Abstract

The Future of Business Survey is a new source of information on small and medium-sized enterprises (SMEs). Launched in February 2016, the monthly survey - a partnership between Facebook, OECD, and The World Bank - provides a timely pulse on the economic environment in which businesses operate and who those businesses are to help inform decision-making at all levels and to deliver insights that can help businesses grow. The Future of Business Survey provides a perspective from newer and long-standing digitalized businesses and provides a unique window into a new mobilized economy.

Policymakers, researchers and businesses share a common interest in the environment in which SMEs operate, as well their outlook on the future, not least because young and innovative SMEs in particular are often an important source of considerable economic and employment growth. Better insights and timely information about SMEs improve our understanding of economic trends, and can provide new insights that can further stimulate and help these businesses grow.

To help provide these insights, Facebook, OECD and The World Bank have collaborated to develop a monthly survey that attempts to improve our understanding of SMEs in a timely and forward-looking manner. The three organizations share a desire to create new ways to hear from businesses and help them succeed in the emerging digitally-connected economy. The shared goal is to help policymakers, researchers, and businesses better understand business sentiment, and to leverage a digital platform to provide a unique source of information to complement existing indicators.

With more businesses leveraging online tools each day, the survey provides a lens into a new mobilized, digital economy and, in particular, insights on the actors: a relatively unmeasured community worthy of deeper consideration and considerable policy interest.

Geographic coverage

Argentina Australia Belgium Brazil Canada Colombia Egypt France Germany Ghana India Indonesia Ireland Israel Italy Kenya Mexico Nigeria Pakistan Philippines (the) Poland Portugal Russian Federation (the) South Africa Spain Taiwan Turkey United Kingdom of Great Britain and Northern Ireland (the) United States of America (the) Viet Nam

Analysis unit

The study describes small and medium-sized enterprises.

Universe

The target population consists of SMEs that have an active Facebook business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. With more businesses leveraging online tools each day, the survey provides a lens into a new mobilized, digital economy and, in particular, insights on the actors: a relatively unmeasured community worthy of deeper consideration and considerable policy interest.

Kind of data

Sample survey data [ssd]

Sampling procedure

Twice a year in over 97 countries, the Facebook Survey Team sends the Future of Business to admins and owners of Facebook-designated small business pages. When we share data from this survey, we anonymize responses to all survey questions and only share country-level data publicly. To achieve better representation of the broader small business population, we also weight our results based on known characteristics of the Facebook Page admin population.

A random sample of firms, representing the target population in each country, is selected to respond to the Future of Business Survey each month.

Mode of data collection

Internet [int]

Research instrument

The survey includes questions about perceptions of current and future economic activity, challenges, business characteristics and strategy. Custom modules include questions related to regulation, access to finance, digital payments, and digital skills. The full questionnaire is available for download.

Response rate

Response rates to online surveys vary widely depending on a number of factors including survey length, region, strength of the relationship with invitees, incentive mechanisms, invite copy, interest of respondents in the topic and survey design.

Note: Response rates are calculated as the number of respondents who completed the survey divided by the total number of SMEs invited.

Sampling error estimates

Any survey data is prone to several forms of error and biases that need to be considered to understand how closely the results reflect the intended population. In particular, the following components of the total survey error are noteworthy:

Sampling error is a natural characteristic of every survey based on samples and reflects the uncertainty in any survey result that is attributable to the fact that not the whole population is surveyed.

Other factors beyond sampling error that contribute to such potential differences are frame or coverage error (sampling frame of page owners does not include all relevant businesses but also may include individuals that don't represent businesses), and nonresponse error.

Note that the sample is meant to reflect the population of businesses on Facebook, not the population of small businesses in general. This group of digitized SMEs is itself a community worthy of deeper consideration and of considerable policy interest. However, care should be taken when extrapolating to the population of SMEs in general. Moreover, future work should evaluate the external validity of the sample. Particularly, respondents should be compared to the broader population of SMEs on Facebook, and the economy as a whole.

Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

Luxembourg Business Survey: Industry: BM: Recent Production Trends: Decrease data was reported at 7.000 % in Jul 2018. This records an increase from the previous number of 2.000 % for Jun 2018. Luxembourg Business Survey: Industry: BM: Recent Production Trends: Decrease data is updated monthly, averaging 6.000 % from Jan 1992 (Median) to Jul 2018, with 319 observations. The data reached an all-time high of 100.000 % in Jan 2012 and a record low of 0.000 % in May 2018. Luxembourg Business Survey: Industry: BM: Recent Production Trends: Decrease data remains active status in CEIC and is reported by The Portal of Statistics of Luxembourg. The data is categorized under Global Database’s Luxembourg – Table LU.S001: Business Survey: Industry.

Granite State Poll is a quarterly poll conducted by the University of New Hampshire Survey Center. The poll sample consists of about 500 New Hampshire adults with a working telephone across the state. Each poll contains a series of basic demographic questions that are repeated in future polls, as well as a set of unique questions that are submitted by clients. This poll includes four questions related to preferences about dams. These questions were designed by Natallia Leuchanka Diessner, Catherine M. Ashcraft, Kevin H. Gardner, and Lawrence C. Hamilton as part of the "Future of Dams" project.This Technical Report was written by the UNH Survey Center and describes the protocols and standards of the Granite State Poll #68 (Client Poll), which includes questions related to preferences about dams, designed by Natallia Leuchanka Diessner, Catherine M. Ashcraft, Kevin H. Gardner, and Lawrence C. Hamilton as part of the "Future of Dams" project.The first file is a screenshot of the Technical Report to provide a preview for Figshare. The second file is the Technical Report in Microsoft Word format.

About this dataset

Context

The dataset tabulates the population of New Point by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for New Point. The dataset can be utilized to understand the population distribution of New Point by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in New Point. 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 New Point.

Key observations

Largest age group (population): Male # 60-64 years (26) | Female # 40-44 years (16). 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 New Point population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the New Point is shown in the following column.
• Population (Female): The female population in the New Point 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 New Point 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 New Point Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of New Freedom by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for New Freedom. The dataset can be utilized to understand the population distribution of New Freedom by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in New Freedom. 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 New Freedom.

Key observations

Largest age group (population): Male # 60-64 years (221) | Female # 55-59 years (271). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 New Freedom population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the New Freedom is shown in the following column.
• Population (Female): The female population in the New Freedom 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 New Freedom 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 New Freedom Population by Gender. You can refer the same here

Aeromagnetic data were collected along flight lines by instruments in an aircraft that recorded magnetic-field values and locations. This dataset presents latitude, longitude, altitude, and magnetic-field values.

Abstract

Iraq successfully conducted two rounds of Integrated Household Socioeconomic Survey (IHSES), nationally representative multi-topic budget surveys, in 2007 and 2012. The surveys allowed an analysis of a range of socio-economic indicators and the estimation of poverty trends. To provide more frequent poverty estimates, Continuous Household Survey (CHS) was implemented in 2014 on a sub-sample of IHSES clusters. However, the fieldwork was disrupted in the summer of 2014 in some parts of the country due to the deterioration in the security situation. The third round of IHSES, planned for 2017, could not take place on time as well. At the same time, the ongoing security and budget crises made it more important than ever to monitor key socio-economic indicators. The objective of the 2017 rapid welfare monitoring survey (SWIFT) was to provide interim estimates of welfare and well-being until another survey comparable in scope and coverage to IHSES could be fielded.

Geographic coverage

Although the security situation had improved since 2014, many parts of the country were still insecure 2017. Thus, nine out of ten districts in Nineveh governorate, the seat of Daesh-occupied Iraq, were intentionally excluded from the sampling frame. As the data collection proceeded, five additional districts – 3 in Anbar, 1 in Baghdad, and 1 in Salah al-din – were judged to be too insecure for fieldwork so the selected enumeration areas from these areas were replaced with other clusters from the same governorate. Thus, the final sample covers only 106 of 120 districts in the country.

Analysis unit

Individual and Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2009 census of dwellings, the most recent sampling frame available for Iraq, served as the sampling frame for the SWIFT survey. Given the large number of people displaced within the country since 2014, the survey was designed to capture a representative sample of internally displaced persons. Furthermore, given the prevalence of Syrian refugees in the Kurdistan region, the survey also sampled refugee households in Kurdistan. A socioeconomic survey of camp residents was conducted by CSO and KRSO in 2017 so to avoid duplication of effort, the camp residents were excluded from the SWIFT survey. Informal ad-hoc settlements that have been constructed since the last update of sampling frame were included in the survey through household listing operation in the sampled enumeration areas. The survey was designed to cover all governorates, including areas in Nineveh deemed safe for field visits.

