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 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.

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.

Philippines BPM: NES: On-line Employment Placement Agencies data was reported at 8.000 Unit in 2015. This records a decrease from the previous number of 9.000 Unit for 2014. Philippines BPM: NES: On-line Employment Placement Agencies data is updated yearly, averaging 9.000 Unit from Dec 2013 (Median) to 2015, with 3 observations. The data reached an all-time high of 9.000 Unit in 2014 and a record low of 8.000 Unit in 2015. Philippines BPM: NES: On-line Employment Placement Agencies data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.S007: Annual Survey of Philippine Business and Industry (ASPBI): Business Process Management (BPM): Number of Establishments.

Abstract

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

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

Geographic coverage

National coverage

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

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

Mode of data collection

Face-to-face [f2f]

Research instrument

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

Cleaning operations

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

Response rate

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

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

Sampling error estimates

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

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

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

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

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

Data appraisal

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

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

Philippines Data Center Server Market size was valued at USD 2.8 Billion in 2024 and is expected to reach USD 10.3 Billion by 2032, growing at a CAGR of 17.6% from 2026 to 2032.

Philippines Data Center Server Market Drivers

Rapid Digital Transformation: The rapid digital transformation is driving the Philippines data center server market. The Department of Information and Communications Technology (DICT), the country's digital adoption index has risen from 0.41 in 2016 to 0.57 in 2023. By 2024, 79.7% of government agencies have switched to cloud services, driving up demand for modern data center equipment. Businesses are also growing their digital operations, necessitating scalable and secure server solutions.

Increasing Adoption of Cloud Services: The increasing adoption of cloud services is propelling the Philippines data center server market. The Philippine Statistics Authority reports that cloud computing utilization among businesses climbed from 27% in 2019 to 46% in 2023. The Philippine Cloud Computing Association, 73% of firms aim to increase their cloud spending by 2025. As demand for cloud-based applications and storage grows, data centers are upgrading their infrastructure to enable them.

Growth in Internet Usage and Connectivity: The Growth in internet usage and connectivity boost the Philippines data center server market. According to the Digital 2024 Philippines report, internet users reached 85.5 million in 2023, accounting for 76% of the population, up 9.2% from the previous year. This increased digital activity is generating demand for improved data processing and storage technologies. Businesses and service providers are extending their data infrastructure to handle more traffic and online transactions. Improved connectivity accelerates cloud usage and digital services.

Philippines BPM: NES: Website Hosting Services data was reported at 19.000 Unit in 2015. This records a decrease from the previous number of 29.000 Unit for 2014. Philippines BPM: NES: Website Hosting Services data is updated yearly, averaging 23.000 Unit from Dec 2013 (Median) to 2015, with 3 observations. The data reached an all-time high of 29.000 Unit in 2014 and a record low of 19.000 Unit in 2015. Philippines BPM: NES: Website Hosting Services data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.S007: Annual Survey of Philippine Business and Industry (ASPBI): Business Process Management (BPM): Number of Establishments.

According to a survey by Rakuten Insight in the Philippines in *********, the majority of both male and female respondents stated that they had not used an online travel agency (OTA). The survey identified booking accommodation as the most common reason for using OTAs.

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

Abstract

Considering tourism as driver and contributor to the economic growth of the country, a national policy on tourism was passed - the Republic Act Numbered 9593 otherwise known as Tourism Act of 2009.With this Act, the State declares tourism “as an indispensable element of the national economy and an industry of national interest and importance, which must be harnessed as an engine of socio-economic growth and cultural affirmation to generate investment, foreign exchange and sense of national pride for all Filipinos”. Moreover, the Philippine Statistical Development Plan provides the blueprint of development of the tourism industry. This is in recognition of the importance, role and impact of tourism on the social and economic development and environment and cultural landscape in the country. Therefore, there is a need to measure the economic contribution of tourism as an input to effective and efficient policy research, monitoring, analysis and development of the tourism industry.

The Philippine Tourism Satellite Account (PTSA) provides the framework by which the economic contribution of tourism is measured. Using this account, it is possible to quantify the contribution of tourism industry in the economy within the context of the Philippine Systems of National Accounts (PSNA).

