This statistic displays the most popular payment methods for online purchases in the United Kingdom (UK) in 2020. During the survey period in 2020, it was found that 51 percent of respondents preferred to pay via credit or debit card when they shopped online. Direct payment through bank was unpopular: one percent of respondents chose this as their preferred method of payment.

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.

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.

This collection provides data on labor force activity for the week prior to the survey. Comprehensive data are available on the employment status, occupation, and industry of persons 14 years old and over. Personal characteristics such as age, sex, race, marital status, veteran status, household relationship, educational background, and Spanish origin are also included in the file. Supplemental statistics are supplied on birth history and birth expectations for women 14-59 years of age. Data include total number of children ever born, date of birth of most recent child, and date of first marriage. Currently married women 14-39 years of age were asked about the number of additional children they expected to have within the next five years. Some demographic information is also provided on husbands of the women interviewed.

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.

According to 72 percent of the gender-identified women in the survey, they have worked at a tech company where "bro culture" is pervasive as of 2021. On the other hand. only 41 percent of the men surveyed within the tech industry say the same. This indicates an overwhelming discrepancy in perception between the genders.

AHE: sa: PW: EH: Child & Youth Services data was reported at 24.850 USD in Mar 2025. This records an increase from the previous number of 24.750 USD for Feb 2025. AHE: sa: PW: EH: Child & Youth Services data is updated monthly, averaging 15.010 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 24.850 USD in Mar 2025 and a record low of 8.520 USD in Jan 1990. AHE: sa: PW: EH: Child & Youth Services data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

Abstract

This research was conducted in Lithuania from June 19 to July 31, 2002, as part of the second round of the Business Environment and Enterprise Performance Survey. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through face-to-face interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The survey topics include company's characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement, security, government policies and regulations, bribery, sources of financing, overall business environment, performance and investment activities, and workforce composition.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment.

Universe

The manufacturing and services sectors are the primary business sectors of interest.

Kind of data

Sample survey data [ssd]

Sampling procedure

The information below is taken from "The Business Environment and Enterprise Performance Survey - 2002. A brief report on observations, experiences and methodology from the survey" prepared by MEMRB Custom Research Worldwide (now part of Synovate), a research company that implemented BEEPS II instrument.

The general targeted distributional criteria of the sample in BEEPS II countries were to be as follows:

1) Coverage of countries: The BEEPS II instrument was to be administered to approximately 6,500 enterprises in 28 transition economies: 16 from CEE (Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, FR Yugoslavia, FYROM, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, Slovenia and Turkey) and 12 from the CIS (Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan).

2) In each country, the sector composition of the total sample in terms of manufacturing versus services (including commerce) was to be determined by the relative contribution of GDP, subject to a 15% minimum for each category. Firms that operated in sectors subject to government price regulations and prudential supervision, such as banking, electric power, rail transport, and water and wastewater were excluded.

Eligible enterprise activities were as follows (ISIC sections): - Mining and quarrying (Section C: 10-14), Construction (Section F: 45), Manufacturing (Section D: 15-37) - Transportation, storage and communications (Section I: 60-64), Wholesale, retail, repairs (Section G: 50-52), Real estate, business services (Section K: 70-74), Hotels and restaurants (Section H: 55), Other community, social and personal activities (Section O: selected groups).

3) Size: At least 10% of the sample was to be in the small and 10% in the large size categories. A small firm was defined as an establishment with 2-49 employees, medium - with 50-249 workers, and large - with 250 - 9,999 employees. Companies with only one employee or more than 10,000 employees were excluded.

4) Ownership: At least 10% of the firms were to have foreign control (more than 50% shareholding) and 10% of companies - state control.

5) Exporters: At least 10% of the firms were to be exporters. A firm should be regarded as an exporter if it exported 20% or more of its total sales.

6) Location: At least 10% of firms were to be in the category "small city/countryside" (population under 50,000).

7) Year of establishment: Enterprises which were established later than 2000 should be excluded.

The sample structure for BEEPS II was designed to be as representative (self-weighted) as possible to the population of firms within the industry and service sectors subject to the various minimum quotas for the total sample. This approach ensured that there was sufficient weight in the tails of the distribution of firms by the various relevant controlled parameters (sector, size, location and ownership).

As pertinent data on the actual population or data which would have allowed the estimation of the population of foreign-owned and exporting enterprises were not available, it was not feasible to build these two parameters into the design of the sample guidelines from the onset. The primary parameters used for the design of the sample were: - Total population of enterprises; - Ownership: private and state; - Size of enterprise: Small, medium and large; - Geographic location: Capital, over 1 million, 1 million-250,000, 250-50,000 and under 50,000; - Sub-sectors (e.g. mining, construction, wholesale, etc).

