The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

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..The age dependency ratio is derived by dividing the combined under-18 and 65-and-over populations by the 18-to-64 population and multiplying by 100..The old-age dependency ratio is derived by dividing the population 65 and over by the 18-to-64 population and multiplying by 100..The child dependency ratio is derived by dividing the population under 18 by the 18-to-64 population and multiplying by 100..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..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.

Key Table Information.Table Title.Age and Sex.Table ID.ACSST1Y2024.S0101.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Subject 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, cities, and towns and estimates of housing units and t...

Initially taken in 1838 to demonstrate the stability and significance of the African American community and to forestall the abrogation of African American voting rights, the Quaker and Abolitionist census of African Americans was continued in 1847 and 1856 and present an invaluable view of the mid-nineteenth century African American population of Philadelphia. Although these censuses list only household heads, providing aggregate information for other household members, and exclude the substantial number of African Americans living in white households, they provide data not found in the federal population schedules. When combined with the information on African Americans taken from the four federal censuses, they offer researchers a richly detailed view of Philadelphia's African American community spanning some forty years. The three censuses are not of equal inclusiveness or quality, however. The 1838 and 1847 enumerations cover only the "old" City of Philadelphia (river-to-river and from Vine to South Streets) and the immediate surrounding districts (Spring Garden, Northern Liberties, Southwark, Moyamensing, Kensington--1838, West Philadelphia--1847); the 1856 survey includes African Americans living throughout the newly enlarged city which, as today, conforms to the boundaries of Philadelphia County. In spite of this deficiency in areal coverage, the earlier censuses are superior historical documents. The 1838 and 1847 censuses contain data on a wide range of social and demographic variables describing the household indicating address, household size, occupation, whether members were born in Pennsylvania, status-at-birth, debts, taxes, number of children attending school, names of beneficial societies and churches (1838), property brought to Philadelphia from other states (1838), sex composition (1847), age structure (1847), literacy (1847), size of rooms and number of people per room (1847), and miscellaneous remarks (1847). While the 1856 census includes the household address and reports literacy, occupation, status-at-birth, and occasional passing remarks about individual households and their occupants, it excludes the other informational categories. Moreover, unlike the other two surveys, it lists the occupations of only higher status African Americans, excluding unskilled and semiskilled designations, and records the status-at-birth of adults only. Indeed, it even fails to provide data permitting the calculation of the size and age and sex structure of households. Variables for each household head and his household include (differ slightly by census year): name, sex, status-at-birth, occupation, wages, real and personal property, literacy, education, religion, membership in beneficial societies and temperance societies, taxes, rents, dwelling size, address, slave or free birth.

The MARS file contains modified race and age data based on the 1990 Census. Both race and age are tabulated by sex and Hispanic origin for several layers of geography. The race data were modified to make reporting categories comparable to those used by state and local agencies. The 1990 Census included 9,804,847 persons who checked the "other race" category and were therefore not included in one of the 15 racial categories listed on the Census form. "Other race" is usually not an acceptable reporting category for state and local agencies. Therefore, the Census Bureau assigned each "other race" person to the specified race reported by another person geographically close with an identical response to the Hispanic-origin question. Hispanic origin was taken into account because over 95 percent of the "other race" persons were of Hispanic origin. (Hispanic-origin persons may be of any race.) The assignment of race to Hispanic-origin persons did not affect the Hispanic-origin category that they checked (i.e, Mexican, Puerto Rican, Cuban, etc.). Age data were modified because respondents tended to report age as of the date they completed the 1990 questionnaire, instead of age as of the April 1, 1990 Census date. In addition, there may have been a tendency for respondents to round up their age if they were close to having a birthday. Age data for individuals in households were modified by adjusting the reported birth-year data by race and sex for each of the 1990 Census's 449 district offices to correspond with the national level quarterly distribution of births available from the National Center for Health Statistics. The data for persons in group quarters were adjusted similarly, but on a state basis. The age adjustment affects approximately 100 million people. In this file their adjusted age is one year different from that reported in the 1990 Census. STF-S-2B only contains data for 12 states, their counties, and Minor Civil Divisions (MCDs). (Source: ICPSR, retrieved 06/15/2011)

LGA based data for Language Spoken at Home by Sex, in Time Series Profile, 1996 Census. Count of all persons aged 5 years and over in 1986, 1991 and 1996 census years. T09 is broken up into two sections (T09A-T09B) this section covers ‘Census 1986 - english only male’ - ‘Census 1996 - other language portuguese persons’. The data is by LGA 1996 boundaries. Periodicity: 5-Yearly. This data is ABS data (geographic boundary cat. no. 1261.0.30.001 & census dictionary cat. no. 2901.0) used with permission from the Australian Bureau of Statistics. For more information visit the ABS .

