The data (name, year of birth, sex, and number) are from a 100 percent sample of Social Security card applications for 1880 on.

Context

Cultural diversity in the U.S. has led to great variations in names and naming traditions and names have been used to express creativity, personality, cultural identity, and values. Source: https://en.wikipedia.org/wiki/Naming_in_the_United_States

Content

This public dataset was created by the Social Security Administration and contains all names from Social Security card applications for births that occurred in the United States after 1879. Note that many people born before 1937 never applied for a Social Security card, so their names are not included in this data. For others who did apply, records may not show the place of birth, and again their names are not included in the data.

All data are from a 100% sample of records on Social Security card applications as of the end of February 2015. To safeguard privacy, the Social Security Administration restricts names to those with at least 5 occurrences.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:usa_names

https://cloud.google.com/bigquery/public-data/usa-names

Dataset Source: Data.gov. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @dcp from Unplash.

Inspiration

What are the most common names?

What are the most common female names?

Are there more female or male names?

Female names by a wide margin?

This annual report provides program and demographic information on the people who receive Social Security Disability Insurance Program benefits. This edition presents a series of detailed tables on the three categories of beneficiaries disabled workers, disabled widowers, and disabled adult children. Numbers presented in these tables may differ slightly from other published statistics because all tables, except those using data from the Survey of Income and Program Participation, are based on 100 percent data files.

Annual report providing program and demographic information about the people receiving disability insurance benefits under the OASDI program. Report for 2016.

This public dataset was created by the Social Security Administration and contains all names from Social Security card applications for births that occurred in the United States after 1879. Note that many people born before 1937 never applied for a Social Security card, so their names are not included in this data. For others who did apply, records may not show the place of birth, and again their names are not included in the data. All data are from a 100% sample of records on Social Security card applications as of the end of February 2015. To safeguard privacy, the Social Security Administration restricts names to those with at least 5 occurrences. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

"The Social Security Administration (SSA) suggested to USC to survey members of the public around these topics: What do people know about Social Security? How do people learn about Social Security and how do they want to learn about Social Security? How do adults use financial products as they age? How do adults make their financial decisions and where do they turn for advice? What are adults' main sources of financial stress? The results of the survey are available at the USC website below after logging in and being granted access by USC."

The NSCF collected data on the health status and functional limitations, health care utilization, health insurance coverage, receipt of services, SSI experience, socioeconomic status of children's households, and housing characteristics of over 8,000 children who were receiving, had received, or were applying for SSI. The study is limited to the non-institutionalized population in the continental United States (i.e., it does not include residents of Alaska, Hawaii, and US territories). Data collection began in July 2001 and ended in July 2002.

In 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw decrease. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