The Philippine Statistics Authority (PSA) conducted the 2014 Survey of Tourism Establishments in the Philippines (STEP) second round. The 2014 STEP was a nationwide survey of establishments in the formal sector engaged in tourism characteristic industries. This survey collected information on the available supply of tourism goods, products and services, which are valuable inputs in the compilation of the PTSA.

The general objective of the 2014 STEP is to provide data on tourism characteristic establishments in the country.

Specifically, the 2014 STEP aims to: - provide data on the supply and capacity in terms of facilities and services - gather data on employment by sex and nationality - gather data on revenue generated from tourist - provide information on indicators for future expansion and/or renovation plans

Geographic coverage

Establishment with Total Employment (TE) 20 and Over - National and Regional Level Establishment with TE Less than 20 - National Level

Analysis unit

Establishment - defined as an economic unit, which engages, under a single ownership or control, i.e. under a single legal entity, in one or predominantly one kind of economic activity at a single fixed physical location. Thus, stores, shops, transport companies, hotels, restaurants, banks, insurance companies, real estate development companies and the like are considered establishments.

Universe

All tourism characteristic establishments operating in 2014.

Tourism characteristic industries as defined in the IRTS 2008 (International Recommendation of Tourism Statistics) with grouping according to industry sub-class (5-digit) of the 2009 PSIC.

It covered the following tourism characteristic industries: - Accommodation (I55 except I55901- Dormitories/boarding houses); - Chartered buses and cars operation (e.g. tourist buses, rent-a-car) (H49204 and H49324).

The other tourism characteristic industries were taken on a sampling basis.

Kind of data

Sample survey data [ssd]

Sampling procedure

In general, the establishment with total employment (TE) 100 and over is a certainty stratum for industries covered in the 2014 STEP.

For the purposes of 2014 STEP, only the following tourism characteristic industries regardless of employment size were completely enumerated (100% coverage): - I55101 - Hotel and motels - I55102 - Resort Hotels - I55103 - Condotels - I55104 - Pension Houses - I55105 - Camping sites/facilities - I55109 - Other shorts term accommodation activities - I55909 - Other accommodation

The sample establishments in the sampling strata of TE of less than 20 were selected using systematic sampling by industry domain and employment stratum at the national level. For each industry domain and employment stratum, the establishments are sorted by region, province from largest actual employment to smallest actual employment, business name and ECN.

For each of the sampling strata of TE of 20 and over (i.e. TE 20-49 and TE 50-99) sample establishments were selected using systematic sampling within the region. For each region in the employment stratum and industry domain, the establishments are sorted by province from largest actual employment to smallest actual employment, business name and ECN.

Systematic sampling was chosen so that the sample employment values were spread out, resulting from having representative samples for each TE size in the employment stratum. Likewise, this mode of sampling provided implicit stratification of TE by employment size group, thus avoiding all sample establishments with low TE values or high TE values.

Mode of data collection

Other [oth]

Research instrument

The following questionnaires were used in the survey: - STEP Form 1: Accommodation - STEP Form 2: Restaurants - STEP Form 3: Transport Operators; Tour and Travel Agencies - STEP Form 4: Health and Wellness - STEP Form 5: Other Tourism Activities

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 - completeness of entries check - consistency check among variables

Data processing was done in Field Offices and Central Office.

Field Offices: - completeness and consistency edits - folioing of questionnaires

Central Office: - online data encoding and updating - online validation - completeness and consistency checks - summarization - tabulation

Response rate

Total response rate as of 30 September 2016 for all establishments by tourism characteristic industry was 85.2 percent (6,142 out of 7,210 establishments).

Of the total responses, 65 establishments responded online.

Sampling error estimates

Not computed

Data appraisal

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

Abstract

The Philippine Statistics Authority generates various establishment based price indices and one of these is the Producers Price Index. The 2013 PPI (2000 base year) is generated from the results of the Producer Price Survey conducted monthly by the PSA. This is done through the collection of actual producer prices from sample establishments nationwide. The PPS uses a shuttle type questionnaire which provides the respondent establishments with a running account of all monthly responses for the year. For 2013, the survey covered 595 sample products produced by 309 manufacturing establishments.