Abstract

The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

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Documentation

This dataset was created on 2020-01-10 22:52:11.461 by merging multiple datasets together. The source datasets for this version were:

IPUMS 1930 households: This dataset includes all households from the 1930 US census.

IPUMS 1930 persons: This dataset includes all individuals from the 1930 US census.

IPUMS 1930 Lookup: This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1930 datasets.

Section 2

Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier. In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

The historic US 1930 census data was collected in April 1930. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

Notes

• We provide IPUMS household and person data separately so that it is convenient to explore the descriptive statistics on each level. In order to obtain a full dataset, merge the household and person on the variables SERIAL and SERIALP. In order to create a longitudinal dataset, merge datasets on the variable HISTID.

• Households with more than 60 people in the original data were broken up for processing purposes. Every person in the large households are considered to be in their own household. The original large households can be identified using the variable SPLIT, reconstructed using the variable SPLITHID, and the original count is found in the variable SPLITNUM.

• Coded variables derived from string variables are still in progress. These variables include: occupation and industry.

• Missing observations have been allocated and some inconsistencies have been edited for the following variables: SPEAKENG, YRIMMIG, CITIZEN, AGEMARR, AGE, BPL, MBPL, FBPL, LIT, SCHOOL, OWNERSHP, FARM, EMPSTAT, OCC1950, IND1950, MTONGUE, MARST, RACE, SEX, RELATE, CLASSWKR. The flag variables indicating an allocated observation for the associated variables can be included in your extract by clicking the ‘Select data quality flags’ box on the extract summary page.

• Most inconsistent information was not edite

Abstract

The key objective of every census is to count every person (man, woman, child) resident in the country on census night, and also collect information on assorted demographic (sex, age, marital status, citizenship) and socio-economic (education/qualifications; labour force and economic activity) information, as well as data pertinent to household and housing characteristics. This count provides a complete picture of the population make-up in each village and town, of each island and region, thus allowing for an assessment of demographic change over time.

The need for a national census became obvious to the Census Office (Bureau of Statistics) during 1997 when a memo was submitted to government officials proposing the need for a national census in an attempt to update old socio-economic figures. The then Acting Director of the Bureau of Statistics and his predecessor shared a similar view: that the 'heydays' and 'prosperity' were nearing their end. This may not have been apparent, as it took until almost mid-2001 for the current Acting Government Statistician to receive instructions to prepare planning for a national census targeted for 2002. It has been repeatedly said that for adequate planning at the national level, information about the characteristics of the society is required. With such information, potential impacts can be forecast and policies can be designed for the improvement and benefit of society. Without it, the people, national planners and leaders will inevitably face uncertainties.

Geographic coverage

National coverage as the Population Census covers the whole of Nauru.

Analysis unit

• Household
• Individual (in a private household dwelling, institutions and non-private dwelling).

Universe

The Census covers all individuals living in private and non-private dwellings and institutions.

Kind of data

Census/enumeration data [cen]

Sampling deviation

There is no sampling for the population census, full coverage.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was based on the Pacific Islands Model Population and Housing Census Form and the 1992 census, and comprised two parts: a set of household questions, asked only of the head of household, and an individual questionnaire, administered to each household member. Unlike the previous census, which consisted of a separate household form plus two separate individual forms for Nauruans and non-Nauruans, the 2 002 questionnaire consisted of only one form separated into different parts and sections. Instructions (and skips) were desi

The questionnaire cover recorded various identifiers: district name, enumeration area, house number, number of households (family units) residing, total number of residents, gender, and whether siblings of the head of the house were also recorded. The second page, representing a summary page, listed every individual residing within the house. This list was taken by the enumerator on the first visit, on the eve of census night. The first part of the census questionnaire focused on housing-related questions. It was administered only once in each household, with questions usually asked of the household head. The household form asked the same range of questions as those covered in the 1992 census, relating to type of housing, structure of outer walls, water supply sources and storage, toilet and cooking facilities, lighting, construction materials and subsistence-type activities. The second part of the census questionnaire focused on individual questions covering all household members. This section was based on the 1992 questions, with notable differences being the exclusion of income-level questions and the expansion of fertility and mortality questions. As in 1992, a problem emerged during questionnaire design regarding the question of who or what should determine a ‘Nauruan’. Unlike the 1992 census, where the emphasis was on blood ties, the issue of naturalisation and citizenship through the sale of passports seriously complicated matters in 2 002. To resolve this issue, it was decided to apply two filtering processes: Stage 1 identified persons with tribal heritage through manual editing, and Stage 2 identified persons of Nauruan nationality and citizenship through designed skips in the questionnaire that were incorporated in the data-processing programming.

