Time series data for the statistic Rural population (% of total population) and country Philippines. Indicator Definition:Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.The indicator "Rural population (% of total population)" stands at 51.39 as of 12/31/2024, the lowest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -0.6323 percent compared to the value the year prior.The 1 year change in percent is -0.6323.The 3 year change in percent is -1.78.The 5 year change in percent is -2.77.The 10 year change in percent is -4.68.The Serie's long term average value is 58.31. It's latest available value, on 12/31/2024, is 11.88 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1960, to it's latest available value, on 12/31/2024, is -26.28%.

Philippines PH: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data was reported at 3.283 % in 2010. This records a decrease from the previous number of 3.288 % for 2000. Philippines PH: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data is updated yearly, averaging 3.288 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 3.292 % in 1990 and a record low of 3.283 % in 2010. Philippines PH: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank: Land Use, Protected Areas and National Wealth. Rural population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

Philippines PH: Rural Population: % of Total Population data was reported at 53.318 % in 2017. This records a decrease from the previous number of 53.525 % for 2016. Philippines PH: Rural Population: % of Total Population data is updated yearly, averaging 54.742 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 69.703 % in 1960 and a record low of 53.014 % in 1990. Philippines PH: Rural Population: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Population and Urbanization Statistics. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Weighted average;

Philippines Enrolment Data

Examining Private and Public Schools

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides an interesting insight into the enrolment numbers in public and private schools across the Philippines. It covers all levels of enrolment – elementary, secondary, and post-secondary – as well as gender and urban/rural distinctions. This information is an invaluable asset for anyone looking to gain a comprehensive understanding of educational enrolment trends within the country in order to make informed decisions regarding resource allocation or policy implementations. However, keep in mind that due to differences in methodology and data collection techniques, caution should be taken when using this data as there may be inaccuracies or vague definitions applicable to specific age groups or subpopulations. Regardless, this dataset still serves as a valuable source of information for anyone wanting a proper picture of educational dynamics within the Philippines

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How to use the dataset

This dataset provides enrolment figures in public and private schools by level in the Philippines. With this data, users can explore disparities between public and private school enrolment and other potential inequalities associated with educational access.

In order to use this Kaggle dataset to analyze educational inequality in the Philippines, firstly one must understand which columns are included:

• Country: The name of the Philippine country
• School Level (Grouped): Groupings of school levels within primary/elementary and secondary level
• Enrolment Type: Public or Private
• Year: Time period of data collection

Now that you have an understanding about what this dataset contains, here are few ways you could use it for your analysis!

• Compare enrollment rates between genders - Use the 'School Level' column grouped into Primary/Elementary or Secondary fields along with 'Enrolment Type' (public vs. private) to sort out male/female enrollment differences from 2007 - 2018 at each grade level.
• Investigate discrepancies between urban vs rural areas - Look at where most students attend as reflected through the different divisions within provinces as defined by Commission on Elections (COMELEC). Depending if pupils mainly take up residence in urban or rural areas make sure to supplement this data with available measures towards educational disparities between these two settings such as infrastructure, resources etc.
• Analyze expansion trends over time - Using all columns within this dataset one could see how trends have changed over time since its inception year 2007 till recent year 2018 spanning different area types (such as mindanao through CAR etc.), school levels and regions across governance such provinces(NCR etc.).One could get additional insights such patterns around funding allocations too.

Using all these different analyses offered one can gain a better understanding about evolving disparities around education access in particular region or even countrywide!

Research Ideas

• Comparing enrolment statistics between public and private schools to identify more effective approaches in either sector.
• Identifying regions or areas which may benefit from additional investment in education infrastructure and resources.
• Visualizing enrolment rates at different levels of schooling to understand the relative level of educational attainment within a certain geographical area or region over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: education-nscb-xls-1.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Humanitarian Data Exchange.

Philippines PH: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data was reported at 2.368 % in 2010. This records an increase from the previous number of 2.359 % for 2000. Philippines PH: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data is updated yearly, averaging 2.368 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 2.461 % in 1990 and a record low of 2.359 % in 2000. Philippines PH: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank: Land Use, Protected Areas and National Wealth. Urban population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

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.

