inclusion of items on labor inputs; and application of organic pesticides. This new questionnaire was used starting April 2012 survey round.

The questionnaire was divided into the following blocks: Block A - Sample identification Block B - Sample particulars Block C - Information on paddy (palay) harvested Block C.1 - Area, production, seed and irrigation information Block C.2 - Fertilizer usage Block C.3 - Pesticide usage Block C.4 - Labor inputs Block D - Palay production disposition (all ecosystem) Block E - Palay production forecast (on standing crop) Block F - Palay planting intentions Block G - Respondent's assessment of the household palay production Block H - Farmer's participation in rice program Block I - Statistical Researcher, Supervisor, PSO and Encoder Identfication

Cleaning operations

Prior to data encoding, the accomplished survey returns are manually edited and coded. Manual editing is checking of responses to the Palay Production Survey (PPS) questionnaire in terms of acceptability and validity. This activity aims at improving the quality of data collected by the SRs. It involves the checking of data items based on criteria like completeness of data, consistency with other data items and data ranges. Coding is the assignment of alpha-numeric codes to questionnaire items to facilitate encoding.

Encoded data are subjected to computerized editing using a customized editing program. The editing program take into consideration the validation criteria such as validity, completeness and consistency with other data items. This activity is done to capture invalid entries that were overlooked during manual editing. An error listing is produced as output of the process. The errors reflected in said lists are verified vis-à-vis the questionnaires. The data files are updated based on the corrections made. Editing and updating are performed iteratively until a clean, error-free data file is generated.

Completeness check is done to compare the data file against a master file of barangays to check if the sample barangays have been completely surveyed or not. This activity is done after a clean, error-free data file is generated.

Response rate

Average 85.0% across quaters - April 2016 Round, July 2016 Round, October 2016 Round and January 2017 Round.

Sampling error estimates

Not computed.

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. These information include results of the Monthly Palay and Corn Survey Reporting System (MPCSRS), historical data series, report on weather condition, area and crop condition, irrigation, levels of inputs usage, supply and demand, marketing of agricultural products, and information on rice and corn program implementation.

Food Availability

Food availability dimension addresses supply side of the food security and expects sufficient quantities of quality food from domestic agriculture production or import.

1. Food Available per Capita
2. Food Production Index
3. Import Dependency Ratio

Food Accessibility

Food accessibility refers to the access by individuals to adequate resources for acquiring appropriate foods for a nutritious diet. It addresses whether the households or individuals have enough resources to acquire appropriate quantity of quality foods, thus, it encompasses their income, expenditure and buying capacity.

There are two aspects of food access – the economic and physical access. Economic access refers to factors such as income, poverty and other indicators of buying capacity. Physical access indicators are related to infrastructure and facilities that hasten the access to food.

Furthermore, the indicators were grouped into the level of importance which were either key or support. Key indicators are those which best describe the dimension. In the absence of available data for the key indicators, the support indicators will be the alternative for use.

1. Consumer Price Index for All Income Households
2. Farmer's share in Consumer Peso

Food Utilization

Food utilization is one of the three dimensions of food security. It is defined as the ability of the human body to ingest and metabolize food through adequate diet, clean water, good sanitation and health care to reach a state of nutritional well-being where all physiological needs are met.

In this dimension, it is essential to know if the food available in a given period of time had been accessed and well utilized. A household makes decisions on what food to consume and how to allocate food within the household. Appropriate food intake is vital for nutritional status of the populace.

1. Ratio of Food Expenditure to Total Family Expenditure

Source

Datasets taken from the Philippine Statistics Authority (PSA) OpenStat website.

Abstract

The Listing of Dairy Farms (LDF) in selected provinces was one of the special activities of the Livestock and Poultry Statistics Division (LPSD) under the Economics Sector Statistics Service (ESSS) of the Philippine Statistics Authority (PSA). It was undertaken to provide an updated sampling frame for the generation of dairy statistics. The 2016 LDF is a collaborative activity between the PSA and the Philippine Carabao Center (PCC). The project intended to update the list of dairy farms/enterprises all over the country where dairying exists. Initially, the first eight (8) provinces were identified by the PCC with National Dairy Authority (NDA) which are the "impact zones" of the two (2) agencies for the conduct of 2016 LDF. The main objective of the project was to update the characteristics of dairy farms/enterprises through a complete listing of barangays in the selected provinces of Cagayan, Isabela, Nueva Ecija, Bulacan, Batangas, Laguna, Bohol, and Misamis Orienta where dairying exists. Specifically, it aims to generate a profile of dairy farms by farm type. Information to be gathered includes farm capacity, legal status of the dairy farm and type of assistance received from the government. Moreover, it seeks to generate information on the type of dairy animals, breed of dairy animals, ownership of the dairy animal, purpose of dairy animal, number of animals on the milk line, and average milk production per head per day.