The topics of questions for each of the parts include: - Person Particulars: - name - relationship - sex - ethnicity - religion - educational attainment - Economic Activity (to all persons 15 years and above): - economic activity - economic inactive - employment status - Fertility: - Fertility - Mortality - Labour Force Activity: - production of cash crops - fishing - own account businesses - handicrafts. - Disability: - type of disability - nature of disability - Household and housing: - electricity - water - tenure - lighting - cooking - sanitation - wealth ownerships

Cleaning operations

Coding, data entry and editing Coding took longer than expected when the Census Office found that more quality-control checks were required before coding could take place and that a large number of forms still required attention. While these quality-control checks were supposed to have been done by the supervisors in the field, the Census Office decided to review all census forms before commencing the coding. This process took approximately three months, before actual data processing could begin. The amount of additional time required to recheck the quality of every census form meant that data processing fell behind schedule. The Census Office had to improvise, with a little pressure from external stakeholders, and coding, in conjunction with data entry, began after recruiting two additional data entry personnel. All four Census Office staff became actively involved with coding, with one staff member alternating between coding and data entry, depending on which process was dropping behind schedule. In the end, the whole process took almost two months to complete. Prior to commencing data entry, the Census Office had to familiarise itself with the data entry processing system. For this purpose, SPC’s Demography/Population Programme was invited to lend assistance. Two office staff were appointed to work with Mr Arthur Jorari, SPC Population Specialist, who began by revising their skills for the data processing software that had been introduced by Dr McMurray. This training attachment took two weeks to complete. Data entry was undertaken using the 2 .3 version of the US Census Bureau’s census and surveying processing software, or CSPro2.3. This version was later updated to CSPro2.4, and all data were transferred accordingly. Technical assistance for data editing was provided by Mr Jorari over a two-week period. While most edits were completed during this period, it was discovered that some batches of questionnaires had not been entered during the initial data capturing. Therefore, batch-edit application had to be regenerated. This process was frequently interrupted by power outages prevailing at the time, which delayed data processing considerably and also required much longer periods of technical support to the two Nauru data processing staff via phone or email (when available).

Data appraisal

Data was compared with Administrative records after the Census to review the quality and reliability of the data.

This data collection contains individual-level and family-level information collected from the 1870 and 1880 manuscript schedules of the United States Population Census for seven Southern cities: Charleston, South Carolina, Richmond, Virginia, Atlanta, Georgia, Savannah, Georgia, Mobile, Alabama, Norfolk, Virginia, and New Orleans, Louisiana. Approximately 5,000 individuals and 1,500 families are represented for each of the two census years studied. Part 1 contains data for 1870, and Part 2 contains data for 1880. The data gathered for sampled individuals include age, sex, race, marital status, presence of health defect, school attendance, ability to read, ability to write, occupational classification (female and male), nationality, and real and personal wealth (for 1870 only). Both datasets include a variable that uniquely identifies each family in the sample to facilitate the aggregation of the data for the creation of family-level data for each member, e.g., sex, race, age, marital status, school attendance, member status in the family, occupation, health, unemployment, city of residence, nationality and parents' nationality, and real and personal wealth.

Abstract

This study is an experiment designed to compare the performance of three methodologies for sampling households with migrants:

• a stratified sample using the census to sample census tracts randomly, in which each household is then listed and screened to determine whether or not it has a migrant, with the full length questionnaire then being applied in a second phase only to the households of interest;
• a snowball survey in which households are asked to provide referrals to other households with migrant members;
• an intercept point survey (or time-and-space sampling survey), in which individuals are sampled during set time periods at a prespecified set of locations where households in the target group are likely to congregate.

Researchers from the World Bank applied these methods in the context of a survey of Brazilians of Japanese descent (Nikkei), requested by the World Bank. There are approximately 1.2-1.9 million Nikkei among Brazil’s 170 million population.

The survey was designed to provide detail on the characteristics of households with and without migrants, to estimate the proportion of households receiving remittances and with migrants in Japan, and to examine the consequences of migration and remittances on the sending households.

The same questionnaire was used for the stratified random sample and snowball surveys, and a shorter version of the questionnaire was used for the intercept surveys. Researchers can directly compare answers to the same questions across survey methodologies and determine the extent to which the intercept and snowball surveys can give similar results to the more expensive census-based survey, and test for the presence of biases.

Geographic coverage

Sao Paulo and Parana states

Analysis unit

Japanese-Brazilian (Nikkei) households and individuals

The 2000 Brazilian Census was used to classify households as Nikkei or non-Nikkei. The Brazilian Census does not ask ethnicity but instead asks questions on race, country of birth and whether an individual has lived elsewhere in the last 10 years. On the basis of these questions, a household is classified as (potentially) Nikkei if it has any of the following: 1) a member born in Japan; 2) a member who is of yellow race and who has lived in Japan in the last 10 years; 3) a member who is of yellow race, who was not born in a country other than Japan (predominantly Korea, Taiwan or China) and who did not live in a foreign country other than Japan in the last 10 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

1) Stratified random sample survey

Two states with the largest Nikkei population - Sao Paulo and Parana - were chosen for the study.