The sampling design followed a nested logic. All households in the sample responded to a short questionnaire. The short form collected information on the following core non-monetary indicators of well-being: household roster, education attainment, labor market variables, dwelling characteristics, access to basic services, asset ownership, transfers and assistance (public and private), incidence of shocks, and subjective well-being. A random subset of the sampled households also responded to the complete list of questions on household expenditure. The full sample was designed to be representative for each governorate. The expenditure sub-sample was representation at the regional level, where each region comprises three to five governorates.

Within each governorate, the out-of-camp sample was selected in two stages as following. First, using the exhaustive list of Census Enumeration Areas as Primary Sampling Units (PSUs), between 60 to 150 EAs in each governorate was selected using Probability Proportional to Size (PPS) criteria, with the number of households in each area as the measure of size. Listing exercise was conducted in the selected areas to update the list of households. In the second stage, using households as secondary sampling units (SSUs), six households were selected in each cluster with equal probability from the post-listing sampling frame. The sample of households in the second stage was stratified by residence status. In selecting six households from a cluster, three each of IDP and non-IDP households were selected in the non-Kurdistan region. In the Kurdistan region, two each of IDP, non-IDP, and refugee households were selected. If an enumeration area in Kurdistan had fewer than two refugee or IDP households, the gap was filled by randomly selecting resident households from the same enumeration area. Likewise, if a PSU in the rest of Iraq had fewer than three IDP households, the shortfall was met by resident households to reach a total of 6 households per PSU.

Expenditure information was collected from a subsample of households from a subsample of enumeration areas. In Kurdistan, one household each of residents, IDPs, and refugees in a subset of clusters responded to the expenditure questions and in Rest of Iraq, one household each of residents and IDPs answered the expenditure questions.

Sampling deviation

Due to insecurity, the survey could be implemented in only 106 of 120 districts (qhadas) in the country.

Mode of data collection

Computer Assisted Personal Interview [capi]

Labour Force Survey 2001 - 4th quarter.

As of the 1st quarter of 1972, SSB has conducted official quarterly labour force surveys (AKU). These surveys aim to give the labour force authorities (and other people interested) knowledge of the occupational structure of the population and how it develops over time. The surveys are meant to give a foundation and statistical material for occupational prognoses and labour research. The samples in AKU are from 1992 representative at county level. In the period 1972-1991 they were representative on county pair level.

Originally, AKU respondents were interviewed in two consecutive quarters of a year, followed by a pause of two quarters, and then another two quarters of interviews. The sample was approximately 10-11.000 respondents in each quarter up until 1988. Originally, AKU was intended to be an analytical supplement to the monthly occupational statistics that was based on the social security membership index file. However, the social security-based statistics disappeared when the sickness benefit was included in the National Insurance as of 1st of January 1971, and AKU has after gradually developed into the most significant source of knowledge of the state of the labour market and its development.

In 1975, Statistics Norway changed the sampling frame of survey research, see article 37: “Om bruk av stikkprøver ved kontoret for intervjuundersøkelser”, SSB (About the Use of Random Samples at the Office for Survey Research, Statistics Norway) by Steinar Tamsfoss, and SØS 33: “Prinsipper og metoder for Statistisk sentralbyrås utvalgsundersøkelse (Principles and Methods for Statistics Norway's sample research) by Ib Thomsen. Simultaneously, the method for estimation of inflation to national numbers was changed, so that reasonable numbers for regions do exist from 1975 and onwards. The change in 1975 led to a different way of interviewing in groups. This caused amongst other things a break with the AKU panel systematics.

In the AKU survey of 1976, a slightly changed questionnaire was introduced. Also, there was a return to the original 6-quarter rotation scheme. The new questionnaire implied a better identification of family workers and persons that are temporarily without paid work. Thus, 30-35 000 more people were defined as employed. The group of "job-seekers without income" were also extended to include persons that were on an involuntary leave of absence. The questions concerning underemployment and “over employment” in the original questionnaire were abandoned.

