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..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..Households not paying cash rent are excluded from the calculation of median gross rent..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.

All addresses in Self Response (TEA 1) and Update Leave (TEA 6) enumeration areas were invited to respond by internet, paper, or phone. The table is the cumulative self-response and return rates by mode as well as undeliverable as addressed (UAA) rates for the nation at the start of NRFU (August 10) and the end of response processing (December 1)..For more information about the different types of enumeration areas, go to the 2020 Census Type of Enumeration (TEA) viewer page by clicking here: Type of Enumeration Area..Self-response rates presented in this table may differ from those presented in the self-response map that was updated daily during the 2020 Census. The map used raw data as it was being processed in real-time while these rates used post processed data..To read the report that provides background information about the rate, go to the Evaluations, Experiments, and Assessment page on census.gov by clicking here: Evaluations Experiments and Assessments..Key Column Terms:.Start of NRFU – self-responses received by August 10.Final – self-responses received by the end of response processing (December 1).Internet – percentage of housing units providing a self-response by internet questionnaire.Paper – percentage of housing units providing a self-response by paper questionnaire.CQA – percentage of housing units providing a self-response by phone.Total – percentage of housing units providing a self-response by internet, paper, or phone.Self-Response Rate – percentage of addresses in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.Return Rate – percentage of occupied housing units in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.UAA Rate – percentage of addresses in Self Response areas (TEA 1) identified as undeliverable as addressed (UAA).NOTE: The Census Bureau's Disclosure Review Board and Disclosure Avoidance Officers have reviewed this information product for unauthorized disclosure of confidential information and have approved the disclosure avoidance practices applied to this release. (CBDRB-FY24-0271).Source: U.S. Census Bureau, 2020 Census

The 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File (2023-04-03) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File.

The NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the redistricting data portion of the 2010 Demonstration Data Products Suite – Redistricting and Demographic and Housing Characteristics File – Production Settings (2023-04-03). These statistical queries, called “noisy measurements” were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016] https://arxiv.org/abs/1605.02065; see also Dwork C. and Roth, A. [2014] https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] https://arxiv.org/abs/2004.00010), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Redistricting Data (P.L.94-171) Demonstration Noisy Measurement File (2023-04-03) has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004).

The data includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census.

The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints—information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) —are provided.

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail.The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts.The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate.More information about these data is available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review FAQs.Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data.Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR).1. Population Density: 2020 Population per square mile,2. Poverty Rate: Percentage of population under 100% FPL,3. Median Household income: Based on countywide median HH income of $71,538.4. Highschool Education Attainment: Percentage of 18 years and older population without high school graduation.5. English Speaking Ability: Percentage of 18 years and older population with less or none English speaking ability. 6. Household without Internet Access: Percentage of HH without internet access.7. Non-Hispanic White Population: Percentage of Non-Hispanic White population.8. Non-Hispanic African-American Population: Percentage of Non-Hispanic African-American population.9. Non-Hispanic Asian Population: Percentage of Non-Hispanic Asian population.10. Hispanic Population: Percentage of Hispanic population.

The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File (2023-06-30) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File. The NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the 2010 Demonstration Data Products Suite – Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File – Production Settings (2023-04-03). These statistical queries, called “noisy measurements” were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016] https://arxiv.org/abs/1605.02065; see also Dwork C. and Roth, A. [2014] https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] https://arxiv.org/abs/2004.00010), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File (2023-04-03) has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004).

The 2010 Census Production Settings Demographic and Housing Characteristics Demonstration Noisy Measurement File includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census.

The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints—information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) —are provided.