The sampling process consisted of three stages. First, a stratified random sample of 75 census tracts was selected based on 2000 Brazilian census. Second, interviewers carried out a door-to-door listing within each census tract to determine which households had a Nikkei member. Third, the survey questionnaire was then administered to households that were identified as Nikkei. A door-to-door listing exercise of the 75 census tracts was then carried out between October 13th, 2006, and October 29th, 2006. The fieldwork began on November 19, 2006, and all dwellings were visited at least once by December 22, 2006. The second wave of surveying took place from January 18th, 2007, to February 2nd, 2007, which was intended to increase the number of households responding.

2) Intercept survey

The intercept survey was designed to carry out interviews at a range of locations that were frequented by the Nikkei population. It was originally designed to be done in Sao Paulo city only, but a second intercept point survey was later carried out in Curitiba, Parana. Intercept survey took place between December 9th, 2006, and December 20th, 2006, whereas the Curitiba intercept survey took place between March 3rd and March 12th, 2007.

Consultations with Nikkei community organizations, local researchers and officers of the bank Sudameris, which provides remittance services to this community, were used to select a broad range of locations. Interviewers were assigned to visit each location during prespecified blocks of time. Two fieldworkers were assigned to each location. One fieldworker carried out the interviews, while the other carried out a count of the number of people with Nikkei appearance who appeared to be 18 years old or older who passed by each location. For the fixed places, this count was made throughout the prespecified time block. For example, between 2.30 p.m. and 3.30 p.m. at the sports club, the interviewer counted 57 adult Nikkeis. Refusal rates were carefully recorded, along with the sex and approximate age of the person refusing.

In all, 516 intercept interviews were collected.

3) Snowball sampling survey

The questionnaire that was used was the same as used for the stratified random sample. The plan was to begin with a seed list of 75 households, and to aim to reach a total sample of 300 households through referrals from the initial seed households. Each household surveyed was asked to supply the names of three contacts: (a) a Nikkei household with a member currently in Japan; (b) a Nikkei household with a member who has returned from Japan; (c) a Nikkei household without members in Japan and where individuals had not returned from Japan.

The snowball survey took place from December 5th to 20th, 2006. The second phase of the snowballing survey ran from January 22nd, 2007, to March 23rd, 2007. More associations were contacted to provide additional seed names (69 more names were obtained) and, as with the stratified sample, an adaptation of the intercept survey was used when individuals refused to answer the longer questionnaire. A decision was made to continue the snowball process until a target sample size of 100 had been achieved.

The final sample consists of 60 households who came as seed households from Japanese associations, and 40 households who were chain referrals. The longest chain achieved was three links.

Mode of data collection

Face-to-face [f2f]

Research instrument

1) Stratified sampling and snowball survey questionnaire

This questionnaire has 36 pages with over 1,000 variables, taking over an hour to complete.

If subjects refused to answer the questionnaire, interviewers would leave a much shorter version of the questionnaire to be completed by the household by themselves, and later picked up. This shorter questionnaire was the same as used in the intercept point survey, taking seven minutes on average. The intention with the shorter survey was to provide some data on households that would not answer the full survey because of time constraints, or because respondents were reluctant to have an interviewer in their house.

2) Intercept questionnaire

The questionnaire is four pages in length, consisting of 62 questions and taking a mean time of seven minutes to answer. Respondents had to be 18 years old or older to be interviewed.

Response rate

1) Stratified random sampling 403 out of the 710 Nikkei households were surveyed, an interview rate of 57%. The refusal rate was 25%, whereas the remaining households were either absent on three attempts or were not surveyed because building managers refused permission to enter the apartment buildings. Refusal rates were higher in Sao Paulo than in Parana, reflecting greater concerns about crime and a busier urban environment.

2) Intercept Interviews 516 intercept interviews were collected, along with 325 refusals. The average refusal rate is 39%, with location-specific refusal rates ranging from only 3% at the food festival to almost 66% at one of the two grocery stores.