The 2008 National Demographic and Health Survey (2008 NDHS) is a nationally representative survey of 13,594 women age 15-49 from 12,469 households successfully interviewed, covering 794 enumeration areas (clusters) throughout the Philippines. This survey is the ninth in a series of demographic and health surveys conducted to assess the demographic and health situation in the country. The survey obtained detailed information on fertility levels, marriage, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and young children, childhood mortality, maternal and child health, and knowledge and attitudes regarding HIV/AIDS and tuberculosis. Also, for the first time, the Philippines NDHS gathered information on violence against women. The 2008 NDHS was conducted by the Philippine National Statistics Office (NSO). Technical assistance was provided by ICF Macro through the MEASURE DHS program. Funding for the survey was mainly provided by the Government of the Philippines. Financial support for some preparatory and processing phases of the survey was provided by the U.S. Agency for International Development (USAID). Like previous Demographic and Health Surveys (DHS) conducted in the Philippines, the 2008 National Demographic and Health Survey (NDHS) was primarily designed to provide information on population, family planning, and health to be used in evaluating and designing policies, programs, and strategies for improving health and family planning services in the country. The 2008 NDHS also included questions on domestic violence. Specifically, the 2008 NDHS had the following objectives: Collect data at the national level that will allow the estimation of demographic rates, particularly, fertility rates by urban-rural residence and region, and under-five mortality rates at the national level. Analyze the direct and indirect factors which determine the levels and patterns of fertility. Measure the level of contraceptive knowledge and practice by method, urban-rural residence, and region. Collect data on family health: immunizations, prenatal and postnatal checkups, assistance at delivery, breastfeeding, and prevalence and treatment of diarrhea, fever, and acute respiratory infections among children under five years. Collect data on environmental health, utilization of health facilities, prevalence of common noncommunicable and infectious diseases, and membership in health insurance plans. Collect data on awareness of tuberculosis. Determine women's knowledge about HIV/AIDS and access to HIV testing. Determine the extent of violence against women. MAIN RESULTS FERTILITY Fertility Levels and Trends. There has been a steady decline in fertility in the Philippines in the past 36 years. From 6.0 children per woman in 1970, the total fertility rate (TFR) in the Philippines declined to 3.3 children per woman in 2006. The current fertility level in the country is relatively high compared with other countries in Southeast Asia, such as Thailand, Singapore and Indonesia, where the TFR is below 2 children per woman. Fertility Differentials. Fertility varies substantially across subgroups of women. Urban women have, on average, 2.8 children compared with 3.8 children per woman in rural areas. The level of fertility has a negative relationship with education; the fertility rate of women who have attended college (2.3 children per woman) is about half that of women who have been to elementary school (4.5 children per woman). Fertility also decreases with household wealth: women in wealthier households have fewer children than those in poorer households. FAMILY PLANNING Knowledge of Contraception. Knowledge of family planning is universal in the Philippines- almost all women know at least one method of fam-ily planning. At least 90 percent of currently married women have heard of the pill, male condoms, injectables, and female sterilization, while 87 percent know about the IUD and 68 percent know about male sterilization. On average, currently married women know eight methods of family planning. Unmet Need for Family Planning. Unmet need for family planning is defined as the percentage of currently married women who either do not want any more children or want to wait before having their next birth, but are not using any method of family planning. The 2008 NDHS data show that the total unmet need for family planning in the Philippines is 22 percent, of which 13 percent is limiting and 9 percent is for spacing. The level of unmet need has increased from 17 percent in 2003. Overall, the total demand for family planning in the Philippines is 73 percent, of which 69 percent has been satisfied. If all of need were satisfied, a contraceptive prevalence rate of about 73 percent could, theoretically, be expected. Comparison with the 2003 NDHS indicates that the percentage of demand satisfied has declined from 75 percent. MATERNAL HEALTH Antenatal Care. Nine in ten