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

UD_Tagalog-NewsCrawl

Paper: https://arxiv.org/abs/2505.20428 The Tagalog Universal Dependencies NewsCrawl dataset consists of annotated text extracted from the Leipzig Tagalog Corpus. Data included in the Leipzig Tagalog Corpus were crawled from Tagalog-language online news sites by the Leipzig University Institute for Computer Science. The text data was automatically parsed and annotated by Angelina Aquino (University of the Philippines), and then manually corrected according the… See the full description on the dataset page: https://huggingface.co/datasets/UD-Filipino/UD_Tagalog-NewsCrawl.

The dataset used in this study was a subset of the corpus 1,696,613 tweets crawled by Andrade et al. and posted from November 2015 to May 2016 during the campaign period for the Philippine presidential election. They were culled based on the presence of candidate names (e.g., Binay, Duterte, Poe, Roxas, and Santiago) and election-related hashtags (e.g., #Halalan2016, #Eleksyon2016, and #PiliPinas2016). Data preprocessing was performed to prepare the tweets for feature extraction and classification. It consisted of the following steps: data de-identification, uniform resource locator (URL) removal, special character processing, normalization, hashtag processing, and tokenization.

Abstract

The Survey on Overseas Filipinos (SOF) was conducted as a rider to the October 2008 Labor Force Survey (LFS).

The survey was designed to gather national estimates on the number of overseas workers, their socio economic characteristics and other information pertaining to the overseas workers who worked or have worked abroad from April to September 2008. The remittances of the Overseas Filipino Workers (OFWs) in cash or in kind were also accounted for the specified reference period. The SOF data are useful inputs to government planners, migrant advocates, researchers, academes, concerned citizens, and other data users to the formulation of policies and programs for the welfare of the overseas Filipino.

Geographic coverage

The geographic coverage consists of the country's 17 administrative regions defined in Executive Order (EO) 36 and 131. The 17 regions are:

National Capital Region (NCR) Cordillera Administrative Region (CAR) Region I - Ilocos Region Region II - Cagayan Valley Region III - Central Luzon Region IV-A - CALABARZON Region IV-B - MIMAROPA Region V - Bicol Region Region VI - Western Visayas Region VII - Central Visayas Region VIII - Eastern Visayas Region IX - Zamboanga Peninsula Region X - Northern Mindanao Region XI - Davao Region Region XII - SOCCSKSARGEN Caraga Autonomous Region in Muslim Mindanao (ARMM)

Analysis unit

Individuals

Universe

Overseas Filipinos whose departure occured within the last five years and who are working or had worked abroad during the past six months (April to September) of the survey period.

For purposes of this survey, overseas workers are the following:

Filipino overseas contract workers (OCW) who are presently and temporarily out of the country to fulfill an overseas work contract for a specific length of time or who are presently at home on vacation but still has an existing contract to work abroad. They may be landbased or seabased.

Landbased workers ? these are overseas contract workers who are hired either by direct hiring of an employer abroad; or through the assistance of Philippine Overseas Employment Administration (POEA); or through a private and licensed recruitment agency. They may have returned to the Philippines for a vacation (annual or emergency leave), or have transferred to other employers, or were rehired by their former employer.

Seabased workers ? these are overseas contract workers who worked or are working in any kind of international fishing/passenger/cargo vessels. Included also are OCWs who worked or are working for a shipping company abroad.

Other Filipino workers abroad with a valid working visa or work permits. Included also are crew members of airplanes such as pilots, stewards, stewardesses, etc. example: Filipinos working in countries such as U.S., Taiwan, Saipan, etc. with a working visa.

Filipinos abroad who are holders of other types of non-immigrant visa such as tourist/visitor, student, medical and others but are presently employed and working full time.

Persons not considered as overseas workers are:

Filipinos whose place of employment is outside the Philippines but whose employer is the Philippine government. Examples are Filipinos who worked or are working in Philippine embassies, missions and consulates abroad.

Filipinos who are sent abroad by the Philippine government or by private institutes for training, scholarship or any other similar purpose, even if they are known to be working abroad. Note that students who are sent abroad by private individual who are working or had worked there are excluded in this category.

Filipinos working in other countries who are hired as consultants/advisers of International organization such as the United Nations International Monetary Fund, etc.