This data set provides statistics about employer and nonemployer businesses from 2021 for the nation, states, counties, and metropolitan statistical areas (MSA). It includes the number of firms, revenue, number of employees, and annual payroll, broken down by industry and owner demographics including as sex, ethnicity, race, and veteran status.About NES-DThe Nonemployer Statistics by Demographics series (NES-D) provides information on the demographic characteristics of nonemployer businesses. The NES-D is the result of a research project by the Census Bureau to complete the picture of U.S. business ownership by demographics for the United States. Historically, the quinquennial Survey of Business Owners (SBO) provided the only comprehensive source of information on both employer and nonemployer businesses by demographic characteristics of the business owners. In 2017, the SBO was replaced by the Annual Business Survey (ABS). The ABS is an annual survey that collects demographic characteristics from employer businesses. However, the ABS excludes the collection of demographic data from nonemployer businesses. The NES-D was developed to produce similar estimates as ABS on owner demographics for nonemployer businesses. The NES-D is not a survey; rather, it leverages existing individual-level administrative records to assign demographic characteristics to the universe of nonemployer businesses. Demographic characteristics including sex, ethnicity, race, veteran status, owner age, place of birth, and U.S. citizenship are assigned to nonemployer business owners.Together, the NES-D and the ABS will continue to provide the only source of detailed and comprehensive statistics on the scope, nature and activities of all U.S. businesses by the demographic characteristics of the business owners. NES-D data will be available annually by detailed geography and industry levels, receipt-size class, and legal form of organization (LFO). Beginning with the 2019 NES-D, the data will include urban and rural classification.

This data set provides statistics about employer and nonemployer businesses from 2020 for the nation, states, and metropolitan statistical areas (MSA). It includes the number of firms, revenue, number of employees, and annual payroll, broken down by industry and owner demographics including as sex, ethnicity, race, and veteran status.About NES-DThe Nonemployer Statistics by Demographics series (NES-D) provides information on the demographic characteristics of nonemployer businesses. The NES-D is the result of a research project by the Census Bureau to complete the picture of U.S. business ownership by demographics for the United States. Historically, the quinquennial Survey of Business Owners (SBO) provided the only comprehensive source of information on both employer and nonemployer businesses by demographic characteristics of the business owners. In 2017, the SBO was replaced by the Annual Business Survey (ABS). The ABS is an annual survey that collects demographic characteristics from employer businesses. However, the ABS excludes the collection of demographic data from nonemployer businesses. The NES-D was developed to produce similar estimates as ABS on owner demographics for nonemployer businesses. The NES-D is not a survey; rather, it leverages existing individual-level administrative records to assign demographic characteristics to the universe of nonemployer businesses. Demographic characteristics including sex, ethnicity, race, veteran status, owner age, place of birth, and U.S. citizenship are assigned to nonemployer business owners.Together, the NES-D and the ABS will continue to provide the only source of detailed and comprehensive statistics on the scope, nature and activities of all U.S. businesses by the demographic characteristics of the business owners. NES-D data will be available annually by detailed geography and industry levels, receipt-size class, and legal form of organization (LFO). Beginning with the 2019 NES-D, the data will include urban and rural classification.

