Philippines PH: Share of Youth Not in Education, Employment or Training: Total: % of Youth Population data was reported at 22.200 % in 2016. This records a decrease from the previous number of 22.740 % for 2015. Philippines PH: Share of Youth Not in Education, Employment or Training: Total: % of Youth Population data is updated yearly, averaging 24.680 % from Dec 2006 (Median) to 2016, with 11 observations. The data reached an all-time high of 25.320 % in 2010 and a record low of 22.200 % in 2016. Philippines PH: Share of Youth Not in Education, Employment or Training: Total: % of Youth 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: Employment and Unemployment. Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average;

In 2024, children below 15 years old accounted for 27.9 percent of the total population in the Philippines. The population share of children in the country has been declining over the past decade.

Philippines PH: Share of Youth Not in Education, Employment or Training: Male: % of Male Youth Population data was reported at 15.480 % in 2016. This records a decrease from the previous number of 17.060 % for 2015. Philippines PH: Share of Youth Not in Education, Employment or Training: Male: % of Male Youth Population data is updated yearly, averaging 17.770 % from Dec 2006 (Median) to 2016, with 11 observations. The data reached an all-time high of 18.850 % in 2010 and a record low of 15.480 % in 2016. Philippines PH: Share of Youth Not in Education, Employment or Training: Male: % of Male Youth 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: Employment and Unemployment. Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average;

In 2025, the average age in the Philippines is expected to reach 26.1 years, increasing to roughly 46.1 years of age by 2100. This is a significant rise, considering that until the year 2000, the country’s median age was under 20 years old. From 2011 to 2021, the share of very young people decreased, while the age brackets for people aged 15-64 and 65 or older grew. This shift in age structure implies a lower birth rate, as well as an aging population. Birth and family size As of 2020, the birth rate in the Philippines is just under 22 children born per thousand inhabitants each year, about 3 less than in the decade before. The fertility rate has likewise been decreasing since 2007, but is still higher than the Oceania region’s average as of 2020. Fewer newborns each year contributes to a lower median age. High mortality in the Philippines is preventable Life expectancy is also factor in a rising median age, although increasing only marginally in the Philippines compared with neighboring countries Cambodia, Myanmar, and Laos (but still higher than in these countries). The life expectancy in the Philippines was just under 72 years of age in 2017, and roughly three years shorter than in Thailand or Vietnam. One factor that lowers the life expectancy is the high mortality rate due to noncontagious diseases, such as cancer and heart and respiratory problems, accounting for more than a quarter of early deaths from ages 30 to 70 in the Philippines.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in the Philippines: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Share of youth not in education, employment or training, male (% of male youth population) in Philippines was reported at 10.2 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Philippines - Share of youth not in education, employment or training, male - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

Philippines PH: Share of Youth Not in Education, Employment or Training: Female: % of Female Youth Population data was reported at 29.240 % in 2016. This records an increase from the previous number of 28.630 % for 2015. Philippines PH: Share of Youth Not in Education, Employment or Training: Female: % of Female Youth Population data is updated yearly, averaging 31.550 % from Dec 2006 (Median) to 2016, with 11 observations. The data reached an all-time high of 32.150 % in 2012 and a record low of 28.630 % in 2015. Philippines PH: Share of Youth Not in Education, Employment or Training: Female: % of Female Youth 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: Employment and Unemployment. Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average;

Preliminary estimates indicated that the youth employment rate in the Philippines was at 89.3 percent, or equivalent to about 6,182 people in 2023. The employment rate of youths in the country plummeted in 2020 during the COVID-19 pandemic.

Philippines PH: Employment To Population Ratio: Modeled ILO Estimate: Aged 15-24: Female data was reported at 29.520 % in 2017. This records an increase from the previous number of 29.300 % for 2016. Philippines PH: Employment To Population Ratio: Modeled ILO Estimate: Aged 15-24: Female data is updated yearly, averaging 30.201 % from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 31.821 % in 1993 and a record low of 28.977 % in 2008. Philippines PH: Employment To Population Ratio: Modeled ILO Estimate: Aged 15-24: Female 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: Employment and Unemployment. Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

The youth unemployment rate in the Philippines increased by 0.1 percentage points (+1.48 percent) in 2023. In total, the youth unemployment rate amounted to 6.85 percent in 2023. The youth unemployment rate of a country or region refers to the share of the total workforce aged 15 to 24 that is currently without work, but actively searching for employment. It does not include economically inactive persons such as full-time students or the long-term unemployed.Find more key insights for the youth unemployment rate in countries like Cambodia and Timor-Leste.