Filipino mothers received some antenatal care (ANC) from a medical professional, either a nurse or midwife (52 percent) or a doctor (39 percent). Most women have at least four antenatal care visits. More than half (54 percent) of women had an antenatal care visit during the first trimester of pregnancy, as recommended. While more than 90 percent of women who received antenatal care had their blood pressure monitored and weight measured, only 54 percent had their urine sample taken and 47 percent had their blood sample taken. About seven in ten women were informed of pregnancy complications. Three in four births in the Philippines are protected against neonatal tetanus. Delivery and Postnatal Care. Only 44 percent of births in the Philippines occur in health facilities-27 percent in a public facility and 18 percent in a private facility. More than half (56 percent) of births are still delivered at home. Sixty-two percent of births are assisted by a health professional-35 percent by a doctor and 27 percent by a midwife or nurse. Thirty-six percent are assisted by a traditional birth attendant or hilot. About 10 percent of births are delivered by C-section. The Department of Health (DOH) recommends that mothers receive a postpartum check within 48 hours of delivery. A majority of women (77 percent) had a postnatal checkup within two days of delivery; 14 percent had a postnatal checkup 3 to 41 days after delivery. CHILD HEALTH Childhood Mortality. Childhood mortality continues to decline in the Philippines. Currently, about one in every 30 children in the Philippines dies before his or her fifth birthday. The infant mortality rate for the five years before the survey (roughly 2004-2008) is 25 deaths per 1,000 live births and the under-five mortality rate is 34 deaths per 1,000 live births. This is lower than the rates of 29 and 40 reported in 2003, respectively. The neonatal mortality rate, representing death in the first month of life, is 16 deaths per 1,000 live births. Under-five mortality decreases as household wealth increases; children from the poorest families are three times more likely to die before the age of five as those from the wealthiest families. There is a strong association between under-five mortality and mother's education. It ranges from 47 deaths per 1,000 live births among children of women with elementary education to 18 deaths per 1,000 live births among children of women who attended college. As in the 2003 NDHS, the highest level of under-five mortality is observed in ARMM (94 deaths per 1,000 live births), while the lowest is observed in NCR (24 deaths per 1,000 live births). NUTRITION Breastfeeding Practices. Eighty-eight percent of children born in the Philippines are breastfed. There has been no change in this practice since 1993. In addition, the median durations of any breastfeeding and of exclusive breastfeeding have remained at 14 months and less than one month, respectively. Although it is recommended that infants should not be given anything other than breast milk until six months of age, only one-third of Filipino children under six months are exclusively breastfed. Complementary foods should be introduced when a child is six months old to reduce the risk of malnutrition. More than half of children ages 6-9 months are eating complementary foods in addition to being breastfed. The Infant and Young Child Feeding (IYCF) guidelines contain specific recommendations for the number of times that young children in various age groups should be fed each day as well as the number of food groups from which they should be fed. NDHS data indicate that just over half of children age 6-23 months (55 percent) were fed according to the IYCF guidelines. HIV/AIDS Awareness of HIV/AIDS. While over 94 percent of women have heard of AIDS, only 53 percent know the two major methods for preventing transmission of HIV (using condoms and limiting sex to one uninfected partner). Only 45 percent of young women age 15-49 know these two methods for preventing HIV transmission. Knowledge of prevention methods is higher in urban areas than in rural areas and increases dramatically with education and wealth. For example, only 16 percent of women with no education know that using condoms limits the risk of HIV infection compared with 69 percent of those who have attended college. TUBERCULOSIS Knowledge of TB. While awareness of tuberculosis (TB) is high, knowledge of its causes and symptoms is less common. Only 1 in 4 women know that TB is caused by microbes, germs or bacteria. Instead, respondents tend to say that TB is caused by smoking or drinking alcohol, or that it is inherited. Symptoms associated with TB are better recognized. Over half of the respondents cited coughing, while 39 percent mentioned weight loss, 35 percent mentioned blood in sputum, and 30 percent cited coughing with sputum. WOMEN'S STATUS Women's Status and Employment.

