About this dataset

Context

This list ranks the 50 states in the United States by Filipino population, as estimated by the United States Census Bureau. It also highlights population changes in each state over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Filipino Population: This column displays the rank of state in the United States by their Filipino population, using the most recent ACS data available.
• State: The State for which the rank is shown in the previous column.
• Filipino Population: The Filipino population of the state is shown in this column.
• % of Total State Population: This shows what percentage of the total state population identifies as Filipino. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total United States Filipino Population: This tells us how much of the entire United States Filipino population lives in that state. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

This list ranks the 86 cities in the Los Angeles County, CA by Filipino population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Filipino Population: This column displays the rank of city in the Los Angeles County, CA by their Filipino population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Filipino Population: The Filipino population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Filipino. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Los Angeles County Filipino Population: This tells us how much of the entire Los Angeles County, CA Filipino population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

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.

About this dataset

Context

This list ranks the 4 cities in the East Baton Rouge Parish, LA by Filipino population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Filipino Population: This column displays the rank of city in the East Baton Rouge Parish, LA by their Filipino population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Filipino Population: The Filipino population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Filipino. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total East Baton Rouge Parish Filipino Population: This tells us how much of the entire East Baton Rouge Parish, LA Filipino population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

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.

Introduction

Welcome to the Philippines English Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Participant Diversity:

- Speakers: 50+ native English speakers from the FutureBeeAI Community.

- Regions: Ensures a balanced representation of Philippines1 accents, dialects, and demographics.

- Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.

Recording Nature: Scripted wake word and command type of audio recordings.

- Duration: Average duration of 5 to 20 seconds per audio recording.

- Formats: WAV format with mono channels, a bit depth of 16 bits. The dataset contains different data at 16kHz and 48kHz.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Different Automobile Related Wake Words: Hey Mercedes, Hey BMW, Hey Porsche, Hey Volvo, Hey Audi, Hi Genesis, Hey Mini, Hey Toyota, Ok Ford, Hey Hyundai, Ok Honda, Hello Kia, Hey Dodge.

Different Cars: Data collection was carried out in different types and models of cars.

Different Types of Voice Commands:

- Navigational Voice Commands

- Mobile Control Voice Commands

- Car Control Voice Commands

- Multimedia & Entertainment Commands

- General, Question Answer, Search Commands

Recording Time: Participants recorded the given prompts at various times to make the dataset more diverse.

- Morning

- Afternoon

- Evening

Recording Environment: Various recording environments were captured to acquire more realistic data and to make the dataset inclusive of various types of noises. Some of the environment variables are as follows:

- Noise Level: Silent, Low Noise, Moderate Noise, High Noise

- Parking Location: Indoor, Outdoor

- Car Windows: Open, Closed

- Car AC: On, Off

- Car Engine: On, Off

- Car Movement: Stationary, Moving

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

Participant Metadata: Unique identifier, age, gender, country, state, district, accent, and dialect.

Other Metadata: Recording transcript, recording environment, device details, sample rate, bit depth, file format, recording time.

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of English voice assistant speech recognition models.

License

This Philippines English In-car audio dataset is created by FutureBeeAI and is available for commercial use.

Philippines PH: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at 1.380 % in 2015. Philippines PH: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging 1.380 % from Dec 2015 (Median) to 2015, with 1 observations. Philippines PH: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate 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: Poverty. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Philippines PH: Urban Land Area Where Elevation is Below 5 Meters data was reported at 746.357 sq km in 2010. This stayed constant from the previous number of 746.357 sq km for 2000. Philippines PH: Urban Land Area Where Elevation is Below 5 Meters data is updated yearly, averaging 746.357 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 746.357 sq km in 2010 and a record low of 746.357 sq km in 2010. Philippines PH: Urban Land Area Where Elevation is Below 5 Meters 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 land area below 5m is the total urban land area in square kilometers 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.; Sum;

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.

The Philippines reported about ****** HIV cases, an increase from the previous year. The number of reported HIV cases has gradually increased since 2012, aside from a significant dip in 2020. The state of HIV As the monthly average number of people newly diagnosed with HIV increases, the risk it poses threatens the lives of Filipinos. HIV is a sexually transmitted infection that attacks the body’s immune system, with more males being diagnosed than females. In 2022, the majority of people newly diagnosed with HIV were those between the age of 25 and 34 years, followed by those aged 15 and 24. There is still no cure for HIV and without treatment, it could lead to other severe illnesses such as tuberculosis and cancers such as lymphoma and Kaposi’s sarcoma. However, HIV is now a manageable chronic illness that can be treated with proper medication. What are the leading causes of death in the Philippines? Between January and September 2024, preliminary figures have shown that ischaemic heart disease was the leading cause of death in the Philippines. The prevalence of heart diseases in the nation has been closely attributed to the Filipino diet, which was described as having a high fat, high cholesterol, and high sodium content. In addition, acute respiratory infections and hypertension also registered the highest morbidity rate among leading diseases in the country in 2021.

Philippines is the 114 least corrupt nation out of 180 countries, according to the 2024 Corruption Perceptions Index reported by Transparency International. This dataset provides the latest reported value for - Philippines Corruption Rank - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Employment Rate in Philippines increased to 96.20 percent in September from 96.10 percent in August of 2025. This dataset provides - Philippines Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

This dataset provides values for UNEMPLOYMENT RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

About this dataset

Context

This list ranks the 1 cities in the Lake of the Woods County, MN by Filipino population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Filipino Population: This column displays the rank of city in the Lake of the Woods County, MN by their Filipino population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Filipino Population: The Filipino population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Filipino. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Lake of the Woods County Filipino Population: This tells us how much of the entire Lake of the Woods County, MN Filipino population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

This list ranks the 7 cities in the San Joaquin County, CA by Filipino population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Filipino Population: This column displays the rank of city in the San Joaquin County, CA by their Filipino population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Filipino Population: The Filipino population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Filipino. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total San Joaquin County Filipino Population: This tells us how much of the entire San Joaquin County, CA Filipino population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

This list ranks the 26 cities in the Prince Georges County, MD by Filipino population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Filipino Population: This column displays the rank of city in the Prince Georges County, MD by their Filipino population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Filipino Population: The Filipino population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Filipino. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Prince Georges County Filipino Population: This tells us how much of the entire Prince Georges County, MD Filipino population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

This list ranks the 1 cities in the Val Verde County, TX by Filipino population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Filipino Population: This column displays the rank of city in the Val Verde County, TX by their Filipino population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Filipino Population: The Filipino population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Filipino. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Val Verde County Filipino Population: This tells us how much of the entire Val Verde County, TX Filipino population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.