The 2016 LDF activities involved capturing "new" dairy farms and their characteristics; updating the status and characteristics of "existing" dairy farms; and de-listing of dairy farms that no longer exists. This activity covers household and commercial dairy farms in the priority provinces mentioned-above. These provinces were determined by PCC and NDA which are part of their impact zones. Dairying activity in these provinces is extensive, thus, their contribution to the total milk production in the country is very significant.

Geographic coverage

Regional Coverage

Analysis unit

Agricultural holdings

Universe

All dairy farms in the selected provinces

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

The processing system used in the 2016 LDF was developed by the System Development Division (SDD) of PSA. Census and Survey Processing System (CSPro), which is the programming language used in most of the PSA surveys, was utilized for this activity. Output tables including the specifications were provided by LPSD to SDD for easier tabulation of data.

Data Processors (DPs) were hired to process the data gathered. They were closely monitored by PSO staff to assure the quality of the data generated. Prior to the start of data processing, training of hired DPs was conducted by selected staff of SDD. A week after the commencement of data collection, processing of accomplished listing forms was done in the provincial offices.

Data processing includes manual and machine processing. Manual processing involves editing and checking for consistency of answers to the data items and codes provided. This was done by the Provincial Focal Person. Machine processing, on the other hand, includes encoding of data, validation of encoded data and generation of output tables.

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

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

2012 City and Municipal-Level Small Area Poverty Estimates

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

City and Municipal-Level Poverty Estimates; 2006 and 2009

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

Column Header / Description

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

1Originally single data for a single port, then transformed to pairwise using the formula a+b where a and b are single values for each port.2Calculated from satellite images using the "ruler" function of Google Earth.3Official data from the Philippine Statistics Authority—National Statistics Office (http://web0.psa.gov.ph/).4Official data from the Philippine Ports Authority (http://www.pdosoluz.com.ph/).Definitions of the variables used to test the hypothesis that human transportation influences population structure of Ae. aegypti in the central-western Philippines.

About the Dataset Repository

This dataset repository houses the datasets used to create QRSP2v1.0-Mindanao. With all three datasets combined, there are 1584 samples. The TRAINING dataset contains 1104 samples, while the VALIDATION and TESTING datasets contain 240 samples each. The following table illustrates which column of the datasets associates to the ID and variable type demonstrated in the paper alongside the provider of the data.

Note:

• PSA: Philippines Statistics Authority
• PAGASA: Philippines Atmospheric Geophysical and Astronomical Services Administration
• NOAA CPC: National Oceanic and Atmospheric Administration Climate Prediction Center

| Type | Column on Dataset | ID | Provider | | ----- | -------------------- | -- | -------- | | | Quarters | | PSA | | Feature | Region | F1 | PSA | | Feature | Field Type | F2 | PSA | | Feature | Quarter Type | F3 | PSA | | Feature | Area Harvested | F4 | PSA | | Feature | Rainfall of 3rd Month of Quarter | F5 | PAGASA | | Feature | Rainfall of 2nd Month of Quarter | F6 | PAGASA | | Feature | Rainfall of 1st Month of Quarter | F7 | PAGASA | | Feature | Rainfall 1 Month Before Start of Quarter | F8 | PAGASA | | Feature | Rainfall 2 Months Before Start of Quarter | F9 | PAGASA | | Feature | Rainfall 3 Months Before Start of Quarter | F10 | PAGASA | | Feature | SSTA El Nino 3.4 of 3rd Month of Quarter | F11 | NOAA CPC | | Feature | SSTA El Nino 3.4 of 2nd Month of Quarter | F12 | NOAA CPC | | Feature | SSTA El Nino 3.4 of 1st Month of Quarter | F13 | NOAA CPC | | Feature | SSTA El Nino 3.4 1 Month Before Start of Quarter | F14 | NOAA CPC | | Feature | SSTA El Nino 3.4 2 Months Before Start of Quarter | F15 | NOAA CPC | | Feature | SSTA El Nino 3.4 3 Months Before Start of Quarter | F16 | NOAA CPC | | Label | Production | L1 | PSA |

Philippines Ceramic Tiles Market size was valued at USD 3.7 Billion in 2024 and is projected to reach USD 6.4 Billion by 2032, growing at a CAGR of 6.26% from 2026 to 2032.Key Market Drivers:Construction Sector Growth and Infrastructure Development: The Philippine government's Build, Build, Build initiative and significant construction activity have fueled demand for ceramic tiles. According to the Philippine Statistics Authority (PSA), the construction industry expanded by 12.7% in 2023, with 41,020 authorized building permits in Q4 2023 alone. Residential building, which relies largely on ceramic tiles, accounted for almost 70% of all permits.

Unemployment Rate in Philippines decreased to 3.80 percent in September from 3.90 percent in August of 2025. This dataset provides - Philippines Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

The 2013 NDHS is designed to provide information on fertility, family planning, and health in the country for use by the government in monitoring the progress of its programs on population, family planning and health.