All addresses in Self Response enumeration areas (TEA 1) received one of two mailing strategies – Internet First or Internet Choice. This table is the daily and cumulative self-response and return rates by mode as well as undeliverable as addressed (UAA) rates for all addresses in areas receiving the Internet Choice mailing..For more information about the different types of enumeration areas, go to the 2020 Census Type of Enumeration (TEA) viewer page by clicking here: Type of Enumeration Area..Internet Choice mailings:.Mailing 1 – Letter and Questionnaire.Mailing 2 – Letter.Mailing 3 – Postcard.Mailing 4 – Letter and Questionnaire.Mailing 5 – “It’s Not Too Late Postcard”.Mailing 6 – Pre-NRFU COVID-19 Postcard.Mailing 7 – Letter and Questionnaire.Mailings 3-7 were targeted to nonrespondents.Mailings 6 and 7 were added during the census due to the COVID-19 pandemic.For more information about the impacts of the COVID-19 pandemic on the 2020 Census, click here: COVID-19 Decennial Census..Self-response rates presented in this table may differ from those presented in the self-response map that was updated daily during the 2020 Census. The map used raw data as it was being processed in real-time while these rates used post processed data..To read the report that provides background information about the rate, go to the Evaluations, Experiments, and Assessment page on census.gov by clicking here: Evaluations Experiments and Assessments..Key Column Terms:.Daily – percentage of housing units whose self-responses were received on a particular date.Cumulative – percentage of housing units whose self-responses were received from the start of the census through a particular date.Internet – percentage of housing units providing a self-response by internet questionnaire.Paper – percentage of housing units providing a self-response by paper questionnaire.CQA – percentage of housing units providing a self-response by phone.Total – percentage of housing units providing a self-response by internet, paper, or phone.Self-Response Rate – percentage of addresses in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.Return Rate – percentage of occupied housing units in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.UAA Rate – percentage of addresses in Self Response areas (TEA 1) identified as undeliverable as addressed (UAA).NOTE: The Census Bureau's Disclosure Review Board and Disclosure Avoidance Officers have reviewed this information product for unauthorized disclosure of confidential information and have approved the disclosure avoidance practices applied to this release. (CBDRB-FY24-0271).Source: U.S. Census Bureau, 2020 Census

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail. The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts. The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate. More information about these data are available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review our FAQs. Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data. Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR)..1. Population Density2. Poverty Rate3. Median Household income4. Education Attainment5. English Speaking Ability6. Household without Internet Access7. Non-Hispanic White Population8. Non-Hispanic African-American Population9. Non-Hispanic Asian Population10. Hispanic Population

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail. The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts. The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate. More information about these data are available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review our FAQs. Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data. Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR)..1. Population Density2. Poverty Rate3. Median Household income4. Education Attainment5. English Speaking Ability6. Household without Internet Access7. Non-Hispanic White Population8. Non-Hispanic African-American Population9. Non-Hispanic Asian Population10. Hispanic Population

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail. The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts. The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate. More information about these data are available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review our FAQs. Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data. Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR)..1. Population Density2. Poverty Rate3. Median Household income4. Education Attainment5. English Speaking Ability6. Household without Internet Access7. Non-Hispanic White Population8. Non-Hispanic African-American Population9. Non-Hispanic Asian Population10. Hispanic Population