This data collection contains detailed county and state-level ecological and descriptive data for the United States for the years 1790 to 2002. Parts 1-43 are an update to HISTORICAL, DEMOGRAPHIC, ECONOMIC, AND SOCIAL DATA: THE UNITED STATES, 1790-1970 (ICPSR 0003). Parts 1-41 contain data from the 1790-1970 censuses. They include extensive information about the social and political character of the United States, including a breakdown of population by state, race, nationality, number of families, size of the family, births, deaths, marriages, occupation, religion, and general economic condition. Parts 42 and 43 contain data from the 1840 and 1870 Censuses of Manufacturing, respectively. These files include information about the number of persons employed in various industries and the quantities of different types of manufactured products. Parts 44-50 provide county-level data from the United States Census of Agriculture for 1840 to 1900. They also include the state and national totals for the variables. The files provide data about the number, types, and prices of various agricultural products. Parts 51-57 contain data on religious bodies and church membership for 1906, 1916, 1926, 1936, and 1952, respectively. Parts 58-69 consist of data from the CITY DATA BOOKS for 1944, 1948, 1952, 1956, 1962, 1967, 1972, 1977, 1983, 1988, 1994, and 2000, respectively. These files contain information about population, climate, housing units, hotels, birth and death rates, school enrollment and education expenditures, employment in various industries, and city government finances. Parts 70-81 consist of data from the COUNTY DATA BOOKS for 1947, 1949, 1952, 1956, 1962, 1967, 1972, 1977, 1983, 1988, 1994, and 2000, respectively. These files include information about population, employment, housing, agriculture, manufacturing, retail, services, trade, banking, Social Security, local governments, school enrollment, hospitals, crime, and income. Parts 82-84 contain data from USA COUNTIES 1998. Due to the large number of variables from this source, the data were divided into into three separate data files. Data include information on population, vital statistics, school enrollment, educational attainment, Social Security, labor force, personal income, poverty, housing, trade, farms, ancestry, commercial banks, and transfer payments. Parts 85-106 provide data from the United States Census of Agriculture for 1910 to 2002. They provide data about the amount, types, and prices of various agricultural products. Also, these datasets contain extensive information on the amount, expenses, sales, values, and production of farms and machinery. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR02896.v3. We highly recommend using the ICPSR version, as they made this dataset available in multiple data formats and updated the data through 2002.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Description:This data deposit contains the Numerical Identification Death Files (National Archives Identifier 23845618), the NUMIDENT SS-5 Application Files (National Archives Identifier 23845613), the NUMIDENT Claims Files (National Archives Identifier 23852747), and the associated technical documentation. Data Acquisition:These files were e-delivered to Anthony Wray via secure link by the Electronic Records Division of the National Archives and Records Administration (NARA) on 17 October 2019, as per a digitized reproduction order (Quote QO1-525370500 and Quote QO1-528389077). The packing slip is included in the data deposit (docs/Packing Slip.PDF).Rights to Publish:The data are in the public domain, as confirmed by emails received from NARA on 28 December 2023 and 3 January 2024 (see docs/permission_to_publish_email.pdf).How to Cite: Please adhere to the citation and data usage guidelines when using this dataset. See the included LICENSE.txt and README.md files for details. Details:The Numerical Identification Files (NUMIDENT), 1936–2007, series contains records for every Social Security number (SSN) assigned to individuals with a verified death or who would have been over 110 years old by December 31, 2007. There are three types of entries in NUMIDENT: application (SS-5), claim, and death records. A NUMIDENT record may contain more than one entry. Information contained in NUMIDENT records includes: each applicant's full name, SSN, date of birth, place of birth, citizenship, sex, father's name, mother's maiden name, and race/ethnic description (optional). NUMIDENT includes information regarding any subsequent changes made to the applicant's record, including name changes and life or death claims. The death records in NUMIDENT do not include any State reported deaths in accordance with the Social Security Act section 205(r). There are 72,182,729 SS-5 records entries; 25,230,486 claim record entries; and 49,459,293 death record entries.See https://catalog.archives.gov/id/12004494 for more information.Related Data:Visit the CenSoc Project for public micro datasets linked to NUMIDENT: https://censoc.berkeley.edu/.

This dataset includes the number of people enrolled in DSS services by town and by race from CY 2015-2024. To view the full dataset and filter the data, click the "View Data" button at the top right of the screen. More data on people served by DSS can be found here. About this data For privacy considerations, a count of zero is used for counts less than five. A recipient is counted in all towns where that recipient resided in that year. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. Notes by year 2021 In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. 2018 On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. On February 14, 2019 the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged. On January 16, 2019 these counts were revised to count a recipient in all locations that recipient resided in that year. On January 1, 2019 the counts were revised to count a recipient in only one town per year even when the recipient moved within the year. The most recent address is used.

US Births - How Common is Your Birthday?

How popular is your birthday?

By Andy Kriebel [source]

About this dataset

The file contains data on births in the United States from 1994 to 2014. The data includes the following columns: year: The year of the observation. (Integer) month: The month of the observation. (Integer) date_of_month: The date of the observation. (Integer) day_of_week: The day of the week of the observation. (Integer) births: The number of births on the given day. (Integer)

How to use the dataset

The US Births dataset on Kaggle contains data on births in the United States from 1994 to 2014. The data is broken down by year, month, date of month, day of week, and births.