For certain parameters where statistical information was not available, enterprise populations and distributions were estimated from other accessible demographic (e.g. human population concentrations in rural and urban areas) and socio-economic (e.g. employment levels) data.

Sampling deviation

The survey was discontinued in Turkmenistan due to concerns about Turkmen government interference with implementation of the study.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Screener and Main Questionnaires.

The survey topics include company's characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement, security, government policies and regulations, bribery, sources of financing, overall business environment, performance and investment activities, and workforce composition.

Cleaning operations

Data entry and first checking and validation of the results were undertaken locally. Final checking and validation of the results were made at MEMRB Custom Research Worldwide headquarters.

Response rate

Overall, in all BEEPS II countries, the implementing agency contacted 18,052 enterprises and achieved an interview completion rate of 36.93%.

Respondents who either refused outright (i.e. not interested) or were unavailable to be interviewed (i.e. on holiday, etc) accounted for 38.34% of all contacts. Enterprises which were contacted but were non-eligible (i.e. business activity, year of establishment, etc) or quotas were already met (i.e. size, ownership etc) or to which “blind calls” were made to meet quotas (i.e. foreign ownership, exporters, etc) accounted for 24.73% of the total number of enterprises contacted.

109-110 Level 1 Land Utilization Current Survey Results (County and City Version): The statistical content includes 22 counties and cities in Taiwan, and the first level 9 major categories of land use current survey results.

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.

This statistic shows the results of a survey conducted in the United States in 2019 on the importance of romance in a relationship. Some 40 percent of respondents stated that romance was essential to them in a relationship because they could not feel love without it.

U.S. Census Bureau QuickFacts statistics for Princeton, New Jersey. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

The Active People Survey commenced in October 2005 and was commissioned by Sports England. The primary objective of the survey was to measure levels of participation in sport and active recreation and its contribution to improving the health of the nation. Sport and active recreation included walking and cycling for recreation in addition to more traditional formal and informal spots. When measuring sports participation the survey not only recorded the type of activity but also the frequency, intensity and duration of the activity.

The Active People Survey was replaced by the Active Lives Survey in November 2015. Active Lives is a new survey with a different methodology and intended to measure different outcomes from those in the Active People Survey, however there are similarities as it was important that data could be reproduced on some of the key measures.

More general information can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables.


For the second edition (October 2015), the data file was replaced with a new version to reflect changes in the Sport England core measure, the ‘1x30’ indicator. The documentation has also been updated.

The Listening to Young Lives at Work: COVID-19 Phone Survey, Fourth and Fifth Call, 2021 is an adapted version of the Round 6 survey with additional questions to directly assess the impact of COVID-19. The 2021 survey consists of two phone calls (Fourth Call and Fifth Call) with each of our Young Lives respondents, across both the younger and older cohorts, and in all four study countries (reaching an estimated total of around 11,000 young people). The Phone Survey will enable Young Lives to inform policy makers on the short-term effects of the COVID-19 pandemic. Subsequently, and together with data collected in further survey rounds, Young Lives will be able to assess the medium and long term implications of the crisis. Further information is available on the Young Lives at Work webpage.

The Listening to Young Lives at Work: COVID-19 Phone Survey, First Call, Second Call and Third Call, 2020 is held at the UK Data Archive under SN 8678 and the Listening to Young Lives at Work: COVID-19 Phone Survey Calls 1-5 Constructed Files, 2020-2021 is held under SN 9070.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..This table provides geographical mobility for persons relative to their residence at the time they were surveyed. The characteristics crossed by geographical mobility reflect the current survey year..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Note: The Board of Governors has discontinued the Survey of Terms of Business Lending (STBL) and the associated E.2 release. The final STBL was conducted in May 2017, and the final E.2 was released on August 2, 2017. The STBL has been replaced by a new Small Business Lending Survey that commenced in February 2018. The new survey is being managed and administered by the Federal Reserve Bank of Kansas City. Results from this new survey can be found here.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..This table provides geographical mobility for persons relative to their residence at the time they were surveyed. The characteristics crossed by geographical mobility reflect the current survey year..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Graph and download economic data for Civilian Labor Force in New Orleans-Metairie, LA (MSA) (LAUMT223538000000006A) from 1990 to 2024 about New Orleans, LA, civilian, labor force, labor, household survey, and USA.

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

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..This table provides geographical mobility for persons relative to their residence at the time they were surveyed. The characteristics crossed by geographical mobility reflect the current survey year..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.