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.

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.

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.

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

Philippines administrative levels:

(0) Country

(1) Region (Filipino: rehiyon)

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

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

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

Abstract

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

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

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

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

Geographic coverage

National Coverage

Analysis unit

Entreprises

Universe

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

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

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

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

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

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

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

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

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

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

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

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

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

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

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

Response rate

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

Data appraisal

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

Abstract

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.

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

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

Key information about Philippines Gross National Product (GNP)

• Philippines Gross National Product (GNP) was reported at 134.069 USD bn in Sep 2025.
• This records a decrease from the previous number of 141.660 USD bn for Jun 2025.
• Philippines Gross National Product (GNP) data is updated quarterly, averaging 23.824 USD bn from Mar 1981 to Sep 2025, with 179 observations.
• The data reached an all-time high of 147.570 USD bn in Dec 2024 and a record low of 6.693 USD bn in Sep 1985.

• Philippines Gross National Product (GNP) data remains active status in CEIC and is reported by CEIC Data.
• CEIC converts quarterly Gross National Product into USD. The Philippine Statistics Authority provides Gross National Product in local currency. Bangko Sentral ng Pilipinas average market exchange rate is used for currency conversions. Gross National Product prior to Q1 2000 is based on old classification of data.

The data underlying the public sector finances statistical bulletin are presented in the tables PSA 1 to 10.

In 2024, the Philippines’ consumer price inflation rate amounted to 3.21 percent compared to the previous year. The Philippines are considered “newly industrialized”, but the economy relies on remittances from nationals overseas, and the services sector generates most of its GDP. Emerging and soon to develop?After switching from agriculture to services and manufacturing, the Philippines are now an emerging economy, i.e. the country has some characteristics of a developed nation but is not quite there yet. In order to transition into a developed nation, the Philippines must meet certain requirements, like being able to sustain their economic development, being very open to foreign investors, or maintaining a very high stability of the institutional framework (like law enforcement and the government). Only if these changes are irreversible can they be classified as a developed nation. The Philippines’ switch to servicesEver since the switch to services and manufacturing, employment in these areas has increased and the country is now among those with the highest employment in the tourism industry worldwide. This transition was not entirely voluntary but also due to decreasing government support, the liberalization of trade, and reform programs. Still, agriculture is important for the country: As of 2017, more than a quarter of Filipinos are still working in the agricultural sector, and urbanization has only increased very slightly over the last decade.

Inflation Rate in Philippines remained unchanged at 1.70 percent in October. This dataset provides the latest reported value for - Philippines Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Growing corn varies depending on the area, and its production cycle is different in all parts of the world. In the Philippines, corn production is based on the landscape and topography of an area. In 2023, the production volume of corn in the Philippines amounted to approximately *** million metric tons, higher than the produced quantity of *** million metric tons in the previous year. Corn farming Over the past six years, about *** million hectares of land were utilized for cultivating corn in the Philippines. Despite fluctuation in production, corn remains among the leading crops produced in the Philippines. The Philippines is also one of the biggest corn producing countries globally. Corn industry in the Philippines Aside from rice, corn is considered another staple crop in the Philippines. The country has six common varieties — sweet corn, wild violet corn, white lagkitan, Visayan white corn, purple, and young corn. Some of the country's corn production is exported, especially maize seeds and frozen sweet corn.