This dataset can be used to answer questions about when people are born, how common certain birthdays are, and any trends over time. For example, you could use this dataset to find out which day of the week has the most births or which month has the most births

Research Ideas

• Determining which day of the year and what time of day that people are mostly born to help with staffing levels in maternity wards
• Identifying trends in baby names over time
• Predicting the number of births on a given day

Acknowledgements

This data set is a combined effort of the U.S. National Center for Health Statistics and the U.S. Social Security Administration, provided by FiveThirtyEight. It contains data on births in the United States from 1994 to 2014, with the following columns: year, month, date_of_month, day_of_week, births

->Thank you to FiveThirtyEight for providing this dataset!

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: US_births_1994-2014.csv | Column name | Description | |:------------------|:---------------------------------------------| | year | Year of the data. (Integer) | | month | Month of the data. (Integer) | | date_of_month | Day of the month of the data. (Integer) | | day_of_week | Day of the week of the data. (Integer) | | births | Number of births on the given day. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit Andy Kriebel.

• Context I am greatly inspired with this dataset containing geo spatial details for each zip code and contains the total wages for each area.This gave me opportunity to create a data visualisation in Tableau using HexBin chart which is added as a Kernel to this dataset.

• Content

  • About the data: 81,831 rows of data. All 41,891 active zipcodes + 634 decommisioned zipcodes from the recent past. All 80673 active Primary(41885), Acceptable(13988), and Not Acceptable(24800) placenames. Some additonal placenames for decommisioned codes. 29,971 Standard, 9465 PO BOX, 2437 Unique, and 649 Military codes.

• 50 States + 361 AA Military

• Americas 38 AE Military

• Europe 164 AP Military

• Pacific 1 AS American Samoa 290 DC Washinton DC 4 FM Federated States Micronesia 13 GU Guam 2 MH Marshall Islands 3 MP Northern Mariana Islands 176 PR Puerto Rico 2 PW Palau 16 VI Virgin Islands

• Name Type Description

• Zipcode Text 5 digit Zipcode or military postal code(FPO/APO)

• ZipCodeType Text Standard, PO BOX Only, Unique, Military(implies APO or FPO)

• City Text USPS offical city name(s)

• State Text USPS offical state, territory, or quasi-state (AA, AE, AP) abbreviation code

• LocationType Text Primary, Acceptable,Not Acceptable

• Lat Double Decimal Latitude, if available

• Long Double Decimal Longitude, if available

• Location Text Standard Display (eg Phoenix, AZ ; Pago Pago, AS ; Melbourne, AU )

• Decommissioned Text If Primary location, Yes implies historical Zipcode, No Implies current Zipcode; If not Primary, Yes implies Historical Placename

• TaxReturnsFiled Long Integer Number of Individual Tax Returns Filed in 2008

• EstimatedPopulation Long Integer Tax returns filed + Married filing jointly + Dependents

• TotalWages Long Integer Total of Wages Salaries and Tips

  • Source: http://federalgovernmentzipcodes.us/

  • Current zipcodes, placenames, zipcode type(Standard, PO, Unique, Military), placename type (Primary, Acceptable, Not Acceptable)

• : USPS Military place names (base or ship name)

• : MPSA 2008 Election Ballot information Tax returns filed, estimated population, total wages: IRS 2008 Latitude and Longitude; National Weather Service supplemented by Google Earth and Maps and occasionally other sources Decommissioned zip codes, Our old database--usually quality sources, but not verifiable.