Philippines PH: Employment To Population Ratio: National Estimate: Aged 15-24: Male data was reported at 45.850 % in 2016. This records an increase from the previous number of 45.660 % for 2015. Philippines PH: Employment To Population Ratio: National Estimate: Aged 15-24: Male data is updated yearly, averaging 47.570 % from Dec 2001 (Median) to 2016, with 15 observations. The data reached an all-time high of 49.910 % in 2004 and a record low of 45.660 % in 2015. Philippines PH: Employment To Population Ratio: National Estimate: Aged 15-24: Male 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: Employment and Unemployment. Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average; The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Philippines PH: Employment To Population Ratio: National Estimate: Aged 15-24: Female data was reported at 27.450 % in 2016. This records a decrease from the previous number of 28.350 % for 2015. Philippines PH: Employment To Population Ratio: National Estimate: Aged 15-24: Female data is updated yearly, averaging 28.970 % from Dec 2001 (Median) to 2016, with 15 observations. The data reached an all-time high of 30.100 % in 2001 and a record low of 27.450 % in 2016. Philippines PH: Employment To Population Ratio: National Estimate: Aged 15-24: Female 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: Employment and Unemployment. Employment to population ratio is the proportion of a country's population that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average; The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

PH：未受教育、无业或未受培训青少年比例：女性：占女性年轻人口百分比在12-01-2016达29.240%，相较于12-01-2015的28.630%有所增长。PH：未受教育、无业或未受培训青少年比例：女性：占女性年轻人口百分比数据按年更新，12-01-2006至12-01-2016期间平均值为31.550%，共11份观测结果。该数据的历史最高值出现于12-01-2012，达32.150%，而历史最低值则出现于12-01-2015，为28.630%。CEIC提供的PH：未受教育、无业或未受培训青少年比例：女性：占女性年轻人口百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的菲律宾 – 表 PH.世界银行：就业和失业。

1. Important variation in the shape and strength of density-dependent growth and mortality is observed across animal populations. Understanding this population variation is critical for predicting density-dependent relationships in natural populations, but comparisons among studies are challenging as studies differ in methodologies and in local environmental conditions.
2. Consequently, it is unclear whether: (1) the shape and strength of density-dependent growth and mortality are population-specific; (2) the potential trade-off between density-dependent growth and mortality differs among populations; and (3) environmental characteristics can be related to population differences in density-dependent relationships.
3. To elucidate these uncertainties, we manipulated the density (0.3-7 fish/m²) of young-of-the-year brook trout (Salvelinus fontinalis) simultaneously in three neighboring populations in a field experiment in Newfoundland, Canada. Within each population, our experiment included both spatial (three sites per stream) and temporal (three consecutive summers) replication.
4. We detected temporally consistent population variation in the shape of density-dependent growth (negative linear and negative logarithmic), but not for mortality (positive logarithmic). The strength of density-dependent growth across populations was reduced in sections with a high percentage of boulder substrate, whereas density-dependent mortality increased with increasing flow, water temperature, and more acidic pH. Neighbouring populations exhibited different mortality-growth trade-offs: the ratio of mortality-to-growth increased linearly with increasing density at different rates across populations (up to 4-fold differences), but also increased with increasing temperature.
5. Our results are some of the first to demonstrate temporally consistent, population-specific density-dependent relationships and trade-offs at small spatial scales that match the magnitude of interspecific variation observed across the globe. Furthermore, key environmental characteristics explain some of these differences in predictable ways. Such population differences merit further attention in models of density-dependence and in science-based management of animal populations.

This study aimed to investigate adolescent's cognitive processes and their thoughts and feelings when answering the International Society for the Prevention of Child Abuse and Neglect Child Abuse Screening Tool - ICAST-C.

This study used face-to-face semi-structured cognitive interviews, employing a combination of think aloud, structured and spontaneous verbal probing, and observations.