From the 1st quarter of 1987, the estimation method (inflation to national numbers) was slightly changed. There was also a minor adjustment in the definition of employment. In order to ensure that the numbers were to be comparable to earlier surveys, new versions of the 1980-1986 AKU-files were drawn up. Consequently two versions of the 1980-1987 files - respectively with the old and new methods of estimation - exist. The “old” means that the data are comparable to the original numbers published in the period of 1972 - 1987, whilst the “new” implies that the data are comparable to numbers published after 1987.

Between the 1st and 2nd quarter of 1988, the AKU file description was changed. The variable “Labour-market status” was given a different coding. In addition, adjustments in the data collections were made - from interviewing a specific week every quarter to carry out continuous weekly interviews. SSB also started up an escalation scheme to increase the sample size. This affected the weights, and from the 2nd quarter of 1988, these were recalculated monthly. To balance out the quarterly or yearly files to total national numbers, the monthly weights therefore had to be divided in three or twelve to give the correct total number.

In 1996, AKU was significantly revised: The questionnaire, the file description and the standard for coding of industry and occupation. The data collection also changed to CATI - Computer Assisted Telephone Interviewing. A new classification of industry was put into use (NOS C 182, based on the EU standard NACE, Rev.1). This standard was updated in 2002 and 2007. Also, the new occupational classification (STYRK) based on ISCO 88 was used from 1996 and onwards. The variable indicating socio-economic status was omitted, as a similar variable was not developed in the new occupational classification.

As from January 2006 some major changes were introduced to AKU in order to enhance its comparability to similar surveys in other countries. The changes consist of minor definitional adjustments of unemployment, some adjustments and enlargement of the questionnaire and a change in age definition (age at reference point instead of at the end of the year). Simultaneously the lower age limit to be included in AKU was lowered from 16 to 15 years. This led to...

Labour Force Survey 2010, 2nd quarter

As of the 1st quarter of 1972, SSB has conducted official quarterly labour force surveys (AKU). These surveys aim to give the labour force authorities (and other people interested) knowledge of the occupational structure of the population and how it develops over time. The surveys are meant to give a foundation and statistical material for occupational prognoses and labour research. The samples in AKU are from 1992 representative at county level. In the period 1972-1991 they were representative on county pair level.

As from January 2006 some major changes were introduced to AKU in order to enhance its comparability to similar surveys in other countries. The changes consist of minor definitional adjustments of unemployment and educational level, some adjustments and enlargement of the questionnaire and a change in age definition (age at reference point instead of at the end of the year). Simultaneously the lower age limit to be included in AKU was lowered from 16 to 15 years. This led to some breaks in the time series in the aforementioned areas.

As of the 1st quarter of 2009 the new classification of economic activities: SN2007/ISIC rev 5 replaces SN2002/ISIC Rev 4.

Originally, AKU respondents were interviewed in two consecutive quarters of a year, followed by a pause of two quarters, and then another two quarters of interviews. The sample was approximately 10-11.000 respondents in each quarter up until 1988. Originally, AKU was intended to be an analytical supplement to the monthly occupational statistics that was based on the social security membership index file. However, the social security-based statistics disappeared when the sickness benefit was included in the National Insurance as of 1st of January 1971, and AKU has after gradually developed into the most significant source of knowledge of the state of the labour market and its development.

In 1975, Statistics Norway changed the sampling frame of survey research, see article 37: “Om bruk av stikkprøver ved kontoret for intervjuundersøkelser”, SSB (About the Use of Random Samples at the Office for Survey Research, Statistics Norway) by Steinar Tamsfoss, and SØS 33: “Prinsipper og metoder for Statistisk sentralbyrås utvalgsundersøkelse (Principles and Methods for Statistics Norway's sample research) by Ib Thomsen. Simultaneously, the method for estimation of inflation to national numbers was changed, so that reasonable numbers for regions do exist from 1975 and onwards. The change in 1975 led to a different way of interviewing in groups. This caused amongst other things a break with the AKU panel systematics.

In the AKU survey of 1976, a slightly changed questionnaire was introduced. Also, there was a return to the original 6-quarter rotation scheme. The new questionnaire implied a better identification of family workers and persons that are temporarily without paid work. Thus, 30-35 000 more people were defined as employed. The group of "job-seekers without income" were also extended to include persons that were on an involuntary leave of absence. The questions concerning underemployment and “over employment” in the original questionnaire were abandoned.

