About this dataset

Context

The dataset tabulates the Non-Hispanic population of Forbes by race. It includes the distribution of the Non-Hispanic population of Forbes across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Forbes across relevant racial categories.

Key observations

With a zero Hispanic population, Forbes is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is White alone with a population of 23 (100% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Forbes
• Population: The population of the racial category (for Non-Hispanic) in the Forbes is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Forbes total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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/.

Recommended for further research

This dataset is a part of the main dataset for Forbes Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Forbes population by age. The dataset can be utilized to understand the age distribution and demographics of Forbes.

Content

The dataset constitues the following three datasets

• Forbes, ND Age Group Population Dataset: A complete breakdown of Forbes age demographics from 0 to 85 years, distributed across 18 age groups
• Forbes, ND Age Cohorts Dataset: Children, Working Adults, and Seniors in Forbes - Population and Percentage Analysis
• Forbes, ND Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

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

The dataset tabulates the population of Forbes by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Forbes across both sexes and to determine which sex constitutes the majority.

Key observations

There is a considerable majority of male population, with 65.22% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Forbes is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Forbes total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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/.

Recommended for further research

This dataset is a part of the main dataset for Forbes Population by Race & Ethnicity. You can refer the same here

In 2022, Giovanni Ferrero, Executive Chairman of the Italian confectionary company Ferrero S.p.A., ranked first in the yearly ranking of Italian billionaires, published by the American business magazine Forbes. With a total net worth estimated at 34.1 billion U.S. dollars, Giovanni Ferrero ranked ahead another Italian entrepreneur - Giorgio Armani, the founder of luxury company, Armani, whose fortune amounted to 6.6 billion U.S. dollars.

A giant in the food industry

The name Ferrero is known worldwide for an enormous amount of branded chocolate and confectionery products. The company was founded in 1946 by Pietro Ferrero, father of Giovanni Ferrero. Ferrero S.p.A. has its headquarters in Piedmont, in the North of Italy, but their products have a strong presence all over the world. The estimated global market share of Ferrero is around 9.5 percent, fourth only to Mars, Mondalez International, and Nestlé.

Italian entrepreneur among the richest persons in the world

When compared to the global ranking of billionaires, the Italian businessman ranked 39th. In fact, Italy ranks among the countries with the largest number of billionaires in the world. In 2021, the title of the richest man on the planet went to Jeff Bezos, the founder and CEO of Amazon. The net worth of this Albuquerque-born entrepreneur was 189 billion U.S. dollars in 2021. The second place was achieved by one of the most visionary businessmen, Elon Musk, whose fortune was estimated at 182 billion U.S. dollars in 2021.

About this dataset

Context

The dataset tabulates the population of Forbes by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Forbes. The dataset can be utilized to understand the population distribution of Forbes by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Forbes. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Forbes.

Key observations

Largest age group (population): Male # 30-34 years (5) | Female # 50-54 years (3). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Forbes population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Forbes is shown in the following column.
• Population (Female): The female population in the Forbes is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Forbes for each age group.

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/.

Recommended for further research

This dataset is a part of the main dataset for Forbes Population by Gender. You can refer the same here

Data set for SNP-based genotyping survey of leopard population in western Mozambique - see Mamugy FPS, Bertola LD, Mertens De Vry A, Dussex N, Shiffthaler B, Paijmans J, Hofreiter M, Forbes RE, Kerley GIH, Everatt KT, et al. 2024. SNP panel for non-invasive genotyping of leopard (Panthera pardus). bioRxiv 2024/621452 for detailed MethodsSee FORBES, R.E., KERLEY, G.I.H., EVERATT, K.T., MAMUGY, F.P.S. & SPONG, G. 2025. Estimating leopard population sizes in western Mozambique using SNP-based capture-mark-recapture models. Journal of Mammalogy 00:00-00. for details of analysis of this data set.

Dataset

This dataset was created by Sudhanshu Aware

Released under CC0: Public Domain

Contents

According to 2021 Forbes data, the richest man in India is business magnate Mukesh Ambani with a net worth of about 84.5 billion U.S. dollars.

Wealth distribution in India

India’s wealth is very unevenly distributed, with the wealthiest one percent of inhabitants owning more than half of the wealth. Currently, the majority of Indians own less than 10,000 U.S. dollars in wealth and assets and over 80 percent of Indian households have an average monthly income of 20,000 Indian rupees (about 286 U.S. dollars) or less – and even less in rural areas. Poverty is among the most common worries of Indian people and a prevalent problem in the country, despite a growing economy.