Immigrants to other countries even though they are working abroad.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Survey on Overseas Filipinos, as a rider to the Labor Force Survey (LFS), used the sampling design of the 2003 Master Sample (MS) for Household Surveys starting July 2003. The design of the Master Sample is described below:

1. Domain The 2003 MS considers the country's 17 administrative regions as its sampling domain. A domain is referred to as a subdivision of the country in which estimates with adequate level of precision is generated. It must be noted that while there is demand for data at the provincial level (and to some extent municipal and barangay levels), these were not treated as domain because of its large number (more than 80) and the large resource requirement it would entail.

2. Sampling Frame As in most household surveys, the 2003 MS made use of an area sample design. For this purpose, the Enumeration Area Reference File (EARF) of the 2000 Census of Population and Housing (CPH) was utilized as sampling frame. The EARF contains the number of households by enumeration area (EA) in each barangay.

This frame was used to form the primary sampling units (PSUs). With consideration of the period for which the 2003 MS will be in use, the PSUs were formed/defined as a barangay or a combination of barangays with at least 500 households.

1. Sample Size The 2003 MS consists of a sample of 2,835 PSUs of which 330 were certainty PSUs and 2,505 were non-certainty PSUs. The entire MS was divided into four sub-samples or independent replicates, such as a quarter sample contains one fourth of the PSUs found in one replicate; a half sample contains one-half of the PSUs in two replicates. The SOF as a rider to the LFS utilizes the full sample.

2. Stratification The 2003 MS considers the 17 regions of the country as the primary strata. Within each region, further stratification was performed using geographic groupings such as provinces, highly urbanized cities (HUCs), and independent component cities (ICCs). Within each of these substrata formed within regions, the PSUs were further stratified, to the extent possible, using the proportion of strong houses (PSTRONG), indicator of engagement in agriculture of the area (AGRI), and a measure of per capita income as stratification factors (PERCAPITA).

PSTRONG is defined to be the percentage of occupied housing units that are classified as made of strong materials in terms of both the roof and outer walls, based on the data from the 2000 CPH. A roof is considered made of strong material if it is made of either galvanized iron, aluminum, concrete/clay tile, half galvanized-half concrete, or asbestos. The outer wall is considered made of strong material if it is made of concrete, brick, stone, wood, half concrete-half wood, galvanized iron, asbestos or glass.

AGRI was determined in the following way: initially, an indicator variable was computed at the barangay level. That variable has the value 1 if more than 50 percent of the households in the barangay were engaged in agriculture or fisheries and 0 otherwise, based on the 2000 CPH Barangay Schedule. To obtain a measure at the PSU level, a weighted average of the barangay indicator variable was computed for all the barangays within the PSU, weighted by the total number of households in the barangay. Thus, the value of AGRI at the PSU level lies between 0 and 1.

PERCAPITA is defined as the total income of the municipality divided by the total population in that municipality. Note that the PERCAPITA value of the PSUs is the same if the PSUs are in the same municipality. The data on municipal income refer to year 2000 and were taken from the Department of Finance. However, if the 2000 municipal income was not reported to the Bureau of Local Government Finance (BLGF), 2001 income was used. If no 2000 or 2001 municipal income was reported, the income classification from the BLGF for this municipality was obtained. Using the data on income, which are presented in income intervals, the average of the lower and the upper values of the income interval for the municipal class to which this municipality belongs were determined.

1. Sample Selection

The 2003 MS consists of a sample of 2,835 PSUs. The entire MS was divided into four sub-samples or independent replicates, such as a quarter sample contains one fourth of the total PSUs; a half sample contains one-half of the four subsamples or equivalent to all PSUs in two replicates.

The final number of sample PSUs for each domain was determined by first classifying PSUs as either self-representing (SR) or non-self-representing (NSR). In addition, to facilitate the selection of subsamples, the total number of NSR PSUs in each region was adjusted to make it a multiple of 4.

SR PSUs refers to a very large PSU in the region/domain with a selection probability of approximately 1 or higher and is outright included in the MS; it is properly treated as a stratum; also known as certainty PSU. NSR PSUs refers to a regular too small sized PSU in a region/domain; also known as non certainty PSU. The 2003 MS consists of 330 certainty PSUs and 2,505 non-certainty PSUs.

To have some control over the sub-sample size, the PSUs were selected with probability proportional to some estimated measure of size. The size measure refers to the total number of households from the 2000 CPH. Because of the wide variation in PSU sizes, PSUs with selection probabilities greater than 1 were identified and were included in the sample as certainty selections.