Philippines PH: Urban Population data was reported at 48,977,863.000 Person in 2017. This records an increase from the previous number of 48,018,073.000 Person for 2016. Philippines PH: Urban Population data is updated yearly, averaging 27,236,081.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 48,977,863.000 Person in 2017 and a record low of 7,959,938.000 Person in 1960. Philippines PH: Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Population and Urbanization Statistics. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Sum;

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 PH: Rural Population data was reported at 55,940,227.000 Person in 2017. This records an increase from the previous number of 55,302,149.000 Person for 2016. Philippines PH: Rural Population data is updated yearly, averaging 32,382,455.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 55,940,227.000 Person in 2017 and a record low of 18,313,087.000 Person in 1960. Philippines PH: Rural Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Population and Urbanization Statistics. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population. Aggregation of urban and rural population may not add up to total population because of different country coverages.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Sum;

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

The 2003 National Demographic and Health Survey (NDHS) is a nationally representative survey of 13,945 women age 15-49 and 5,009 men age 15-54. The main purpose of the 2003 NDHS is to provide policymakers and program managers with detailed information on fertility, family planning, childhood and adult mortality, maternal and child health, and knowledge and attitudes related to HIV/AIDS and other sexually transmitted infections. The 2003 NDHS also collects high quality data on family health: immunizations, prevalence and treatment of diarrhea and other diseases among children under five, antenatal visits, assistance at delivery and breastfeeding. The 2003 NDHS is the third national sample survey undertaken in Philippines under the auspices of the worldwide Demographic and Health Surveys program. The 2003 Philippines National Demographic and Health Survey (NDHS) is designed to provide upto-date information on population, family planning, and health to assist policymakers and program managers in evaluating and designing strategies for improving health and family planning services in the country. In particular, the 2003 NDHS has the following objectives: Collect data at the national level, which will allow the calculation of demographic rates and, particularly, fertility and under-five mortality rates. Analyze the direct and indirect factors that determine the level and trends of fertility. Indicators related to fertility will serve to inform plans for social and economic development. Measure the level of contraceptive knowledge and practice by method, urban-rural residence, and region. Collect data on knowledge and attitudes of women and men about sexually transmitted infections and HIV/AIDS and evaluate patterns of recent behavior regarding condom use. Collect high-quality data on family health, including immunizations, prevalence and treatment of diarrhea and other diseases among children under five, antenatal visits, assistance at delivery, and breastfeeding.