India’s growing economy benefits many, but not all

Most Indians live in rural areas, where agriculture is still the main provider. In fact, agriculture was an important economic driver for a long time, until services gained traction (and now generates almost half of India’s GDP). Mukesh Ambani, India’s richest entrepreneur, is one of the beneficiaries of this development, since his company, Reliance Industries, owns businesses mostly in the services sector.

As of June 2024, Elon Musk was estimated as the wealthiest person in the United States with a net worth of around 195 billion dollars. Richest people in the United States - additional information Every year since 1982, the American business magazine Forbes has been compiling lists of the 400 richest people in the United States, known as the “Forbes 400.” In addition to that, since 1987, the publication has also been compiling a ranking of the 500 richest people in the world (excluding royalty and dictators), as well as more specialized tops, such as “World's Most Powerful Women,” “America's Richest Families,” “Most Valuable Brands” or “30 Under 30,” which focuses on young entrepreneurs from various fields which have gained millions in the past year by the use of social media, technical innovations and generally new and fresh approaches to business.

1. An explicit spatial understanding of population dynamics is often critical for effective management of wild populations. Sophisticated approaches are available to simulate these dynamics, but are largely either spatially homogeneous or agent-based, and thus best suited to small spatial or temporal scales. These approaches also often ignore financial decisions crucial to choosing management approaches on the basis of cost-effectiveness. 2. We created a user-friendly and flexible modelling framework for simulating these population issues at large spatial scales – the Spatial Population Abundance Dynamics Engine (SPADE). SPADE is based on the STAR model (McMahon et al. 2010) and uses a reaction-diffusion approach to model population trajectories and a cost-benefit analysis technique to calculate optimal management strategies over long periods and across broad spatial scales. It expands on STAR by incorporating species interactions and multiple concurrent management strategies, and by allowing full user control of functional forms and parameters. 3. We used SPADE to simulate the eradication of feral domestic cats Felis catus on sub-Antarctic Marion Island (Bester et al. 2002) and compared modelled outputs to observed data. The parameters of the best-fitting model reflected the conditions of the management programme, and the model successfully simulated the observed movement of the cat population to the southern and eastern portion of the island under hunting pressure. We further demonstrated that none of the management strategies would likely have been successful within a reasonable timeframe if performed in isolation. 4. SPADE is applicable to a wide range of population management problems, and allows easy generation, modification and analysis of management scenarios. It is a useful tool for the planning, evaluation and optimisation of the management of wild populations, and can be used without specialised training.

Dieter Schwarz, according to Forbes, was the wealthiest person in Germany as of 2025, with assets amounting to around 38.5 billion U.S. dollars. Dieter Schwarz is the owner of the Schwarz Gruppe, which owns both the supermarket chains Kaufland and Lidl. Klaus-Michael Kühne follows in second place. Among other things, he is the majority owner of the logistics company Kühne + Nagel International AG. Who are the richest people in Germany? Dieter Schwarz is the owner of the Schwarz Gruppe, which owns both the supermarket chains Kaufland and Lidl. In 2023, the Schwarz Gruppe recorded around 56.55 million euros in revenue. Karl Albrecht Jr. is the owner of the popular discount grocery store chain Aldi Süd. As of 2023, there were over 2,000 Aldi Süd stores in Germany. This, of course, does not include all the stores in other countries such as Ireland, the United Kingdom, and Australia, just to name a few. German salaries Germany currently has the highest GDP in Europe. However, this does not mean that everyone in Germany is a billionaire. The average salary is, in fact, around 48,380 euros. Although the trend is that German salaries have been on the rise since 2000, there has been a dip since 2019. This is probably due to the COVID-19 pandemic and the inflation of 2022.

During the winter and spring of 2002, underwater calling rates were measured near mid-day on an opportunistic basis at 7 breeding sites and, at two breeding sites, over 24 hour periods once a month. The data were analysed with respect to reproductive season (early ice formation, prebreeding, pupping and mating) and if the recordings were made when it was dark or twilight/light.