All addresses in Self Response enumeration areas (TEA 1) received one of two mailing strategies – Internet First or Internet Choice. This table is the daily and cumulative self-response and return rates by mode as well as undeliverable as addressed (UAA) rates for all addresses in areas receiving the Internet Choice mailing..For more information about the different types of enumeration areas, go to the 2020 Census Type of Enumeration (TEA) viewer page by clicking here: Type of Enumeration Area..Internet Choice mailings:.Mailing 1 – Letter and Questionnaire.Mailing 2 – Letter.Mailing 3 – Postcard.Mailing 4 – Letter and Questionnaire.Mailing 5 – “It’s Not Too Late Postcard”.Mailing 6 – Pre-NRFU COVID-19 Postcard.Mailing 7 – Letter and Questionnaire.Mailings 3-7 were targeted to nonrespondents.Mailings 6 and 7 were added during the census due to the COVID-19 pandemic.For more information about the impacts of the COVID-19 pandemic on the 2020 Census, click here: COVID-19 Decennial Census..Self-response rates presented in this table may differ from those presented in the self-response map that was updated daily during the 2020 Census. The map used raw data as it was being processed in real-time while these rates used post processed data..To read the report that provides background information about the rate, go to the Evaluations, Experiments, and Assessment page on census.gov by clicking here: Evaluations Experiments and Assessments..Key Column Terms:.Daily – percentage of housing units whose self-responses were received on a particular date.Cumulative – percentage of housing units whose self-responses were received from the start of the census through a particular date.Internet – percentage of housing units providing a self-response by internet questionnaire.Paper – percentage of housing units providing a self-response by paper questionnaire.CQA – percentage of housing units providing a self-response by phone.Total – percentage of housing units providing a self-response by internet, paper, or phone.Self-Response Rate – percentage of addresses in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.Return Rate – percentage of occupied housing units in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.UAA Rate – percentage of addresses in Self Response areas (TEA 1) identified as undeliverable as addressed (UAA).NOTE: The Census Bureau's Disclosure Review Board and Disclosure Avoidance Officers have reviewed this information product for unauthorized disclosure of confidential information and have approved the disclosure avoidance practices applied to this release. (CBDRB-FY24-0271).Source: U.S. Census Bureau, 2020 Census

This data is pulled from the U.S. Census website. This data is for years Calendar Years 2009-2014. Product: SAHIE File Layout Overview Small Area Health Insurance Estimates Program - SAHIE Filenames: SAHIE Text and SAHIE CSV files 2009 – 2014 Source: Small Area Health Insurance Estimates Program, U.S. Census Bureau. Internet Release Date: May 2016 Description: Model‐based Small Area Health Insurance Estimates (SAHIE) for Counties and States File Layout and Definitions

The Small Area Health Insurance Estimates (SAHIE) program was created to develop model-based estimates of health insurance coverage for counties and states. This program builds on the work of the Small Area Income and Poverty Estimates (SAIPE) program. SAHIE is only source of single-year health insurance coverage estimates for all U.S. counties.

For 2008-2014, SAHIE publishes STATE and COUNTY estimates of population with and without health insurance coverage, along with measures of uncertainty, for the full cross-classification of: •5 age categories: 0-64, 18-64, 21-64, 40-64, and 50-64

•3 sex categories: both sexes, male, and female

•6 income categories: all incomes, as well as income-to-poverty ratio (IPR) categories 0-138%, 0-200%, 0-250%, 0-400%, and 138-400% of the poverty threshold

•4 races/ethnicities (for states only): all races/ethnicities, White not Hispanic, Black not Hispanic, and Hispanic (any race).

In addition, estimates for age category 0-18 by the income categories listed above are published.

Each year’s estimates are adjusted so that, before rounding, the county estimates sum to their respective state totals and for key demographics the state estimates sum to the national ACS numbers insured and uninsured.

This program is partially funded by the Centers for Disease Control and Prevention's (CDC), National Breast and Cervical Cancer Early Detection ProgramLink to a non-federal Web site (NBCCEDP). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the NBCCEDP. Most state NBCCEDP programs define low-income as 200 or 250 percent of the poverty threshold. Also included are IPR categories relevant to the Affordable Care Act (ACA). In 2014, the ACA will help families gain access to health care by allowing Medicaid to cover families with incomes less than or equal to 138 percent of the poverty line. Families with incomes above the level needed to qualify for Medicaid, but less than or equal to 400 percent of the poverty line can receive tax credits that will help them pay for health coverage in the new health insurance exchanges.

We welcome your feedback as we continue to research and improve our estimation methods. The SAHIE program's age model methodology and estimates have undergone internal U.S. Census Bureau review as well as external review. See the SAHIE Methodological Review page for more details and a summary of the comments and our response.

The SAHIE program models health insurance coverage by combining survey data from several sources, including: •The American Community Survey (ACS) •Demographic population estimates •Aggregated federal tax returns •Participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program •County Business Patterns •Medicaid •Children's Health Insurance Program (CHIP) participation records •Census 2010

Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level.