The Producer Price Index (PPI) for Manufacturing is a composite figure of producers prices of representative commodities included in the market basket. The PPI serves various purposes, the most important of which are the following: - measures monthly or yearly changes in the producers prices of key commodities in the manufacturing sector; - serves as deflator to Value of Production Index (VAPI) in the estimation of Volume of Production Index (VOPI); - serves as deflator in the estimation of manufacturing production in real terms (at constant prices) in the system of national accounts.

The PPI is computed using the Paasche-type method of index computation. As such, the weights are continously revised upon availability of the latest data from the annual survey or census. In the case of 2013 PPI, the weights are taken from the 2010 Annual Survey of Philippine Business and Industry (ASPBI). The revision of weights , are however instituted at the beginning of each year and are used for the entire year.

Geographic coverage

National

Analysis unit

Establishment - 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 PPS covers formal sector manufacturing establishments with total employment of 20 and over.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2013 PPS is a non-probability sampling survey of the manufacturing sector. Sample establishments and commodities were selected using the following criteria: a. the commodity has a relatively high market share b. the commodity was available in the market in 2000, this being the base year c. the commodity is being produced currently, and d. the market share of the commodity has been stable for the last 3 years

In the same manner, criteria were also set for the selection of establishments, as follows: a. establishment has an average total employemeny of 50 and over b. establishment has relatively high concentration ratio c. establishment is good respondent in past and current surveys of PSA; that is, it submits prompt reports and provides quality data d. preferably, the establishment is a sample of the MISSI.

The 2013 PPS utilizes the 3-digit and selected 4-digit ammended PSIC as its industry domain which is patterned after ISIC version 3. Thus, there are 20 major sectors with 10 further categorized into sub-sectors or a total of 37 sub-sectors for the 2013 MISSI/PPS.

Mode of data collection

Face-to-face [f2f]

Research instrument

The PPS went through the clearance process of the National Statistical Coordination Board (NSCB). It utilizes a shuttle type questionnaire with NSCB approval number and expiration date. The field offices distribute the questionnaires at the beginning of the year and collects the data on a monthly basis.

Cleaning operations

It is important to verify the reasonableness and reliability of the prices of products included in the market basket for a given month. Data editing consisted of three stages: field editing, office verification and machine validation.

Field editing of data was done by the provincial staff upon collection of the accomplished questionnaires from the establishments. The objective is to check for completeness and consistency of entries in the questionnaires. Any inconsistent or missing data was corrected at this stage as this can be immediately verified from the respondents.

Office verification was done by provincial office staff upon receipt of the accomplished questionnaires from the field men. In some instances, callback to the establishments in the form of phone call or email to verify some inconsistent or missing data is done.

Desk verification was done by the ISD staff to check the consistency and reasonableness of entries in the accomplished questionnaires. This process also validates the status of establishments such as non-responding and reported closed, cannot be located, transferred, and out of scope. The telephone was extensively utilized to verify information from the establishment's contact person. The Internet was also used to obtain information on the contact address and to research for information on the status of the establishment.

For unit or item non-response, the following are undertaken: - Establishments that stopped operation, temporary out of business, on strike, etc., during the year, historical imputation without trend adjustment or the use of the latest available data of the establishment. This method is appropriate for the reason that the prices of a number of products/commodities do not change very much over a short period of time. - Imputed values are revised upon receipt of actual data for inclusion in the revised indices.

Response rate

The average monthly response rate is 88.84%, 35 days (preliminary tabulation) after the reference month and 95 % for the final table.

Sampling error estimates

Not applicable.

Data appraisal

The quality of the PPI indicators are measured in terms of the following:

• Representativeness of the samples as measured in the Concentration Ratio - the combined production value of the samples as a percentage to the total industry production value;
• Response rate of the survey;
• Imputation method used for non-responses.

Philippines BPM: Employment: On-line Employment Placement Agencies data was reported at 73.000 Person in 2015. This records a decrease from the previous number of 104.000 Person for 2014. Philippines BPM: Employment: On-line Employment Placement Agencies data is updated yearly, averaging 104.000 Person from Dec 2013 (Median) to 2015, with 3 observations. The data reached an all-time high of 106.000 Person in 2013 and a record low of 73.000 Person in 2015. Philippines BPM: Employment: On-line Employment Placement Agencies data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.S009: Annual Survey of Philippine Business and Industry (ASPBI): Business Process Management (BPM): Employment.