Taken from the abstract of the paper referenced below:

Underwater vocalisation monitoring and surveys, both on ice and underwater, were used to determine if Weddell seals (Leptonychotes weddellii) near Mawson Station, Antarctica, remain under the fast ice during winter within close range of breeding sites. Daytime and nighttime underwater calling rates were examined at seven breeding sites during austral winter and spring to identify seasonal and diel patterns. Seals rarely hauled out at any of the sites during winter, although all cohorts (adult males, females, and juveniles) were observed underwater and surfacing at breathing holes throughout winter (June-September) and spring (October-December). Seal vocalisations were recorded during each sampling session throughout the study (n=102 daytime at seven sites collectively, and n=5 24-h samples at each of two sites). Mean daytime calling rate was low in mid-winter (July) (mean = 18.9 plus or minus 7.1 calls per minute) but increased monthly, reaching a peak during the breeding season (November) (mean = 62.6 plus or minus 15.7 calls per minute). Mean nighttime calling rate was high throughout the winter and early spring (July-October) with mean nocturnal calling rate in July (mean = 61.8 plus or minus 35.1 calls per minute) nearly equal to mean daytime calling rate in November (during 24-h daylight). Reduced vocal behaviour during winter daylight periods may result from animals utilising the limited daylight hours for nonvocal activities, possibly feeding.

The following study sites were among those used in this project (provided by Phil Rouget):

• Forbes site (identified as Site 6 in the paper) is located at Forbes Glacier (approx. 0.5 km to the west of the glacier tongue and approximately 200 meters offshore of the mainland). (67 degrees 35.256 minutes S, 62 degrees 16.756 minutes E)

• Kista site is located in the middle of Kista Strait (site 7 in the Marine Mammal Science paper). (67 degrees, minutes 33.840 S, 62 degrees 47.402, minutes E)

• SPA site was our site located just west of the western boundary of the SPA which itself is located west of Mawson and east of Forbes Glacier. (Site 2 in Marine Mammal Science paper). (67 degrees 35.179 S, 62 degrees 25.425 minutes E)

• McDonald Islands (or Rocks) was the site located North/NorthWest of Kista Strait, as it is named so on the Framens Mtn. Nautical Chart. From memory, it was approximately 12 km north/north west of Mawson Station. (This was site 5 in the Marine Mammal Science paper). (67 degrees 29.414 minutes S, 62 degrees 41.011 minutes E)

• Stewart Rocks (also named Sewart Rocks on an alternative map) is located due north of Mawson Station, also by about 12 km. (East of McDonald site, and North East of Kista). This was site 4 in the Marine Mammal Science paper. (67 degrees 29.933 minutes S, 62 degrees 51.765 minutes E)

• Anderson Rocks is an extensive group of rocky islets west of Auster Island (approximately 6-7 km offshore). This was site 3 in the Marine Mammal Science paper. (67 degrees 26.445 minutes S, 63 degrees 25.414 minutes E)

• SEAL MO was located just north of Macey Hut by about 2 km. This was site 1 in the Marine Mammal Science paper. (67 degrees 23.399 minutes S, 63 degrees 47.977 minutes E)

• Aside from SEAL MO and SPA, the names from all these sites are indicated in the Framnes Mountain Chart.

An image showing the locations of the fields sites is also part of the download file.

The fields in this dataset are:

Site Period Day Calling rate photoperiod Sun time

This collection provides a complete list of convict names and sufficient biographical data to enable unambiguous identification of convicts who were disembarked from convict ship "Sir Charles Forbes (2)" at Van Diemen's Land on 1827-01-03

This includes, where known, an estimation of the year of birth, place of birth, where and when convicted, the sentence, the date of arrival in the colony and the convict's age on arrival. The brief convict biographical data provided in this collection serves as an index into the far more extensive set of life course events which are recorded in the prosopgraphy database built by the Founders and Survivors project.

Basic details for this ship: * ship name (as known in VDL records): Sir Charles Forbes (2) * sailed date : 1826-09-16 from London * arrival date : 1827-01-03 * population (per Bateson's The Convict Ships): Embarked:73 Women ; Deaths:4 Women ; Landed:69(VDL) Women

Data for convicts listed in this collection comes from the source which has been determined by Founders and Survivors to form the "base population" for this ship. Further information as to the methodology and the linkage of multiple sources is detailed in the narrative format of the collection. The matching and linkage of additional sources about Tasmanian convict's is the subject of ongoing research. This collection may be repuplished regularly, and in additional formats and with specific user interfaces, to enable public participation in the quality of convict matching and linkage -- see for example the EXPERIMENTAL linkage.htm format for this collection. Linkage for ships arriving at Norfolk Island and Port Philip is incomplete.