In particular, the 2013 NDHS has the following specific objectives: • Collect data which will allow the estimation of demographic rates, particularly fertility rates and under-five mortality rates by urban-rural residence and region. • Analyze the direct and indirect factors which determine the level and patterns of fertility. • Measure the level of contraceptive knowledge and practice by method, urban-rural residence, and region. • Collect data on health, immunizations, prenatal and postnatal check-ups, assistance at delivery, breastfeeding, and prevalence and treatment of diarrhea, fever and acute respiratory infections among children below five years old. • Collect data on environmental health, utilization of health facilities, health care financing, prevalence of common non-communicable and infectious diseases, and membership in the National Health Insurance Program (PhilHealth). • Collect data on awareness of cancer, heart disease, diabetes, dengue fever and tuberculosis. • Determine the knowledge of women about AIDS, and the extent of misconception on HIV transmission and access to HIV testing. • Determine the extent of violence against women.

Geographic coverage

National coverage

Analysis unit

• Household
• Individuals/ persons
• Woman age 15 to 49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample selection methodology for the 2013 NDHS is based on a stratified two-stage sample design, using the 2010 Census of Population and Housing (CPH) as a frame. The first stage involved a systematic selection of 800 sample enumeration areas (EAs) distributed by stratum (region, urban/rural). In the second stage, 20 sample housing units were selected from each sample EA, using systematic random sampling.

All households in the sampled housing units were interviewed. An EA is defined as an area with discern able boundaries consisting of contiguous households. The sample was designed to provide data representative of the country and its 17 administrative regions.

Further details on the sample design and implementation are given in Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2013 NDHS used three questionnaires: Household Questionnaire, Individual Woman’s Questionnaire, and Women’s Safety Module. The development of these questionnaires resulted from the solicited comments and suggestions during the deliberation in the consultative meetings and separate meetings conducted with the various agencies/organizations namely: PSA-NSO, POPCOM, DOH, FNRI, ICF International, NEDA, PCW, PhilHealth, PIDS, PLCPD, UNFPA, USAID, UPPI, UPSE, and WHO. The three questionnaires were translated from English into six major languages - Tagalog, Cebuano, Ilocano, Bicol, Hiligaynon, and Waray.

The main purpose of the Household Questionnaire was to identify female members of the sample household who were eligible for interview with the Individual Woman’s Questionnaire and the Women’s Safety Module.

The Individual Woman’s Questionnaire was used to collect information from all women aged 15-49 years.

The Women’s Safety Module was used to collect information on domestic violence in the country, its prevalence, severity and frequency from only one selected respondent from among all the eligible women who were identified from the Household Questionnaire.

Cleaning operations

All completed questionnaires and the control forms were returned to the PSA-NSO central office in Manila for data processing, which consisted of manual editing, data entry and verification, and editing of computer-identified errors. An ad-hoc group of thirteen regular employees from the DSSD, the Information Resources Department (IRD), and the Information Technology Operations Division (ITOD) of the NSO was created to work fulltime and oversee data processing operation in the NDHS Data Processing Center that was carried out at the NSO-CVEA Building in Quezon City, Philippines. This group was responsible for the different aspects of NDHS data processing. There were 19 data encoders hired to process the data who underwent training on September 12-13, 2013.

Data entry started on September 16, 2013. The computer package program called Census and Survey Processing System (CSPro) was used for data entry, editing, and verification. Mr. Alexander Izmukhambetov, a data processing specialist from ICF International, spent two weeks at NSO in September 2013 to finalize the data entry program. Data processing was completed on December 6, 2013.

Response rate

For the 2013 NDHS sample, 16,732 households were selected, of which 14,893 were occupied. Of these households, 14,804 were successfully interviewed, yielding a household response rate of 99.4 percent. The household response rates in urban and rural areas are almost identical.

Among the households interviewed, 16,437 women were identified as eligible respondents, and the interviews were completed for 16,155 women, yielding a response rate of 98.3 percent. On the other hand, for the women’s safety module, from a total of 11,373 eligible women, 10,963 were interviewed with privacy, translating to a 96.4 percent response rate. At the individual level, urban and rural response rates showed no difference. The principal reason for non-response among women was the failure to find individuals at home, despite interviewers’ repeated visits to the household.

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 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 2013 National Demographic and Health Survey (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 2013 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 error is a measure of the variability between the results of all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey data.

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 percent 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 2013 NDHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2013 NDHS is a SAS program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions. The Jackknife repeated replications method is used for variance estimation of more complex statistics such as fertility and mortality rates.

The Taylor linearization method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of weighted cases in the group or subgroup under consideration.

Further details on sampling errors calculation are given in Appendix B of the final report.

Data appraisal

Data quality tables were produced to review the quality of the data: - 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

Note: The tables are presented in APPENDIX C of the final report.