A detailed explanation of how this dataset was put together, including data sources and methodologies, follows below.Please see the "Terms of Use" section below for the Data DictionaryDATA ACQUISITION AND CLEANING PROCESSThis dataset was built from 5 separate datasets queried during the months of April and May 2023 from the Census Microdata System (link below):https://data.census.gov/mdat/#/All datasets include information on Property Value (VALP) by: Educational Attainment (SCHL), Gender (SEX), a specified race or ethnicity (RAC or HISP), and are grouped by Public Use Microdata Areas (PUMAS). PUMAS are geographic areas created by the Census bureau; they are weighted by land area and population to facilitate data analysis. Data also Included totals for the state of New Mexico, so 19 total geographies are represented. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Cleaning each dataset started with recoding the SCHL and HISP variables - details on recoding can be found below.After recoding, each dataset was transposed so that PUMAS were rows and SCHL, VALP, SEX, and Race or Ethnicity variables were the columns.Median values were calculated in every case that recoding was necessary. As a result, all Property Values in this dataset reflect median values.At times the ACS data downloaded with zeros instead of the 'null' values in initial query results. The VALP variable also included a "-1" variable to reflect N/A values (details in variable notes). Both zeros and "-1" values were removed before calculating median values, both to keep the data true to the original query and to generate accurate median values.Recoding the SCHL variable resulted in 5 rows for each PUMA, reflecting the different levels of educational attainment in each region. Columns grouped variables by race or ethnicity and gender. Cell values were property values.All 5 datasets were joined after recoding and cleaning the data. Original datasets all include 95 rows with 5 separate Educational Attainment variables for each PUMA, including New Mexico State totals.Because 1 row was needed for each PUMA in order to map this data, the data was split by Educational Attainment (SCHL), resulting in 110 columns reflecting median property values for each race or ethnicity by gender and level of educational attainment.A short, unique 2 to 5 letter alias was created for each PUMA area in anticipation of needing a unique identifier to join the data with. GIS AND MAPPING PROCESSA PUMA shapefile was downloaded from the ACS site. The Shapefile can be downloaded here: https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/PUMA_TAD_TAZ_UGA_ZCTA/MapServerThe DBF from the PUMA shapefile was exported to Excel; this shapefile data included needed geographic information for mapping such as: GEOID, PUMACE. The UIDs created for each PUMA were added to the shapefile data; the PUMA shapfile data and ACS data were then joined on UID in JMP.The data table was joined to the shapefile in ARC GiIS, based on PUMA region (specifically GEOID text).The resulting shapefile was exported as a GDB (geodatabase) in order to keep 'Null' values in the data. GDBs are capable of including a rule allowing null values where shapefiles are not. This GDB was uploaded to NMCDCs Arc Gis platform. SYSTEMS USEDMS Excel was used for data cleaning, recoding, and deriving values. Recoding was done directly in the Microdata system when possible - but because the system is was in beta at the time of use some features were not functional at times.JMP was used to transpose, join, and split data. ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform. VARIABLE AND RECODING NOTESTIMEFRAME: Data was queried for the 5 year period of 2015 to 2019 because ACS changed its definiton for and methods of collecting data on race and ethinicity in 2020. The change resulted in greater aggregation and les granular data on variables from 2020 onward.Note: All Race Data reflects that respondants identified as the specified race alone or in combination with one or more other races.VARIABLE:ACS VARIABLE DEFINITIONACS VARIABLE NOTESDETAILS OR URL FOR RAW DATA DOWNLOADRACBLKBlack or African American ACS Query: RACBLK, SCHL, SEX, VALP 2019 5yrRACAIANAmerican Indian and Alaska Native ACS Query: RACAIAN, SCHL, SEX, VALP 2019 5yrRACASNAsian ACS Query: RACASN, SCHL, SEX, VALP 2019 5yrRACWHTWhite ACS Query: RACWHT, SCHL, SEX, VALP 2019 5yrHISPHispanic Origin ACS Query: HISP ORG, SCHL, SEX, VALP 2019 5yrHISP RECODE: 24 original separate variablesThe Hispanic Origin (HISP) variable originally included 24 subcategories reflecting Mexican, Central American, South American, and Caribbean Latino, and Spanish identities from each Latin American counry. 7 recoded VariablesThese 24 variables were recoded (grouped) into 7 simpler categories for data analysis: Not Spanish/Hispanic/Latino, Mexican, Caribbean Latino, Central American, South American, Spaniard, All other Spanish/Hispanic/Latino Female. Not Spanish/Hispanic/Latino was not really used in the final dataset as the race datasets provided that information.SCHLEducational Attainment25 original separate variablesThe Educational Attainment (SCHL) variable originally included 25 subcategories reflecting the education levels of adults (over 18) surveyed by the ACS. These include: Kindergarten, Grades 1 through 12 separately, 12th grade with no diploma, Highschool Diploma, GED or credential, less than 1 year of college, more than 1 year of college with no degree, Associate's Degree, Bachelor's Degree, Master's Degree, Professional Degree, and Doctorate Degree.SCHL RECODE: 5 recoded variablesThese 25 variables were recoded (grouped) into 5 simpler categories for data analysis: No High School Diploma, High School Diploma or GED, Some College, Bachelor's Degree, and Advanced or Professional DegreeSEXGender2 variables1 - Male, 2 - FemaleVALPProperty Value1 variableValues were rounded and top-coded by ACS for anonymity. The "-1" variable is defined as N/A (GQ/ Vacant lots except 'for sale only' and 'sold, not occupied' / not owned or being bought.) This variable reflects the median value of property owned by individuals of each race, ethnicity, gender, and educational attainment category.PUMAPublic Use Microdata Area18 PUMAsPUMAs in New Mexico can be viewed here:https://nmcdc.maps.arcgis.com/apps/mapviewer/index.html?webmap=d9fed35f558948ea9051efe9aa529eafData includes 19 total regions: 18 Pumas and NM State TotalsNOTES AND RESOURCESThe following resources and documentation were used to navigate the ACS PUMS system and to answer questions about variables:Census Microdata API User Guide:https://www.census.gov/data/developers/guidance/microdata-api-user-guide.Additional_Concepts.html#list-tab-1433961450Accessing PUMS Data:https://www.census.gov/programs-surveys/acs/microdata/access.htmlHow to use PUMS on data.census.govhttps://www.census.gov/programs-surveys/acs/microdata/mdat.html2019 PUMS Documentation:https://www.census.gov/programs-surveys/acs/microdata/documentation.2019.html#list-tab-13709392012014 to 2018 ACS PUMS Data Dictionary:https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2014-2018.pdf2019 PUMS Tiger/Line Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Public+Use+Microdata+Areas Note 1: NMCDC attemepted to contact analysts with the ACS system to clarify questions about variables, but did not receive a timely response. Documentation was then consulted.Note 2: All relevant documentation was reviewed and seems to imply that all survey questions were answered by adults, age 18 or over. Youth who have inherited property could potentially be reflected in this data.Dataset and feature service created in May 2023 by Renee Haley, Data Specialist, NMCDC.