This ship's prosopography index is published in a directory named "358.34" (the ship's project id). Three three different file formats provided: -- (default; suitable for web browsing) HTML: world wide web hypertext markup language format which provides a "narrative" view of the collection (index.htm); and -- (structured prosopgraphy: persons and events) XML / TEIp5 : Text Encoding Initiative (version p5) XML format which provides the underlying XML database for this collection (index.xml); and -- Not yet available simple list of convict names in a flat file, tab delimited, suitable for Excel, Stata, SPSS or database usage (index.tab). See notes below.

In 2024, Eduardo Saverin was ranked by the Forbes World's Billionaires List as the wealthiest person in Brazil. Vicky Safra ranked second, with the Greek born billionaire and her family having a fortune worth 16.7 billion U.S. dollars. She was followed by the investment banker and businessman, Jorge Paulo Lemann, with a fortune of around 15.8 billion U.S. dollars.

Survival models assessed for female sage-grouse.

In 2024, Forbes released their annual list of billionaires, where nine Chileans where included. The Chilean mining magnate and businesswoman, Iris Fontbona, had a fortune worth 23.1 billion U.S. dollars and was thus the richest person in the country. Jean Salata followed, with a fortune of 5.5 billion dollars.

Abstract

The Labor Force Survey is a nationwide survey of households conducted regularly to gather data on the demographic and socio-economic characteristics of the population. It is primarily geared towards the estimation of the levels of employment in the country.

The Labor Force Survey aims to provide a quantitative framework for the preparation of plans and formulation of policies affecting the labor market.

Geographic coverage

National coverage, the sample design has been drawn in such a way that accurate lower level classification would be possible. The 74 provinces, 24 cities and eight key municipalities are covered.

Analysis unit

• Person/ individual

Universe

The survey covered all persons 10 years old and over. Persons who reside in institutions are not covered.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design of the Labor Force Survey adopts that of the Integrated Survey of Households (ISH), which uses a stratified two-stage sampling design. It is prepared by the NEDA Technical Committee on Survey Design and first implemented in 1984. It is the same sampling design used in the ISH modules starting in 1986.

The urban and rural areas of each province are the principal domains of the survey. In addition, the urban and rural areas of cities with a population of 150,000 or more as of 1990 are also made domains of the survey with urban and rural dimensions. These include the four cities and five municipalities of Metro Manila (Manila, Quezon City, Pasay and Caloocan; Valenzuela, Paranaque, Pasig, Marikina and Makati), and other key cities such as Baguio, Angeles, Cabanatuan, Olongapo, Batangas, Lipa, Lucena, San Pablo, Bacolod, Iloilo, Cebu, Mandaue, Zamboanga, Butuan, Cagayan de Oro, Davao, General Santos, and Iligan and key municipalities such as San Fernando, Pampanga and Tarlac, Tarlac.

The rest of Metro Manila, i.e., the remaining municipalities are treated as separate domains. In the case of Makati, six exclusive villages are identified and samples are selected using a different scheme. These villages are Forbes Park, Bel-Air, Dasmarinas, San Lorenzo, Urdaneta and Magallanes.

Because of the creation of the Autonomous Region of Muslim Mindanao (ARMM), this, defining its areas of coverage, Marawi City and Cotabato Cfity are likewises treated as domains.

Sampling Units and Sampling Frame The primary sampling units (PSUs) under the sample design are the barangays and the households within each sample barangay comprise the secondary sampling units (SSUs). The frame from which the sample barangays are drawn is obtained from the 1990 Census of Population and Housing (CPH). Hence, all the approximately 40,000 barangays covered in the 1990 CPH are part of the primary sampling frame. The sampling frame for the SSUs, that is, the households, is prepared by listing all households in each of the selected sample barangays. The listing operation is conducted regularly in the sample barangays to update the secondary sampling frame from where the sample households are selected.

Sample Size and Sampling Fraction The size of the sample is envisioned to meet the demand for fairly adequate statistics at the domain level. Taking this need into account and considering cost constraints as well, the decision reached is for a national sample of about 26,000 households. In general, the sample design results in self-weighting samples within domains, with a uniform sampling fraction of 1:400 for urban and 1:600 for rural areas. However, special areas are assigned different sampling fractions so as to obtain "adequate" samples for each. Special areas refer to the urban and rural areas of a province or large city which are small relative to their counterparts.

Selection of Samples For the purpose of selecting PSUs, the barangay in each domain are arranged by population size (as of the 1990 Census of Population) in descending order and then grouped into strata of approximately equal sizes. Four independent PSUs are drawn with probability proportional to size with complete replacement.