• Acknowledgements

  Other Sources of zipcode information:

• Placenames (Cities, towns, geographic features) can be found at US Geological Survey GNIS Dataset The IRS has additional data fields for 2008 and is reviewing their publication procedures for later years.

  see http://www.irs.gov/taxstats/indtaxstats/article/0,,id=96947,00.html

• The Census publishes data, but they use Zipcode Tabulation Areas (ZCTAs) which

• 1) have changed areas between the 2000 census and the 2010 census

• 2) do not map well to USPS zipcodes well. If needed http://www.census.gov/geo/ZCTA/zcta.html Social Security recipients by zipcode http://www.ssa.gov/policy/docs/statcomps/oasdi_zip/ For economic researchers and those who want tons of background on data sources by zipcode, University of Missouri OSEDA project

  • Free Zipcode Database (8.7 MB) Updated 1/22/2012 All Locations (Multiple locations for some zipcodes)

  Inspiration I am hoping that people in the right government agencies/authorities would be able to utilize this data to focus on

  community developments where it needs immediate attention.

Key Table Information.Table Title.Social Security Income in the Past 12 Months for Households.Table ID.ACSDT1Y2024.B19055.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.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.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..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.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.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..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.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, citi...

Judgement on economic and social conditions in the USA in comparison to the FRG. Topics: Development of personal economic conditions and the standard of living in the FRG; reasons for the so-called economic miracle and share of the USA in the economic recovery; perceived linking of German economic development with other countries; attitude to a European Common Market; reasons for the high American standard of living; comparison between the USA and the FRG regarding working conditions, productivity, social security and job security of workers; image of Americans; knowledge of economic data of the USA; investment inclination; attitude to the competitive economy; assumed ownership of various branches of the economy in the FRG and in the USA, differences according to government and private; expected influence of the American government on the economy and vice versa; estimated proportion of members of the middle classes; image of American agriculture; judgement on the ideological influence of the USA on the FRG; sources of information about America; membership in clubs and organizations and offices taken on; party preference; self-assessment of social class; local residency. Demography: age (classified); marital status; religious denomination; school education; occupation; employment; household income; state; refugee status. Interviewer rating: social class and willingness of respondent to cooperate; number of contact attempts. Also encoded were: age of interviewer and sex of interviewer; city size. Beurteilung der wirtschaftlichen und sozialen Verhältnisse in den USA im Vergleich zur BRD. Themen: Entwicklung der persönlichen wirtschaftlichen Verhältnisse und des Lebensstandards in der BRD; Gründe für das sogenannte Wirtschaftswunder und Anteil der USA am wirtschaftlichen Aufschwung; wahrgenommene Verknüpfung der deutschen Wirtschaftsentwicklung mit anderen Ländern; Einstellung zu einem europäischen gemeinsamen Markt; Gründe für den hohen amerikanischen Lebensstandard; Vergleich zwischen USA und BRD bezüglich der Arbeitsbedingungen, Produktivität, Leistungsfähigkeit, Sozialversicherung und Arbeitsplatzsicherheit von Arbeitern; Image von Amerikanern; Kenntnis wirtschaftlicher Daten der USA; Investitionsneigung; Einstellung zur Wettbewerbswirtschaft; vermutete Eignerschaft verschiedener Wirtschaftszweig in der BRD und in den USA, unterschieden nach staatlich und privat; vermuteter Einfluß der amerikanischen Regierung auf die Wirtschaft und umgekehrt; geschätzter Anteil von Zugehörigen zum Mittelstand; Image der amerikanischen Landwirtschaft; Beurteilung des ideologischen Einflusses der USA auf die BRD; Informationsquellen über Amerika; Mitgliedschaft in Vereinen und Organisationen und übernommene Ämter; Parteipräferenz; Selbsteinschätzung der Schichtzugehörigkeit; Ortsansässigkeit. Demographie: Alter (klassiert); Familienstand; Konfession; Schulbildung; Beruf; Berufstätigkeit; Haushaltseinkommen; Bundesland; Flüchtlingsstatus. Interviewerrating: Schichtzugehörigkeit und Kooperationsbereitschaft des Befragten; Anzahl der Kontaktversuche. Zusätzlich verkodet wurden: Intervieweralter und Interviewergeschlecht; Ortsgröße.