At the second stage, enumeration areas (EAs) were selected within sampled PSUs, and at the third stage, housing units were selected within sampled EAs. Generally, all households in sampled housing units were enumerated, except for few cases when the number of households in a housing unit exceeds three. In which case, a sample of three households in a sampled housing unit was selected at random with equal

This dataset contains 103+ hours of spontaneous English conversations spoken in a Filipino accent, recorded in a studio environment to ensure crystal-clear audio quality. The conversations are designed as role-play scenarios between agents and customers across a variety of call center domains. 🗣️ Speech Style: Natural, unscripted role-playing between native Filipino-accented English speakers, simulating real-world customer interactions. 🎧 Audio Format: High-quality stereo WAV files, recorded… See the full description on the dataset page: https://huggingface.co/datasets/AIxBlock/Eng-Filipino-Accented-audio-with-human-transcription-call-center-topic.

Abstract (en): IIMMLA was supported by the Russell Sage Foundation. Since 1991, the Russell Sage Foundation has funded a program of research aimed at assessing how well the young adult offspring of recent immigrants are faring as they move through American schools and into the labor market. Two previous major studies have begun to tell us about the paths to incorporation of the children of contemporary immigrants: The Children of Immigrants Longitudinal Study (CILS), and the Immigrant Second Generation in New York study. The Immigration and Intergenerational Mobility in Metropolitan Los Angeles study is the third major initiative analyzing the progress of the new second generation in the United States. The Immigration and Intergenerational Mobility in Metropolitan Los Angeles (IIMMLA) study focused on young adult children of immigrants (1.5- and second-generation) in greater Los Angeles. IIMMLA investigated mobility among young adult (ages 20-39) children of immigrants in metropolitan Los Angeles and, in the case of the Mexican-origin population there, among young adult members of the third- or later generations. The five-county Los Angeles metropolitan area (Los Angeles, Orange, Ventura, Riverside and San Bernardino counties) contains the largest concentrations of Mexicans, Salvadorans, Guatemalans, Filipinos, Chinese, Vietnamese, Koreans, and other nationalities in the United States. The diverse migration histories and modes of incorporation of these groups made the Los Angeles metropolitan area a strategic choice for a comparison study of the pathways of immigrant incorporation and mobility from one generation to the next. The IIMMLA study compared six foreign-born (1.5-generation) and foreign-parentage (second-generation) groups (Mexicans, Vietnamese, Filipinos, Koreans, Chinese, and Central Americans from Guatemala and El Salvador) with three native-born and native-parentage comparison groups (third- or later-generation Mexican Americans, and non-Hispanic Whites and Blacks). The targeted groups represent both the diversity of modes of incorporation in the United States and the range of occupational backgrounds and immigration status among contemporary immigrants (from professionals and entrepreneurs to laborers, refugees, and unauthorized migrants). The surveys provide basic demographic information as well as extensive data about socio-cultural orientation and mobility (e.g., language use, ethnic identity, religion, remittances, intermarriage, experiences of discrimination), economic mobility (e.g., parents' background, respondents' education, first and current job, wealth and income, encounters with the law), geographic mobility (childhood and present neighborhood of residence), and civic engagement and politics (political attitudes, voting behavior, as well as naturalization and transnational ties). ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Young adults aged 20-39 from six foreign-born and foreign-parentage groups: Mexican, Vietnamese, Filipino, Korean, Chinese, and Central American (Guatemalan and Salvadoran), as well as native-born and native-parentage Mexican-Americans, and non-Hispanic Whites and Blacks, in the Los Angeles metropolitan area. Multistage random sampling. 2008-07-01 Edits were made to the metadata record. Funding insitution(s): Russell Sage Foundation. telephone interview Data collection for IIMMLA was subcontracted to and carried out by the Field Research Corporation, San Francisco, CA.

