❌❌❌ THIS DATASET IS RETIRED ❌❌❌

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

Context

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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

Image generated with Bing Image Generator

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.

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/.

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 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.

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

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Payment Schedule Application - Cost Lists

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.

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.

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.

Abstract

The Bureau of Agricultural Statistics (BAS) has been monitoring the palay and corn situation in the country through a Monthly Palay and Corn Situation Reporting System (MPCSRS) since 1985. The activity aims primarily to update the forecasts (based on standing crop and planting intentions) generated through the Palay and Corn Production Survey (PCPS). Based on the findings of the MPCSRS, the BAS submits a memorandum to the office of the Secretary, Department of Agriculture to inform him of the latest production status of palay and corn.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Universe

MPCSRS covers palay and corn farming households with at least 0.100 hectare or 1000 square meters of operation.

Kind of data

Sample survey data [ssd]

Sampling procedure

The MPCSRS is conducted monthly in between the Palay Production Survey (PPS)/Corn Production Survey (CPS) rounds, making use of one replicate of the PPS/PCS as sample such that:

• For pure palay provinces, one replicate consisting of ten (10) barangays is taken from the PPS samples
• For pure corn provinces, one replicate consisting of ten (10) barangays is taken from the CPS samples
• For overlap (that is palay and corn) provinces, five (5) barangays is taken from the PPS and another five (5) barangays are taken from the CPS samples yielding only one sample of barangays for the province.
• For minor palay or corn provinces, one replicate consisting of five (5) barangays are taken as samples.
• For non-corn provinces, one replicate of the PPS barangay samples is taken as samples for the MPCSR.

All households in the selected barangays are enumerated. Currently, the MPCSRS has a total sample size of 680 barangays nationwide. The replicates are selected using probability proportional to size based on total palay/corn areas.

Details of the documentation of the Palay Production Survey (PPS) and Corn Production Survey (CPS) sampling procedure can be viewed from the BAS Electronic Archiving and Network Services (BEANS), http://beans.psa.gov.ph.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

Manual coding and editing are done at the Provincial Operations Centers (POCs). At the POCs, during the electronic data processing, checking of household serial numbers based on the master list of samples, consistency checks based on data ranges, and consistency checks against other data variables within the questionnaire are done by running an editing program. A completeness check program is also run to check if all sample respondents are accounted for.

At the Central Office, another round of editing is done. This activity is done to check that the data file is totally clean. The output tables generated from the clean data files are converted to Excel files to facilitate further data analysis. The estimates generated from the clean MPCSR data are reviewed at the provincial level before submitting to the Central Office. At the Central Office, the estimates are subjected to review and validation.

Response rate

Response rate for Palay samples was 92.59 %, while response rate for Corn samples = 100 %

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

This dataset was lifted from openstat.psa.gov.ph database under the Agriculture, Forestry, Fisheries (Crops) Category.

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 outlines the list of local courses offered by the Public Service Academy (PSA) during fiscal 2023-2024 and the number of GoRTT officers trained in each course.

Abstract

The Occupational Wages Survey (OWS) generates statistics for wage and salary administration and for wage determination in collective bargaining negotiations. This nationwide biennial survey covers establishments employing at least 20 workers.

The OWS is one of the designated statistical activities in E.O. 352 (s.1996) that designates those critical for decision making by the government and the private sector. Moreover, the data category average monthly occupational wage rates in selected occupation is among those listed by the Philippine government under the Special Data Dissemination Standard (SDDS) of the International Monetary Fund. The SDDS serves as reference to member countries in the dissemination of economic and financial data to the public.

Geographic coverage

National coverage, 17 administrative regions

Analysis unit

Establishment

Universe

The survey covers agricultural and non-agricultural establishments employing 20 or more workers except central banking, public administration and defense and compulsory social security, public education services, public medical, dental and other health services, activities of membership organizations, activities of households as employers of domestic personnel, undifferentiated goods-and-services-producing activities of households for own use and activities of extra-territorial organizations and bodies.

Pre-determined industries for wage monitoring now total to 50 due to the inclusion of agriculture, forestry and fishery; and the splitting and merging of original domains with the adoption of the 2009 PSIC.

Inclusion of new domains: - Crop and Animal Production, Hunting and Related Service Activities; Forestry and Logging (A01/A02) - Fishing and Aquaculture (A03) - Manufacture of Basic Pharmaceutical Products and Pharmaceutical Preparation (C21)

Splitting of original domains: - Publishing and Printing (D221/D222/D223 of 1994 PSIC as amended) into Printing and Reproduction of Recorded Media (C18); and Publishing Activities (J58) - Supporting and Auxiliary Transport Activities; Activities of Travel Agencies (I63 of 1994 PSIC as amended) into Warehousing and Support Activities for Transportation (H52); and Travel Agency, Tour Operator, Reservation Service and Related Activities (N79)

Merging of original domains: - Banking Institutions except Central Banking (J65 excl. J6510 of 1994 PSIC as amended) and Non-Bank Financial Intermediation (J66 of 1994 PSIC as amended) into Financial Service Activities except Insurance, Pension Funding and Central Banking (K64 excl. K6411)

Kind of data

Sample survey data [ssd]

Sampling procedure

Statistical unit: The statistical unit is the establishment. Each unit is classified to an industry that reflects its main economic activity---the activity that contributes the biggest or major portion of the gross income or revenues of the establishment.

Survey universe/Sampling frame: The 2014 BLES Survey Sampling Frame (2014 SSF) is an integrated list of establishments culled from the updated 2012 BLES Survey Sampling Frame based on the status of establishments reported in the 2011/2012 BLES Integrated Survey (BITS) and 2012 Occupational Wages Survey (OWS). Other sources were Lists of Establishments from the National Statistics Office (2012), DOLE Regional Office IV-B,and the BLES Job Displacement Monitoring System (JDMS).