From the 1st quarter of 1987, the estimation method (inflation to national numbers) was slightly changed. There was also a minor adjustment in the definition of employment. In order to ensure that the numbers were to be comparable to earlier surveys, new versions of the 1980-1986 AKU-files were drawn up. Consequently two versions of the 1980-1987 files - respectively with the old and new methods of estimation - exist. The “old” means that the data are comparable to the original numbers published in the period of 1972 - 1987, whilst the “new” implies that the data are comparable to numbers published after 1987.

Between the 1st and 2nd quarter of 1988, the AKU file description was changed. The variable “Labour-market status” was given a different coding. In addition, adjustments in the data collections were made - from interviewing a specific week every quarter to carry out continuous weekly interviews. SSB also started up an escalation scheme to increase the sample size. This affected the weights, and from the 2nd quarter of 1988, these were recalculated monthly. To balance out the quarterly or yearly files to total national numbers, the monthly weights therefore had to be divided in three or twelve to give the correct total number.

In 1996, AKU was significantly revised: The questionnaire, the file description and the standard for coding of industry and occupation. The data collection also changed to CATI - Computer Assisted Telephone Interviewing. A new classification of industry was put into use (NOS C 182, based on the EU standard NACE, Rev.1). This standard was updated in 2002 and 2007. Also, the new Norwegian standard...

The COVID-19 pandemic has dramatically altered family life in the United States. Over the long duration of the pandemic, parents had to adapt to shifting work conditions, virtual schooling, the closure of daycare facilities, and the stress of not only managing households without domestic and care supports but also worrying that family members may contract the novel coronavirus. Reports early in the pandemic suggest that these burdens have fallen disproportionately on mothers, creating concerns about the long-term implications of the pandemic for gender inequality and mothers’ well-being. Nevertheless, less is known about how parents’ engagement in domestic labor and paid work has changed throughout the pandemic and beyond, what factors may be driving these changes, and what the long-term consequences of the pandemic may be for the gendered division of labor and gender inequality more generally. The Study on U.S. Parents’ Divisions of Labor During COVID-19 (SPDLC) collects longitudinal survey data from partnered U.S. parents that can be used to assess changes in parents’ divisions of domestic labor, divisions of paid labor, and well-being throughout and after the COVID-19 pandemic. The goal of SPDLC is to understand both the short- and long-term impacts of the pandemic for the gendered division of labor, work-family issues, and broader patterns of gender inequality. Survey data for this study is collected using Prolifc (www.prolific.co), an opt-in online platform designed to facilitate scientific research. The sample is comprised U.S. adults who were residing with a romantic partner and at least one biological child (at the time of entry into the study). In each survey, parents answer questions about both themselves and their partners. Wave 1 of the SPDLC was conducted in April 2020, and parents who participated in Wave 1 were asked about their division of labor both prior to (i.e., early March 2020) and one month after the pandemic began. Wave 2 of the SPDLC was collected in November 2020. Parents who participated in Wave 1 were invited to participate again in Wave 2, and a new cohort of parents was also recruited to participate in the Wave 2 survey. Wave 3 of SPDLC was collected in October 2021. Parents who participated in either of the first two waves were invited to participate again in Wave 3, and another new cohort of parents was also recruited to participate in the Wave 3 survey. Wave 4 of the SPDLC was collected in October 2022. Parents who participated in either of the first three waves were invited to participate again in Wave 4, and another new cohort of parents was also recruited to participate in the Wave 4 survey. Wave 5 of the SPDLC was collected in October 2023. Parents who participated in any of the first four waves were invited to participate again in Wave 5, and another new cohort of parents was also recruited to participate in the Wave 5 survey. This research design (follow-up survey of panelists and new cross-section of parents at each wave) will continue through 2024, culminating in six waves of data spanning the period from March 2020 through October 2024. An estimated total of approximately 6,500 parents will be surveyed at least once throughout the duration of the study. SPDLC data will be released to the public two years after data is collected; Waves 1-4 are currently publicly available. Wave 5 will be publicly available in October 2025, with subsequent waves becoming available yearly. Data will be available to download in both SPSS (.sav) and Stata (.dta) formats, and the following data files will be available: (1) a data file for each individual wave, which contains responses from all participants in that wave of data collection, (2) a longitudinal panel data file, which contains longitudinal follow-up data from all available waves, and (3) a repeated cross-section data file, which contains the repeated cross-section data (from new respondents at each wave) from all available waves. Codebooks for each survey wave and a detailed user guide describing the data are also available.