According to a survey by Rakuten Insight in the Philippines in June 2023, ** percent of the respondents aged 16 and 24 years stated that they had not used an online travel agency. By comparison, ** percent of respondents between 25 and 34 years and 45 and 54 years old stated that they used an online travel agency.

Philippines BPM: Revenue: On-line Employment Placement Agencies data was reported at 29,951.000 PHP in 2015. This records a decrease from the previous number of 75,111.000 PHP for 2014. Philippines BPM: Revenue: On-line Employment Placement Agencies data is updated yearly, averaging 48,752.000 PHP from Dec 2013 (Median) to 2015, with 3 observations. The data reached an all-time high of 75,111.000 PHP in 2014 and a record low of 29,951.000 PHP in 2015. Philippines BPM: Revenue: On-line Employment Placement Agencies data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.S008: Annual Survey of Philippine Business and Industry (ASPBI): Business Process Management (BPM): Revenue.

Unemployment Rate in Philippines decreased to 3.90 percent in May from 4.10 percent in April of 2025. This dataset provides - Philippines Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Philippines BPM: Employment: Website Hosting Services data was reported at 294.000 Person in 2015. This records a decrease from the previous number of 738.000 Person for 2014. Philippines BPM: Employment: Website Hosting Services data is updated yearly, averaging 533.000 Person from Dec 2013 (Median) to 2015, with 3 observations. The data reached an all-time high of 738.000 Person in 2014 and a record low of 294.000 Person in 2015. Philippines BPM: Employment: Website Hosting Services data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.S009: Annual Survey of Philippine Business and Industry (ASPBI): Business Process Management (BPM): Employment.

Abstract

The Global Adult Tobacco Survey (GATS) is the global standard to systematically monitor adult tobacco use and track key tobacco control indicators. GATS is a nationally representative household survey of adults 15 years of age or older, using a standard protocol. It is intended to generate comparable data within and across countries. GATS enhances countries' capacity to design, implement and evaluate tobacco control interventions.

Geographic coverage

National

Analysis unit

Individuals

Universe

Adults aged 15 years or older.

Kind of data

Sample survey data [ssd]

Sampling procedure

Using a multistage stratified cluster sample design, a total of 20,971 households were sampled and one individual was randomly selected from each participating household to complete the survey.

Mode of data collection

Face-to-face [f2f]

Response rate

There were a total of 18,466 completed individual interviews, with an overall response rate of 97.0%.

Philippines BPM: Compensation: On-line Employment Placement Agencies data was reported at 22,715.000 PHP in 2015. This records a decrease from the previous number of 32,979.000 PHP for 2014. Philippines BPM: Compensation: On-line Employment Placement Agencies data is updated yearly, averaging 22,715.000 PHP from Dec 2013 (Median) to 2015, with 3 observations. The data reached an all-time high of 32,979.000 PHP in 2014 and a record low of 19,555.000 PHP in 2013. Philippines BPM: Compensation: On-line Employment Placement Agencies data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.S010: Annual Survey of Philippine Business and Industry (ASPBI): Business Process Management (BPM): Compensation.

Philippines BPM: Revenue: Website Hosting Services data was reported at 104,865.000 PHP in 2015. This records a decrease from the previous number of 323,421.000 PHP for 2014. Philippines BPM: Revenue: Website Hosting Services data is updated yearly, averaging 227,270.000 PHP from Dec 2013 (Median) to 2015, with 3 observations. The data reached an all-time high of 323,421.000 PHP in 2014 and a record low of 104,865.000 PHP in 2015. Philippines BPM: Revenue: Website Hosting Services data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.S008: Annual Survey of Philippine Business and Industry (ASPBI): Business Process Management (BPM): Revenue.

According to a survey by Rakuten Insight on online travel agencies (OTA) conducted in the Philippines in June 2023, ** percent of the respondents stated that they had not used an online travel agency. By comparison, ** percent of the respondents said they had.