Abstract

The survey was one of three components of a World Bank project implemented to provide information on the size and composition of the civil service, improve systems and control mechanisms, institutional capacity, and provide information on policy-formulation and decision-making processes. Other components included a census of Guatemalan civil servants and contractors, and the continuous updating and use of this information to strengthen checks and improve transparency, and a new policy framework aimed at strengthening the institutional capacity of the Guatemalan civil service.

The aim of the survey was to assess the characteristics and quality of human resource management in the public administration, as well as to capture the attitudes, motivations, and experiences of public officials. In particular, the survey focused on the priority areas for reform identified by the Government of Guatemala and the World Bank. The data collected was used to support the World Bank’s diagnostic of key problem areas in the human resource management of the public administration in Guatemala. It was used to inform the design of institution-level interventions, as well as the new public policy framework.

Geographic coverage

The target population were civil servants across 18 institutions in Guatemala at the central, and their respective departmental and municipal branches.

Analysis unit

Public servants (managers and non-managers) across 18 institutions in Guatemala at the central, and their respective departmental and municipal branches.

Kind of data

Aggregate data [agg]

Sampling procedure

The sample frame used comes from the frame used for the Human Resources National Census. It has the list of positions in all the units of the 18 institutions selected for this study. The sample size for the managerial level was calculated with a 95% confidence level and a 5% margin error for each institution. For the non-managers, it was calculated with the same confidence level and margin error. The sample sizes are adjusted so the sample would have an even number for each study domain for the experiment which will assign a different questionnaire to half of the respondents.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey questionnaire comprises following modules: 1- Pre-interview questions, 2- Demographic and work history information, 3- Management practices, 4- Performance evaluation, 5- perceptions about discrimination, 6- Human resources management practices, 7- Perceptions of the national office of the civil service, 8- Perception of acts of corruption, and 9- Review of surveys.

The questionnaire was prepared in English and Spanish.

Response rate

Response rate was 96%.

All addresses in Self Response (TEA 1) and Update Leave (TEA 6) enumeration areas were invited to respond by internet, paper, or phone. The table is the county-level self-response and return rates by mode as well as undeliverable as addressed (UAA) rates for the nation at the start of NRFU (August 10, 2020) and the end of response processing (December 1, 2020). Self-response, return, and UAA rates from the 2010 Census at the NRFU cut date (April 19, 2010) and the end of response processing (September 7, 2010) are included to compare rates between censuses..For more information about the different types of enumeration areas, go to the 2020 Census Type of Enumeration (TEA) viewer page by clicking here: Type of Enumeration Area..Self-response rates presented in this table may differ from those presented in the self-response map that was updated daily during the 2020 Census. The map used raw data as it was being processed in real-time while these rates used post processed data..To read the report that provides background information about the rate, go to the Evaluations, Experiments, and Assessment page on census.gov by clicking here: Evaluations Experiments and Assessments..Key Column Terms:.NRFU (2020) – self-responses received by August 10, 2020.NRFU (2010) – self-responses received by April 19, 2010.Final (2020) – self-responses received by the end of response processing (December 1, 2020).Final (2010) – self-responses received by the end of response processing (September 7, 2010).Internet – percentage of housing units providing a self-response by internet questionnaire.Paper – percentage of housing units providing a self-response by paper questionnaire.CQA – percentage of housing units providing a self-response by phone.Total – percentage of housing units providing a self-response by internet, paper, or phone.Self-Response Rate – percentage of addresses in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.Return Rate – percentage of occupied housing units in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.UAA Rate – percentage of addresses in Self Response areas (TEA 1) identified as undeliverable as addressed (UAA).NOTE: The Census Bureau's Disclosure Review Board and Disclosure Avoidance Officers have reviewed this information product for unauthorized disclosure of confidential information and have approved the disclosure avoidance practices applied to this release. (CBDRB-FY24-0271).Source: U.S. Census Bureau, 2020 Census