Secondary sampling units are selected systematiclally with a random start.

Sampling deviation

Replacement of non-responding or transferred sample households is allowed although it is still possible to have non-response cases due to critical peace and order situation or inaccessibility of the selected sample households. If there are unenumerated barangays or sample households, non-response adjustments are utilized.

Mode of data collection

Face-to-face [f2f]

Research instrument

The items of information presented in the July 1991 Quarterly Labor Force Survey questionnaire were derived from a structured questionnaire covering the demographic and economic characteristics of individuals. The demographic characteristics include age, sex, relationship to household head, marital status, and highest grade completed. The economic characteristics include employment status, occupation, industry, nomal working hours, total hours worked, class of worker, etc.

Cleaning operations

Data processing involves two stages: manual processing and machine processing. Manual processing refers to the manual editing and coding of questionnaires. This was done prior to machine processing which entailed code validation, consistency checks as well as tabulation.

Enumeration is a very complex operation and may happen that accomplished questionnaires may have some omissions and implausible or inconsistent entries. Editing is meant to correct these errors.

For purposes of operational convenience, field editing was done. The interviewers were required to review the entries at the end of each interview. Blank items, which were applicable to the respondents, were verified and filled out. Before being transmitted to the regional office, all questionnaires were edited in the field offices.

Coding, the transformation of information from the questionnaire to machine readable form, was likewise done in the field offices.

Machine processing involved all operations that were done with the use of a computer and/or its accessories, that is, from data encoding to tabulation. Coded data are usually in such media as tapes and diskettes. Machine editing is preferred to ensure correctness of encoded information. Except for sample completeness check and verification of geographic identification which are the responsibility of the subject matter division, some imputations and corrections of entries are done mechanically.

Response rate

The response rate for January 1992 LFS was 99.94 percent. The non-response rate of 0.06 percent was due to crticial peace and order situation or inaccessibility of the selected sample or sample households.

Sampling error estimates

Standard Error (SE) and Coefficient of Variation (CV) for the selected variables of the Labor Force Survey (LFS) for July 1991 survey round was computed using the statistical package IMPS. The selected variables referred to include the employment, unemployment and labor force population levels and rates.

A sampling error is usually measured in terms of the standard error for a particular statistic. A standard error is a measure of dispersion of an estimate from the expected value.

The SE can be used to calculate confidence intervals within which the true value for the population can be estimated, while the CV is a measure of relative variability that is commonly used to assess the precision of survey estimates.

The CV is defined as the ratio of the standard error and the estimate. An estimate with CV value of less than 10 percent is considered precise.

According to the Forbes World's Billionaires List of 2024, Amancio Ortega is the wealthiest Spaniard. Ortega is the founder of Inditex fashion group, known mainly for its Zara and Bershka retail chains. As of 2023, he had a fortune worth nearly 103 billion U.S. dollars. His daughter, Sandra Ortega Mera, is the second richest person in the country, with a fortune of over nine billion U.S. dollars.

Abstract

The Labor Force Survey is a nationwide survey of households conducted regularly to gather data on the demographic and socio-economic characteristics of the population. It is primarily geared towards the estimation of the levels of employment in the country.

The Labor Force Survey aims to provide a quantitative framework for the preparation of plans and formulation of policies affecting the labor market.

Geographic coverage

National coverage, the sample design has been drawn in such a way that accurate lower level classification would be possible. The 74 provinces, 24 cities and eight key municipalities are covered.

Analysis unit

• Person/ individual

Universe

The survey covered all persons 10 years old and over. Persons who reside in institutions are not covered.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design of the Labor Force Survey adopts that of the Integrated Survey of Households (ISH), which uses a stratified two-stage sampling design. It is prepared by the NEDA Technical Committee on Survey Design and first implemented in 1984. It is the same sampling design used in the ISH modules starting in 1986.

The urban and rural areas of each province are the principal domains of the survey. In addition, the urban and rural areas of cities with a population of 150,000 or more as of 1990 are also made domains of the survey with urban and rural dimensions. These include the four cities and five municipalities of Metro Manila (Manila, Quezon City, Pasay and Caloocan; Valenzuela, Paranaque, Pasig, Marikina and Makati), and other key cities such as Baguio, Angeles, Cabanatuan, Olongapo, Batangas, Lipa, Lucena, San Pablo, Bacolod, Iloilo, Cebu, Mandaue, Zamboanga, Butuan, Cagayan de Oro, Davao, General Santos, and Iligan and key municipalities such as San Fernando, Pampanga and Tarlac, Tarlac.