The sample in this study consisted of 53 adolescents aged 10-17 years across three contexts. Interviews were conducted with 17 participants in Romania, 20 participants in South Africa, and 16 participants in the Philippines. This study adopted a purposive sampling strategy. In addition to purposive sampling, this study employed maximum variation sampling. Maximum variation sampling is an appropriate strategy when the study aims to understand the variability of views existing in a particular group. Geographical and cultural variation, as well as variation in age, gender, and previous research exposure, were considerations in implementing this strategy.

Both research-exposed (those who had answered a self-report violence measure) and research non-exposed (those who had not answered a self-report violence measure) participants were recruited. Apart from these considerations, participants were recruited on the basis of age (those aged between 10-17 years) and gender (male, female, and other gender identities).

Globally, 95 million children become victims of physical, emotional and sexual child abuse every year. Child abuse has lifetime impacts including medical trauma, mental health distress, illness, school drop-out and unemployment. We know there is also a cycle of violence across generations. In other words, victims of child abuse are more likely to commit violent crime and to abuse their own children. They are also more likely to become a victim of violence again, both in childhood and in their adult relationships. Child abuse also has a hidden but massive impact on society because of illness and disability, costing an estimated 124 billion USD a year in the United States. But why do child abuse rates remain so inexplicably high? Child abuse is a complex problem that reaches across the home and community. In order to combat child abuse, we need to understand how many children are affected, where they are and who is most at risk. Then we need effective interventions to prevent and reduce child abuse. However, we know very little about either. A small number of high-income countries have social services data but these only identify the tip of the iceberg; most child abuse is never reported to services. To detect abuse within the whole population, we need to conduct surveys. That being said, the only child abuse measures available are lengthy and detailed, and they are therefore costly to carry out nationally. If a short child abuse measure existed, it could be included in larger, regularly conducted surveys (e.g. Demographic and Health Surveys or census). Interventions aim to prevent and reduce abuse, but there is currently no child abuse measure that can test whether such interventions have worked. A measure needs to be designed to detect changes in how severe and how often abusive behaviours occur. At the moment, researchers often use proxy measures for abuse, such as parenting stress. This study has two aims: (1) to develop a brief child abuse measure for the inclusion in large surveys, and (2) to test and validate a sensitive child abuse measure for use in intervention evaluation research. These will then be made available, together with a user manual, at no cost. To combat child abuse, we need strong collaborations between research and policy. I have already established strong partnerships with a number of academic institutions and international organisations in child protection. I have developed a prototype of the measure for intervention testing, and this is being used in six studies with 3800 participants in South Africa, Tanzania, the Democratic Republic of Congo and the Philippines. My collaborators will share the data, allowing me to conduct statistical analysis on how and whether the measure works. I will also conduct analyses testing whether the tool measures the same concepts across cultures. Finally, I will carry out qualitative research with key stakeholders in child protection to find the best questions for the short child abuse measure. To complement this, I will use statistical techniques on the pooled dataset to identify questions that can be used in surveys. This project can have a large impact on global child abuse prevention efforts. It will help researchers and policy-makers to measure accurately the number of children affected and determine whether interventions really work. It is an essential step in creating high quality evidence for protecting the world's children.

As of October 2023, the share of working children aged five to 17 years old in the Philippines who were working in the agriculture industry was estimated at 43.7 percent, indicating a slight increase from the previous year. The share of working children in the country fluctuated since 2021.

As of October 2023, it was estimated that 678,000 working children aged five to 17 years in the Philippines were engaged in child labor. This indicates a decrease from the previous year's total. The majority of working children engaged in child labor were males.

56.8% of the world’s total population is active on social media.

Philippines PH: Probability of Dying at Age 15-19 Years: per 1000 data was reported at 3.400 Ratio in 2019. This records a decrease from the previous number of 3.500 Ratio for 2018. Philippines PH: Probability of Dying at Age 15-19 Years: per 1000 data is updated yearly, averaging 4.050 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 5.700 Ratio in 1991 and a record low of 3.400 Ratio in 2019. Philippines PH: Probability of Dying at Age 15-19 Years: per 1000 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: Health Statistics. Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

90% of people aged 18-29 use social media in some form. 15% of people aged 23-38 admit that they are addicted to social media.