About this dataset

Context

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

Key observations

Largest age group (population): Male # 65-69 years (219) | Female # 80-84 years (161). 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 Real County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Real County is shown in the following column.
• Population (Female): The female population in the Real County 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 Real County 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 Real County Population by Gender. You can refer the same here

Abstract

The 2000 Republic of Palau Census of Population and Housing was the second census collected and processed entirely by the republic itself. This monograph provides analyses of data from the most recent census of Palau for decision makers in the United States and Palau to understand current socioeconomic conditions. The 2005 Census of Population and Housing collected a wide range of information on the characteristics of the population including demographics, educational attainments, employment status, fertility, housing characteristics, housing characteristics and many others.

Geographic coverage

National

Analysis unit

• Household;
• Individual.

Universe

The 1990, 1995 and 2000 censuses were all modified de jure censuses, counting people and recording selected characteristics of each individual according to his or her usual place of residence as of census day. Data were collected for each enumeration district - the households and population in each enumerator assignment - and these enumeration districts were then collected into hamlets in Koror, and the 16 States of Palau.

Kind of data

Census/enumeration data [cen]

Sampling procedure

No sampling - whole universe covered

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000 censuses of Palau employed a modified list-enumerate procedure, also known as door-to-door enumeration. Beginning in mid-April 2000, enumerators began visiting each housing unit and conducted personal interviews, recording the information collected on the single questionnaire that contained all census questions. Follow-up enumerators visited all addresses for which questionnaires were missing to obtain the information required for the census.

Cleaning operations

The completed questionnaires were checked for completeness and consistency of responses, and then brought to OPS for processing. After checking in the questionnaires, OPS staff coded write-in responses (e.g., ethnicity or race, relationship, language). Then data entry clerks keyed all the questionnaire responses. The OPS brought the keyed data to the U.S. Census Bureau headquarters near Washington, DC, where OPS and Bureau staff edited the data using the Consistency and Correction (CONCOR) software package prior to generating tabulations using the Census Tabulation System (CENTS) package. Both packages were developed at the Census Bureau's International Programs Center (IPC) as part of the Integrated Microcomputer Processing System (IMPS).

The goal of census data processing is to produce a set of data that described the population as clearly and accurately as possible. To meet this objective, crew leaders reviewed and edited questionnaires during field data collection to ensure consistency, completeness, and acceptability. Census clerks also reviewed questionnaires for omissions, certain inconsistencies, and population coverage. Census personnel conducted a telephone or personal visit follow-up to obtain missing information. The follow-ups considered potential coverage errors as well as questionnaires with omissions or inconsistencies beyond the completeness and quality tolerances specified in the review procedures.

Following field operations, census staff assigned remaining incomplete information and corrected inconsistent information on the questionnaires using imputation procedures during the final automated edit of the data. The use of allocations, or computer assignments of acceptable data, occurred most often when an entry for a given item was lacking or when the information reported for a person or housing unit on an item was inconsistent with other information for that same person or housing unit. In all of Palau’s censuses, the general procedure for changing unacceptable entries was to assign an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. The assignment of acceptable data in place of blanks or unacceptable entries enhanced the usefulness of the data.

Sampling error estimates

Human and machine-related errors occur in any large-scale statistical operation. Researchers generally refer to these problems as non-sampling errors. These errors include the failure to enumerate every household or every person in a population, failure to obtain all required information from residents, collection of incorrect or inconsistent information, and incorrect recording of information. In addition, errors can occur during the field review of the enumerators' work, during clerical handling of the census questionnaires, or during the electronic processing of the questionnaires. To reduce various types of non-sampling errors, Census office personnel used several techniques during planning, data collection, and data processing activities. Quality assurance methods were used throughout the data collection and processing phases of the census to improve the quality of the data.