The International Social Survey Programme (ISSP) is a continuous programme of cross-national collaboration running annual surveys on topics important for the social sciences. The programme started in 1984 with four founding members - Australia, Germany, Great Britain, and the United States – and has now grown to almost 50 member countries from all over the world. As the surveys are designed for replication, they can be used for both, cross-national and cross-time comparisons. Each ISSP module focuses on a specific topic, which is repeated in regular time intervals. Please, consult the documentation for details on how the national ISSP surveys are fielded. The present study focuses on questions about social inequality. The release of the cumulated ISSP ´Social Inequality´ modules for the years 1987, 1992, 1999 and 2009 consists of two separate datasets: ZA5890 and ZA5891. This documentation deals with the supplementary dataset ZA5891. This dataset contains in addition to some only apparently cumulated substantial variables all those national specific background variables that could not be cumulated for various reasons, or in case of the miscellaneous variables differ from the cumulation standard. However, the variables of this dataset can be matched easily to the cumulated file if necessary. A comprehensive overview on the contents, the structure and basic coding rules of both data files can be found in the following guide: Guide for the ISSP ´Social Inequality´ cumulation of the years 1987,1992, 1999 and 2009 Social Inequality I-IV - Add On: Substantial variables on ideas of real and appropriate earnings of different professions for all countries and modules if available. Country specific background variables on respondent’s education, respondent’s income, family income, party affiliation, party vote last general election, size of community, ethnic identity and on national occupations for respondent, spouse/ partner, father and mother if deviating from ILO ISCO-4 digits or cumulation standard. Miscellaneous variables including module specific background variables and country specific variables for cumulated substantial and background variables if deviating from cumulation standard: type of community: urban-rural area; industrial sector (1987, 1992); number of supervised people (1992, 1999); respondent´s working type: private versus public sector (1987); working type of spouse/partner: working for private or public sector or self-employed (2009); administrative mode of data-collection and case substitution flag (2009); self-placement of social class (Philippines 1992); industrial sector (1992, only in Austria, Germany, Russia and United States); current employment status of spouse/partner (Philippines 1992); household composition (children and adults) (United States 1992); type of living (Philippines 1992); party affiliation left-right (derived from party vote intention) (Hungary 1992); region (Slovak Republic 1999 and 2009, Czech Republic and Israel 2009). Das International Social Survey Programme (ISSP) ist ein länderübergreifendes, fortlaufendes Umfrageprogramm, das jährlich Erhebungen zu Themen durchführt, die für die Sozialwissenschaften wichtig sind. Das Programm begann 1984 mit vier Gründungsmitgliedern - Australien, Deutschland, Großbritannien und den Vereinigten Staaten - und ist inzwischen auf fast 50 Mitgliedsländer aus aller Welt angewachsen. Da die Umfragen auf Replikationen ausgelegt sind, können die Daten sowohl für länder- als auch für zeitübergreifende Vergleiche genutzt werden. Jedes ISSP-Modul konzentriert sich auf ein bestimmtes Thema, das in regelmäßigen Zeitabständen wiederholt wird. Details zur Durchführung der nationalen ISSP-Umfragen entnehmen Sie bitte der Dokumentation. Die vorliegende Studie konzentriert sich auf Fragen zu sozialer Ungleichheit. Das Release der kumulierten ISSP´Social Inequality´ Module für die Jahre 1987, 1992, 1999 und 2009 besteht aus zwei getrennten Datensätzen: ZA5890 und ZA5891. Diese Dokumentation befasst sich mit dem Add-On Datensatz ZA5891. Dieser Datensatz enthält all jene spezifischen nationalen Hintergrundvariablen, die zum Standard-ISSP gehören, aber aus verschiedenen Gründen nicht kumuliert werden konnten. Allerdings können die Variablen des Datensatzes einfach auf die kumulierte Datei angepasst werden, falls erforderlich. Einen umfassenden Überblick über die Inhalte, die Struktur und Grundcodierungsregeln beider Datendateien finden Sie in der folgenden Anleitung unter: Guide for the ISSP ´Social Inequality´ cumulation of the years 1987,1992, 1999 and 2009 Social Inequality I-IV - Add On: Wesentliche Variablen über Vorstellungen über reale und angemessene Einkommen verschiedener Berufe für alle Länder und Module, falls verfügbar. Länderspezifische Hintergrundvariablen hinsichtlich Bildung des Befragten, Einkommen des Befragten, Familieneinkommen, Parteizugehörigkeit, Abstimmungsverhalten bei der letzten Wahl, Gemeindegröße, ethnische Identität und hinsichtlich der nationalen Berufe für den Befragten, dessen Ehegatten bzw. Partner, Vater und Mutter, falls abweichend von ILO ISCO -4 Ziffern oder der Standardkumulierung. Sonstige Variablen einschließlich modulspezifischer Hintergrundvariablen und länderspezifischer Variablen für kumulierte wesentliche und Hintergrundvariablen, wenn abweichend von der Standardkumulierung: Gemeindetyp: Stadt-Land-Bereich; Industriesektor (1987, 1992); Anzahl der beaufsichtigten Personen (1992, 1999); Erwerbstyp des Befragten: privater oder öffentlicher Sektor (1987); Erwerbstyp des Ehegatten bzw. des Partners: Arbeit im privaten oder öffentlichen Sektor oder Selbstständige (2009); Verwaltungsmodus der Datenerhebung und case substitution flag (2009); Selbsteinstufung der Schichtzugehörigkeit (Philippinen 1992); Industriesektor (1992, nur in Österreich, Deutschland, Russland und den USA); aktueller Beschäftigungsstatus des Ehegatten bzw. des Partners (Philippinen 1992); Haushaltszusammensetzung (Kinder und Erwachsene) (USA 1992); Wohnart (Philippinen 1992); Parteizugehörigkeit links-rechts (abgeleitet von Parteiwahlabsicht) (Ungarn 1992); Region (Slowakei 1999 und 2009, Tschechische Republik und Israel 2009).

Philippines PH: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data was reported at 5.651 % in 2010. This records an increase from the previous number of 5.648 % for 2000. Philippines PH: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data is updated yearly, averaging 5.651 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 5.754 % in 1990 and a record low of 5.648 % in 2000. Philippines PH: Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Land Use, Protected Areas and National Wealth. Population below 5m is the percentage of the total population living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted average;

Philippines PH: Rural Population Growth data was reported at 1.147 % in 2017. This records a decrease from the previous number of 1.208 % for 2016. Philippines PH: Rural Population Growth data is updated yearly, averaging 2.116 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 2.966 % in 1961 and a record low of 0.774 % in 1989. Philippines PH: Rural Population Growth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Population and Urbanization Statistics. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Weighted average;