Sampling design: The OWS is a sample survey of agricultural and non-agricultural establishments employing 20 persons or more where the survey domain is the industry. Those establishments employing at least 200 persons are covered with certainty and the rest are sampled (stratified random sampling). The design does not consider the region as a domain to allow for detailed industry groupings.

Sample size: For 2014 OWS, the number of establishments covered was 8,399, of which, 6,595 were eligible units.

Mode of data collection

Other [oth]

Research instrument

The questionnaire contains the following sections:

Cover Page (Page 1) This contains the address box, contact particulars for assistance, spaces for changes in the name and location of sample establishment and head office information in case the questionnaire is endorsed to it and status codes of the establishment to be accomplished by PSA and its field personnel.

Survey Information (Page 2) This contains the survey objective and uses of the data, scope of the survey, confidentiality clause, collection authority, authorized field personnel, coverage, periodicity and reference period, due date for accomplishment and expected date when the results of the 2014 OWS would be available.

Part A: General Information (Page 3) This portion inquires on main economic activity, major products/goods or services and total employment.

Part B: Employment and Wage Rates of Time-Rate Workers on Full-Time Basis (Pages 4-5) This section requires data on the number of time-rate workers on full-time basis by time unit and by basic pay and allowance intervals.

Part C: Employment and Wage Rates of Time-Rate Workers on Full-Time Basis in Selected Occupations (Pages 6-9) This part inquires on the basic pay and allowance per time unit and corresponding number of workers in the two benchmark occupations and in the pre-determined occupations listed in the occupational sheet to be provided to the establishment where applicable.

Part D: Certification (Page 10) This portion is provided for the respondent's name/signature, position, telephone no., fax no. and e-mail address and time spent in answering the questionnaire.

Appropriate spaces are also provided to elicit comments on data provided for the 2014 OWS; results of the 2012 OWS; and presentation/packaging, particularly on the definition of terms, layout, font and color.

Part E: Survey Personnel (Page 10) This portion is for the particulars of the enumerators and area/regional supervisors and reviewers at the PSA Central Office and PSA Field Offices involved in the data collection and review of questionnaire entries.

Part F: Industries With Selected Occupations (Page 11) The list of industries for occupational wage monitoring has been provided to guide the enumerators in ensuring that the correct occupational sheet has been furnished to the respondent.

Selected Statistics from 2012 OWS (Page 12) The results of the 2012 OWS are found on page 12 of the questionnaire. These results can serve as a guide to the survey personnel in editing/review of the entries in the questionnaire.

Cleaning operations

Data are manually and electronically processed. Upon collection of accomplished questionnaires, enumerators perform field editing before leaving the establishments to ensure completeness, consistency and reasonableness of entries in accordance with the field operations manual. The forms are again checked for data consistency and completeness by their field supervisors.

The LSRSD personnel undertake the final review, coding of information on classifications used, data entry and validation and scrutiny of aggregated results for coherence. Questionnaires with incomplete or inconsistent entries are returned to the establishments for verification, personally or through mail.

Response rate

The response rate in terms of eligible units was 87.2%.

Data appraisal

The survey results are checked for consistency with the results of previous OWS data and the minimum wage rates corresponding to the reference period of the survey.

Average wage rates of unskilled workers by region is compared for proximity with the corresponding minimum wage rates during the survey reference period.

This dataset provides information about an Entity, it's registered Agents and shares details

Patient experience. Source agency: Health Designation: National Statistics Language: English Alternative title: Patient Experience PSA Scores

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.

Abstract

The Quarterly Aquaculture Survey aims to generate accurate and timely information on quarterly production, area and price by aquafarm type and species at the provincial level. It asks for the actual level of production, area harvested and price for each species during the reference quarter of the current and previous year from the sample operators in the top producing municipalities. Fisheries outputs form part of the estimation for the performance of agriculture and eventually, of the National Accounts for the generation GVA, GNP and GDP.

Geographic coverage

The survey is conducted in 82 provinces nationwide.

Analysis unit

• Aquafarm

Universe

The survey covers aquaculture operations in 82 provinces of 17 aquaculture species in 13 aquafarm types/environments. The following are the aquafarm types and environments covered: - Brackishwater and freshwater fishpond - Brackishwater, freshwater and marine pen and cage - Oyster, mussel and seaweed - Other freshwater aquafarms like rice fish, SFR, etc.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Quarterly Aquaculture Survey is a non-probability survey. Selection of farms was done by aquafarm type in the province. By aquafarm type, top producing municipalities are those with cumulative share of at least 80% to total area based on Aquaculture Farms Inventory (AqFI).

For each municipality, eight sample aquafarms are selected, if the number of aquafarms in the municipality is more than 25. If the number of aquafarms is less than 25, five sample aquafarms are selected. A total of 6,662 sample aquafarms were covered nationwide.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were five QAqS survey forms that were being used depending on the type of aquafarm: - QAqS Form 1 - Fishpond - QAqS Form 2 - Pen and Cage - QAqS Form 3 - Oyster, Mussel and Seaweed - QAqS Form 4 - Hatchery - QAqS Form 5 - Other Freshwater Farms

The datasets were the same for all the forms except for the section on species cultured applicable to the type of an aquafarm.

Cleaning operations

Initially, the survey returns are manually edited to ensure completeness and accuracy. During this stage, survey returns are checked for completeness from the list of samples. For each of the survey forms, entries should be complete and numeric entries are in proper unit of measurement and decimal places. After encoding, the entries are again inspected and reviewed for completeness, accuracy and consistency with other items.

Response rate

Response rate is 73%. This accounted for farms in operation and those without harvest during the reference period.

Sampling error estimates

Not computed