United States Employment: NF: OS: Voluntary Health Organization data was reported at 39.100 Person th in Sep 2018. This records a decrease from the previous number of 39.400 Person th for Aug 2018. United States Employment: NF: OS: Voluntary Health Organization data is updated monthly, averaging 37.000 Person th from Jan 1990 (Median) to Sep 2018, with 345 observations. The data reached an all-time high of 40.800 Person th in Oct 2008 and a record low of 28.500 Person th in Sep 1990. United States Employment: NF: OS: Voluntary Health Organization data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G024: Current Employment Statistics Survey: Employment: Non Farm.

About this dataset

Context

The dataset tabulates the population of New Washington by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for New Washington. The dataset can be utilized to understand the population distribution of New Washington by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in New Washington. 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 New Washington.

Key observations

Largest age group (population): Male # 5-9 years (54) | Female # 25-29 years (63). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 New Washington population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the New Washington is shown in the following column.
• Population (Female): The female population in the New Washington 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 New Washington 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 New Washington Population by Gender. You can refer the same here

To provide estimates of employment, unemployment, and other characteristics of the general labor force, of the population as a whole, and of various subgroups of the population. Monthly labor force data for the country are used by the Bureau of Labor Statistics (BLS) to determine the distribution of funds under the Job Training Partnership Act. These data are collected through combined computer-assisted personal interviewing (CAPI) and computer-assisted telephone interviewing (CATI). In addition to the labor force data, the CPS basic funding provides annual data on work experience, income, and migration from the March Annual Demographic Supplement and on school enrollment of the population from the October Supplement. Other supplements, some of which are sponsored by other agencies, are conducted biennially or intermittently.

Release Date: 2024-09-26.Key Table Information:.The BDS data tables are compiled from the Longitudinal Business Database (LBD). The LBD is a longitudinal database of business establishments and firms with coverage starting in 1976. The LBD is constructed by linking annual snapshot files from the Census Bureau's Business Register (BR), and incorporating edits to BR data made by the County Business Patterns program. See: About This Program and BDS Methodology for complete information on the coverage, scope, and methodology of the Business Dynamics Statistics data series...Data Items and Other Identifying Records: .This file contains data classified by Firm age and Employment size of firms.Number of firms.Number of establishments.Number of employees.(DHS) denominator.Number of establishments born during the last 12 months.Rate of establishments born during the last 12 months.Number of establishments exited during the last 12 months.Rate of establishments exited during the last 12 months.Number of jobs created from expanding and opening establishments during the last 12 months.Number of jobs created from opening establishments during the last 12 months.Number of jobs created from expanding establishments during the last 12 months.Rate of jobs created from opening establishments during the last 12 months.Rate of jobs created from expanding and opening establishments during the last 12 months.Number of jobs lost from contracting and closing establishments during the last 12 months.Number of jobs lost from closing establishments during the last 12 months.Number of jobs lost from contracting establishments during the last 12 months.Rate of jobs lost from closing establishments during the last 12 months.Rate of jobs lost from contracting and closing establishments during the last 12 months.Number of net jobs created from expanding/contracting and opening/closing establishments during the last 12 months.Rate of net jobs created from expanding/contracting and opening/closing establishments during the last 12 months.Rate of reallocation during the last 12 months.Number of firms that exited during the last 12 months.Number of establishments associated with firm deaths during the last 12 months.Number of employees associated with firm deaths during the last 12 months...Geography Coverage:.The data are shown at the U.S. level...Industry Coverage:.The data are shown at the 2-digit NAICS level...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/bds/data/BDSFAGEFSIZE.zip..API Information:.Business Dynamics Statistics (BDS) data are housed in the Business Dynamics Statistics (BDS) API. For more information, see Business Dynamics Statistics (BDS) Data (census.gov)...Methodology:.In accordance with U.S. Code, Title 13, Section 9, no data are published that would disclose the operations of an individual employer. The BDS has adapted the disclosure avoidance method of the County Business Patterns (CBP) in using Hybrid Balanced Multiplicative Noise Infusion. CBP has been released with noise-infusion since 2007; see the CBP methodology webpage..In addition to noise infusion, cells with fewer than three firms are suppressed with a publication flag 'D'. In addition, cells with identified data quality concerns are suppressed with a publication flag 'S'. Cells that are "structurally missing" or "structurally zero" are indicated with a publication flag of 'X'. Finally, rate cells that cannot be calculated are indicated with a publication flag of 'N'..For more information about BDS methodology, see the BDS methodology pages...Source:.U.S. Census Bureau, 2022 Business Dynamics Statistics..Contact Information:.U.S. Census Bureau.Economy-Wide Statistics Division.Business Dynamics Statistics.Tel: (301) 763 - 6090 .Email: ewd.bds@census.gov