The rest of Metro Manila, i.e., the remaining municipalities are treated as separate domains. In the case of Makati, six exclusive villages are identified and samples are selected using a different scheme. These villages are Forbes Park, Bel-Air, Dasmarinas, San Lorenzo, Urdaneta and Magallanes.

Because of the creation of the Autonomous Region of Muslim Mindanao (ARMM), this, defining its areas of coverage, Marawi City and Cotabato Cfity are likewises treated as domains.

Sampling Units and Sampling Frame The primary sampling units (PSUs) under the sample design are the barangays and the households within each sample barangay comprise the secondary sampling units (SSUs). The frame from which the sample barangays are drawn is obtained from the 1990 Census of Population and Housing (CPH). Hence, all the approximately 40,000 barangays covered in the 1990 CPH are part of the primary sampling frame. The sampling frame for the SSUs, that is, the households, is prepared by listing all households in each of the selected sample barangays. The listing operation is conducted regularly in the sample barangays to update the secondary sampling frame from where the sample households are selected.

Sample Size and Sampling Fraction The size of the sample is envisioned to meet the demand for fairly adequate statistics at the domain level. Taking this need into account and considering cost constraints as well, the decision reached is for a national sample of about 26,000 households. In general, the sample design results in self-weighting samples within domains, with a uniform sampling fraction of 1:400 for urban and 1:600 for rural areas. However, special areas are assigned different sampling fractions so as to obtain "adequate" samples for each. Special areas refer to the urban and rural areas of a province or large city which are small relative to their counterparts.

Selection of Samples For the purpose of selecting PSUs, the barangay in each domain are arranged by population size (as of the 1990 Census of Population) in descending order and then grouped into strata of approximately equal sizes. Four independent PSUs are drawn with probability proportional to size with complete replacement.

Secondary sampling units are selected systematiclally with a random start.

Sampling deviation

Replacement of non-responding or transferred sample households is allowed although it is still possible to have non-response cases due to critical peace and order situation or inaccessibility of the selected sample households. If there are unenumerated barangays or sample households, non-response adjustments are utilized.

Mode of data collection

Face-to-face [f2f]

Research instrument

The items of information presented in the October 1991 Quarterly Labor Force Survey questionnaire were derived from a structured questionnaire covering the demographic and economic characteristics of individuals. The demographic characteristics include age, sex, relationship to household head, marital status, and highest grade completed. The economic characteristics include employment status, occupation, industry, nomal working hours, total hours worked, class of worker, etc.

Cleaning operations

Data processing involves two stages: manual processing and machine processing. Manual processing refers to the manual editing and coding of questionnaires. This was done prior to machine processing which entailed code validation, consistency checks as well as tabulation.

Enumeration is a very complex operation and may happen that accomplished questionnaires may have some omissions and implausible or inconsistent entries. Editing is meant to correct these errors.

For purposes of operational convenience, field editing was done. The interviewers were required to review the entries at the end of each interview. Blank items, which were applicable to the respondents, were verified and filled out. Before being transmitted to the regional office, all questionnaires were edited in the field offices.

Coding, the transformation of information from the questionnaire to machine readable form, was likewise done in the field offices.

Machine processing involved all operations that were done with the use of a computer and/or its accessories, that is, from data encoding to tabulation. Coded data are usually in such media as tapes and diskettes. Machine editing is preferred to ensure correctness of encoded information. Except for sample completeness check and verification of geographic identification which are the responsibility of the subject matter division, some imputations and corrections of entries are done mechanically.

Response rate

The response rate for October 1991 LFS was 99.81 percent. The non-response rate of 0.19 percent was due to crticial peace and order situation or inaccessibility of the selected sample or sample households.

Sampling error estimates

Standard Error (SE) and Coefficient of Variation (CV) for the selected variables of the Labor Force Survey (LFS) for October1991 survey round was computed using the statistical package IMPS. The selected variables referred to include the employment, unemployment and labor force population levels and rates.

A sampling error is usually measured in terms of the standard error for a particular statistic. A standard error is a measure of dispersion of an estimate from the expected value.

The SE can be used to calculate confidence intervals within which the true value for the population can be estimated, while the CV is a measure of relative variability that is commonly used to assess the precision of survey estimates.

The CV is defined as the ratio of the standard error and the estimate. An estimate with CV value of less than 10 percent is considered precise.

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