Abstract

This is the tenth census undertaken by the Statistics Office, the first being in 1971, and it has been held every five years ever since.

The census counts all persons present in the Cook Islands on the census night of the 1st December 2016, including visitors temporary in the country. Cook Islanders who are living or are temporarily on vacation abroad are excluded.

1. Organisation
   The overall organisation and control of the census, by virtue of the Statistics Act 2016, is vested upon the Government Statistician who, for the purpose of the census will be referred to as the Census Officer. A number of sections of the Act apply in carrying out the census. These include the “confidentiality” clause, which provides against the release or publication of any individual particulars and the offences and penalty clauses, which may be invoked against any persons failing to abide by the provisions of the Act.

2. Scope and Coverage The scope of the early Cook Islands censuses was limited; in fact they consisted of head counts only. With the passage of time the census has expanded. Gradually, questions on sex, age, marital status, religion, education, employment, etc., have been included. Questions on unpaid work and income earned were included for the first time in the 1996 Census. In the 2016 Census, questions on relationship to head of household was expanded to reflect household living arrangement.
   A personal questionnaire is completed for every man, woman and child alive at midnight on census night within the geographical boundaries of the Cook Islands. The Census excludes those persons on foreign vessels, yachts and aircraft flying through or stopping temporarily (transit). A dwelling questionnaire is completed for every occupied dwelling as at midnight on census night.

3. Objectives of the Census Taking account of the many comments, evaluations and recommendations arising from the 2011 Census, the design of the 2016 Census is based on a number of key strategic aims: 1) to give the highest priority to getting the national and local population counts right; 2) to maximise overall response and minimise differences in response rates in specific areas and among particular population sub-groups; 3) to build effective partnerships with other organisations, particularly local authorities, in planning and executing the field operation; 4) to provide high quality, value-for-money, statistics that meet user needs ; 5) to protect, and be seen to protect, confidential personal census information.

4. The selection of topics and questions The topic content of the 2016 Census has been driven largely by the demands and requirements of users of census statistics, the evaluation of the 2016 and 2011 Census, and the priority of government as stated in the National Strategic Development Plan of the Cook Islands (NSDP) and the advice and guidance of organizations with experience of similar operations. These have been determined by extensive consultation with various Ministries of government and Non-Governmental Organizations (NGOs).

Geographic coverage

National coverage.

Analysis unit

Households and Individuals.

Universe

A Dwelling Questionnaire must be completed for every occupied dwelling as at midnight on Census Night. A Personal Questionnaire must be completed for each and every man, woman and child alive at midnight on Census Night within the geographical boundaries of the Cook Islands, excluding those persons on foreign vessels, yachts and aircraft flying through or stopping temporarily (transit).

Kind of data

Census/enumeration data [cen]

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

-The selection of topics and questions: The topic content of the 2016 Census has been driven largely by the demands and requirements of users of census statistics, the evaluation of the 2016 and 2011 Census, and the priority of government as stated in the National Strategic Development Plan of the Cook Islands (NSDP) and the advice and guidance of organizations with experience of similar operations. These have been determined by extensive consultation with various Ministries of government and Non-Governmental Organizations (NGOs).

-The census questions: The topics proposed for the census are those that have been shown to be most needed by the major users of census information and for which questions have been devised that can be expected to produce reliable and accurate data. In each case, no other comparable and accessible source of the information is available in combination with other items in the census. Consultation on the topic content for the 2016 Census has (as ever) resulted in a much larger demand for questions than would be possible to accommodate on a census form that households could reasonably be expected to complete. Consequently a number of difficult decisions have had to be made in assessing the different requirements for information and balancing the needs for change against continuity. In assessing which topics should be included in the census, Statistics Office has had to consider a number of factors. The criteria for evaluating the strength of users' requirements for information were that: ? there should be a clearly demonstrated and signi?cant need ? the information collected was of major national importance ? users' requirements could not adequately be met by information from other sources ? there should be a requirement for multivariate analysis (that is the ability to cross-analyse one variable against other), and ? there should be consideration of the ability for comparison with previous censuses wherever possible

So therefore were 2 questionnaires or forms used for the Census and they are: 1. Dwelling form - consist of the housheholds information on dwelling type, land tenure, dwelling materials, water and sanitation, energy, household facilities, solid waste, agriculture and fishing activities and equipments, household consumption, communication technology etc. and household relationship to head 2. Personal form - consist of the every member/individuals of the households' information on nationality, migration, ethnic origin, marital status, religion, physically challenged, literacy, information technology, education, training attainment, occupation, industry, employment, income, smoking, drinking, cultural activities and fertility

They were published in english and all are provided as external resources.