Graph and download economic data for Employed Persons in Westchester County, NY (LAUCN361190000000005) from Jan 1990 to Apr 2025 about Westchester County, NY; New York; NY; household survey; employment; persons; and USA.

Aeromagnetic data were collected along flight lines by instruments in an aircraft that recorded magnetic-field values and locations. This dataset presents latitude, longitude, altitude, and magnetic-field values.

"Social media" and "Search engines (e.g., Google)" are the top two answers among UK consumers in our survey on the subject of "Sources of inspiration for new products".The survey was conducted online among 4,751 respondents in the UK, in 2025.

The World Bank Group is interested in gauging the views of clients and partners who are either involved in development in Papua New Guinea or who observe activities related to social and economic development. The following survey will give the World Bank Group's team that works in Papua New Guinea, greater insight into how the Bank's work is perceived. This is one tool the World Bank Group uses to assess the views of its stakeholders, and to develop more effective strategies that support development in Papua New Guinea. A local independent consultant was hired to oversee the logistics of this survey. This survey was designed to achieve the following objectives: Assist the World Bank Group in gaining a better understanding of how stakeholders in Papua New Guinea (PNG) perceive the Bank Group; Obtain systematic feedback from stakeholders in Papua New Guinea regarding: · Their views regarding the general environment in Papua New Guinea; · Their overall attitudes toward the World Bank Group in Papua New Guinea; · Overall impressions of the World Bank Group's effectiveness and results, knowledge work and activities, and communication and information sharing in Papua New Guinea; · Perceptions of the World Bank Group's future role in Papua New Guinea. Use data to help inform Papua New Guinea country team's strategy.

This data collection is comprised of responses from two sets of survey questionnaires, the basic Current Population Survey (CPS) and a survey administered as a supplement to the January CPS questionnaire on the topic of displaced workers, employee tenure, and occupational mobility in the United States. The CPS, administered monthly, collects labor force data about the civilian noninstitutional population living in the United States. Moreover, the CPS provides current estimates of the economic status and activities of this population which includes estimates of total employment (both farm and nonfarm), nonfarm self-employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment. Data from the CPS are provided for the week prior to the administration of the survey. All persons eligible for the labor force items of the basic CPS were also eligible for the supplement. The supplement was designed to be a proxy response supplement, meaning a single respondent could provide answers for all eligible household members, provided the respondent was a household member 15 years of age or older. Persons 20 years of age and older, who lost or left a job in the last 3 years for selected reasons, were eligible for the first part of the supplement, which consists of the displaced workers items. Employed persons 15 years of age and older were eligible for the second part of the supplement, which consists of the employee tenure and occupational mobility items. Respondents were queried on involuntary job loss within the last three years based on operating decisions of a firm, plant, or business, reasons for job displacement, industry and occupation of the former job, group health insurance coverage, job tenure, and weekly earnings. Additional data refer to periods of unemployment as well as number of jobs held, use of unemployment benefits, whether residence was changed to seek work in another area, and current health insurance coverage. Although the main purpose of the survey was to collect information on an individual's employment situation, a very important secondary purpose was to collect information on demographic characteristics such as age, sex, race, Hispanic origin, marital status, veteran status, educational attainment, family relationship, occupation, and income.