Note: For information on data collection, confidentiality protection, nonsampling error, and definitions, see the 2020 Island Areas Censuses Technical Documentation..Due to operational changes for military installation enumeration, the 2020 Census of Guam data tables reporting housing, social, and economic characteristics do not include housing units or populations living on Guam's U.S. military installations in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about operational changes and the impacts on Guam's data products, see the 2020 Island Areas Censuses Technical Documentation..Due to COVID-19 restrictions impacting data collection for the 2020 Census of Guam, data users should consider the following when using Guam's data products: 1) Data tables reporting social and economic characteristics do not include the group quarters population in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about data collection limitations and the impacts on Guam's data products, see the 2020 Island Areas Censuses Technical Documentation. 2) Cells in data tables will display the letter "N" when those data are not statistically reliable. A list of the geographic areas and data tables that will not have data displayed due to data quality concerns can be found in the 2020 Island Areas Censuses Technical Documentation. 3) The Census Bureau advises that data users consider high allocation rates while using the 2020 Census of Guam's available characteristics data. Allocation rates -- a measure of item nonresponse -- are higher than past censuses. Final counts can be adversely impacted when an item's allocation rate is high, and bias can be introduced if the characteristics of the nonrespondents differ from those reported by respondents. Allocation rates for Guam's key population and housing characteristics can be found in the 2020 Island Areas Censuses Technical Documentation. .[1] People who reported multiple responses may be counted in more than one of the race alone or in combination categories. For example, a respondent reporting Chamorro and Filipino is counted in the "Native Hawaiian and Other Pacific Islander alone or in combination" category, the "Chamorro alone or in any combination" category, the "Asian alone or in combination" category, and the "Filipino alone or in any combination" category. These categories may add to more than the total population..[2] "Native Hawaiian and Other Pacific Islander alone or in combination" includes respondents who reported a Native Hawaiian and Other Pacific Islander group alone (e.g., Chamorro), multiple Native Hawaiian and Other Pacific Islander groups (e.g., Chamorro and Chuukese), as well as respondents who reported one Native Hawaiian and Other Pacific Islander group and one or more other groups classified as another race (e.g., Chamorro and White)..[3] "Asian alone or in combination" includes respondents who reported an Asian group alone (e.g., Filipino), multiple Asian groups (e.g., Filipino and Korean), as well as respondents who reported an Asian group and one or more other groups classified as another race (e.g., Filipino and White)..[4] "Other races alone or in combination" includes respondents who reported one race group or multiple race groups that were not classified as Native Hawaiian and Other Pacific Islander or Asian (e.g., White and a Black or African American group such as Jamaican), as well as respondents who reported one group that was not classified as Native Hawaiian and Other Pacific Islander or Asian and another that was classified as Native Hawaiian and Other Pacific Islander or Asian (e.g., Jamaican and Chamorro)..[5] The most common reported Hispanic origin group in the 2010 Census of Guam..[6] This category includes people who reported Cuban, Spaniard, and other detailed Hispanic responses. It also includes people who reported "Hispanic" or "Latino" and other general terms..[7] "Spouse" represents spouse of the householder. It does not reflect all spouses in a household..[8] "Family households" consist of a householder and one or more other people related to the householder by birth, marriage, or adoption..Explanation of Symbols: 1.An "-" means the statistic could not be computed because there were an insufficient number of observations. 2. An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.3. An "+" following a median estimate means the median falls in the upper interval of an open-ended di...

UK residents by broad country of birth and citizenship groups, broken down by UK country, local authority, unitary authority, metropolitan and London boroughs, and counties. Estimates from the Annual Population Survey.

The USD/PHP exchange rate fell to 57.0160 on July 18, 2025, down 0.37% from the previous session. Over the past month, the Philippine Peso has strengthened 0.55%, and is up by 2.37% over the last 12 months. Philippine Peso - values, historical data, forecasts and news - updated on July of 2025.

Number of people belonging to a visible minority group as defined by the Employment Equity Act and, if so, the visible minority group to which the person belongs. The Employment Equity Act defines visible minorities as 'persons, other than Aboriginal peoples, who are non-Caucasian in race or non-white in colour.' The visible minority population consists mainly of the following groups: South Asian, Chinese, Black, Filipino, Latin American, Arab, Southeast Asian, West Asian, Korean and Japanese.

Dataset Card for Filipino-English Reviews with Code-Switching (FiReCS)

  Dataset Summary

We introduce FiReCS, the first sentiment-annotated corpus of product and service reviews involving Filipino-English code-switching. The data set is composed of 10,487 reviews with a fairly balanced number per sentiment class. Inter-annotator agreement is high with a Kripendorffs’s α for ordinal metric of 0.83. Three human annotators were tasked to manually label reviews according to… See the full description on the dataset page: https://huggingface.co/datasets/ccosme/FiReCS.