Census staff implemented several coverage improvement programs during the development of census enumeration and processing strategies to minimize under-coverage of the population and housing units. A quality assurance program improved coverage in each census. Telephone and personal visit follow-ups also helped improve coverage. Computer and clerical edits emphasized improving the quality and consistency of the data. Local officials participated in post-census local reviews. Census enumerators conducted additional re-canvassing where appropriate.

All addresses in Self Response (TEA 1) and Update Leave (TEA 6) enumeration areas were invited to respond by internet, paper, or phone. This table is the daily and cumulative self-response and return rates by mode as well as undeliverable as addressed (UAA) rates for the nation..Self-response rates presented in this table may differ from those presented in the self-response map that was updated daily during the 2020 Census. The map used raw data as it was being processed in real-time while these rates used post processed data..To read the report that provides background information about the rate, go to the Evaluations, Experiments, and Assessment page on census.gov by clicking here: Evaluations Experiments and Assessments..For more information about the different types of enumeration areas, go to the 2020 Census Type of Enumeration (TEA) viewer page by clicking here: Type of Enumeration Area..Key Column Terms:.Daily – percentage of housing units whose self-responses were received on a particular date.Cumulative – percentage of housing units whose self-responses were received from the start of the census through a particular date.Internet – percentage of housing units providing a self-response by internet questionnaire.Paper – percentage of housing units providing a self-response by paper questionnaire.CQA – percentage of housing units providing a self-response by phone.Total – percentage of housing units providing a self-response by internet, paper, or phone.Self-Response Rate – percentage of addresses in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.Return Rate – percentage of occupied housing units in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.UAA Rate – percentage of addresses in Self Response areas (TEA 1) identified as undeliverable as addressed (UAA).NOTE: The Census Bureau's Disclosure Review Board and Disclosure Avoidance Officers have reviewed this information product for unauthorized disclosure of confidential information and have approved the disclosure avoidance practices applied to this release. (CBDRB-FY24-0271).Source: U.S. Census Bureau, 2020 Census

This dataset shows the number of workers in 25 categories of occupation. The information is mapped according to place of residence. The data is part of the Census Transportation Planning Package (CTPP), and is the result of a cooperative effort between various groups including the State Departments of Transportation, U.S. Census Bureau, and the Federal Highway Administration. The data is a special tabulation of responses from households completing the decennial census long form. The data was collected in 2000 and is shown at tract level.

Release Date: 2021-04-22.Release Schedule:.The data in this file come from the 2017 Economic Census. For information about economic census planned data product releases, see Economic Census: About: 2017 Release Schedules...Key Table Information:.Includes only establishments of firms with payroll...Data Items and Other Identifying Records:.Number of establishments.Sales, value of shipments, or revenue ($1,000).Annual payroll ($1,000).Number of employees.Operating expenses ($1,000).Response coverage of ownership and control of government hospitals inquiry (%)..Each record includes a code which represents a specific type of ownership and control of government hospitals category...Geography Coverage:.The data are shown for employer establishments at the U.S. level only. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies...Industry Coverage:.The data are shown for 2017 NAICS codes 6221101, 6222101, and 6223101. For information about NAICS, see Economic Census: Technical Documentation: Economic Census Code Lists...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector62/EC1762HOSP.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Contact Us.

This dataset shows the incomes of people reporting from their job location. The data is part of the Census Transportation Planning Package (CTPP), and is the result of a cooperative effort between various groups including the State Departments of Transportation, U.S. Census Bureau, and the Federal Highway Administration. The data is a special tabulation of responses from households completing the decennial census long form. The data was collected in 2000 and is shown at tract level.

AHA Annual Survey Database™ for Fiscal Year 2018 is a comprehensive hospital database for peer review benchmarking, market analysis, and health services research. It is produced primarily from the AHA Annual Survey of Hospitals, which has been administered by the American Hospital Association (AHA) since 1946. The survey responses are supplemented by data drawn the U.S. Census Bureau, hospital accrediting bodies, and other organizations.