This annual publication focuses on the Social Security beneficiary population at the ZIP Code level. It presents basic program data on the number and type of beneficiaries and the amount of benefits paid in each state, Social Security Administration field office, and ZIP Code. It also shows the number of beneficiaries aged 65 or older. Report for 2016.

A broad and generalized selection of 2012-2016 US Census Bureau 2016 5-year American Community Survey race, ethnicity and citizenship data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico Census tracts). The selection is not comprehensive, but allows a first-level characterization of the household income, median household income by race and by age group, Social Security income, the GINI Index, per capita income, median family income, and median household earnings by age, and by education level, in New Mexico. The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users.The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. The ACS combines population or housing data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. While the ACS contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by Census tract boundaries in New Mexico. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area. NOTE: A '-666666666' entry indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.

This dataset, prepared by the Inter-university Consortium for Political and Social Research, comprises 2 percent of the cases in the second release of CENSUS OF POPULATION AND HOUSING, 1990 UNITED STATES: PUBLIC USE MICRODATA SAMPLE: 5-PERCENT SAMPLE (ICPSR 9952). As 2 percent of the 5-percent Public Use Microdata Sample (PUMS), it constitutes a 1-in-1,000 sample, and contains all housing and population variables in the original 5-percent PUMS. Housing variables include area type, state and area of residence, farm/nonfarm status, type of structure, year structure was built, vacancy and boarded-up status, number of rooms and bedrooms, presence or absence of a telephone, presence or absence of complete kitchen and plumbing facilities, type of sewage, water source, and heating fuel used, property value, tenure, year moved into housing unit, type of household/family, type of group quarters, household language, number of persons, related children, own/adopted children, and stepchildren in the household, number of persons and workers in the family, status of mortgage, second mortgage, and home equity loan, number of vehicles available, household income, sales of agricultural products, payments for rent, mortgage, and property tax, condominium fees, mobile home costs, and cost of electricity, water, heating fuel, and flood/fire/hazard insurance. Person variables cover age, sex, relationship to householder, educational attainment, school enrollment, race, Hispanic origin, ancestry, language spoken at home, citizenship, place of birth, year of immigration, place of residence in 1985, marital status, number of children ever born, presence and age of own children, military service, mobility and personal care limitation, work limitation status, employment status, employment status of parents, occupation, industry, class of worker, hours worked last week, weeks worked in 1989, usual hours worked per week, temporary absence from work, place of work, time of departure for work, travel time to work, means of transportation to work, number of occupants in vehicle during ride to work, total earnings, total income, wages and salary income, farm and nonfarm self-employment income, Social Security income, public assistance income, retirement income, and rent, dividends, and net rental income. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR06497.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

The 1940 Census Public Use Microdata Sample Project was assembled through a collaborative effort between the United States Bureau of the Census and the Center for Demography and Ecology at the University of Wisconsin. The collection contains a stratified 1-percent sample of households, with separate records for each household, for each "sample line" respondent, and for each person in the household. These records were encoded from microfilm copies of original handwritten enumeration schedules from the 1940 Census of Population. Geographic identification of the location of the sampled households includes Census regions and divisions, states (except Alaska and Hawaii), standard metropolitan areas (SMAs), and state economic areas (SEAs). Accompanying the data collection is a codebook that includes an abstract, descriptions of sample design, processing procedures and file structure, a data dictionary (record layout), category code lists, and a glossary. Also included is a procedural history of the 1940 Census. Each of the 20 subsamples contains three record types: household, sample line, and person. Household variables describe the location and condition of the household. The sample line records contain variables describing demographic characteristics such as nativity, marital status, number of children, veteran status, wage deductions for Social Security, and occupation. Person records also contain variables describing demographic characteristics including nativity, marital status, family membership, education, employment status, income, and occupation. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08236.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.