Cleaning operations

After sending the forms to Statistics New Zealand for scanning, Cook Islands Statistics Office (CISO) staff then carry out the coding of the industries and occupation and the first visual editing if there are some inconsistencies in the questionnaire mainly using the Access software, and the tabulations is carried-out in both Access and Excel ready for analysis and report writing.

Prepared by the Inter-university Consortium for Political and Social Research, this data collection consists of selected subsets extracted from the Census of Population and Housing, 2000 [United States]: Summary File 1, Advance National (ICPSR 3325). Summary File 1 data contain information compiled from the questions asked of all people and of every housing unit enumerated in Census 2000: questions covering sex, age, race, Hispanic or Latino origin, type of living quarters (household/group quarters), household relationship, housing unit vacancy status, and housing unit tenure (owner/renter). The information is presented in 286 tables, which are tabulated for every case, i.e., every geographic unit represented in the data. There is one variable per table cell, plus additional variables with geographic information. All cases in the summary file data are classified by levels of observation, known as "summary levels," in the Census Bureau's nomenclature. These levels of observation served as the selection criteria for the subsets. Each subset comprises all of the cases in one of five summary levels: the nation (summary level 010), states (summary level 040), counties (summary level 050), places (summary level 160), and five-digit ZIP code tabulation areas (summary level 860). Three files are supplied for each subset except the last. There is a single, relatively large, file that contains all of the tables in the data, plus two smaller files, each of which contains approximately one half of the tables. For the five-digit ZIP code tabulation areas, there is only one file, which contains all of the tables.

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad," "Some College," and "College Grad." High school graduates are considered to include GED completers, and those with some college include community college attendees (and graduates) and those who have attended certain postsecondary vocational or technical schools--in other words, it signifies additional education beyond high school, but short of attaining a bachelor's degree or equivilent. Note that educational attainment is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by education, even if they are otherwise considered part of the universe for the variable of interest.

6. sex: "Male" and "Female" are the two groups in this category. The CPS does not currently provide response options for intersex individuals.

7. race: This category includes "White," "Black," "Hispanic," "Asian," "Am Indian," and "Other" groups. The CPS asks about Hispanic origin separately from racial identification; as a result, all persons identifying as Hispanic are in the Hispanic group, regardless of how else they identify. Furthermore, all non-Hispanic persons identifying with two or more races are tallied in the "Other" group (along with other less-prevelant responses). The Am Indian group includes both American Indians and Alaska Natives.

8. disability: Disability status is divided into "No" and "Yes" groups, indicating whether the person was identified as having a disability. Disabilities screened for in the CPS include hearing impairment, vision impairment (not sufficiently correctable by glasses), cognitive difficulties arising from physical, mental, or emotional conditions, serious difficulty walking or climbing stairs, difficulty dressing or bathing, and difficulties performing errands due to physical, mental, or emotional conditions. The Census Bureau began collecting data on disability status in June 2008; accordingly, this category is unavailable in Supplements prior to that date. Note that disability status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by disability status, even if they are otherwise considered part of the universe for the variable of interest.

9. metro: Metropolitan status is divided into "No," "Yes," and "Unkown," reflecting information in the dataset about the household's location. A household located within a metropolitan statistical area is assigned to the Yes group, and those outside such areas are assigned to No. However, due to the risk of de-anonymization, the metropolitan area status of certain households is unidentified in public use datasets. In those cases, the Census Bureau has determined that revealing this geographic information poses a disclosure risk. Such households are tallied in the Unknown group.

10. scChldHome:

The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. Results for sub-state geographic areas in New Mexico were released in a series of data products. These data come from Summary File 1 (SF-1). The geographic coverage for SF-1 includes the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, census tracts, block groups and blocks, among others. The data in these particular RGIS Clearinghouse tables are for New Mexico and all census tracts in the state. There are two data tables in this file that show housing units by vacancy status (type of vacancy). Table DC10_01031 shows the number of vacant housing units by the following categories--total, for rent, rented but not yet occupied, for sale only, sold but not yet occupied, seasonal or recreational or occasional use, for migrant workers, and vacant for some other reason. Table DC10_01032 shows percent distribution of housing units for each of these same categories. These files, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

Updated for 2013-17: US Census American Community Survey data table for: Journey to Work subject area. Provides information about: SEX OF WORKERS BY PLACE OF WORK--STATE AND COUNTY LEVEL for the universe of: Workers 16 years and over. These data are extrapolated estimates only, based on sampling; they are not actual complete counts. The data is based on 2010 Census Tracts. Table ACS_B08007_SEXPLACEWORKPLSTATECO contains both the Estimate value in the E item for the census topic and an adjacent M item which defines the Margin of Error for the value. The Margin of Error (MOE) is the plus/minus range for the item estimate value, where the range between the Estimate minus the Margin of Error and the Estimate plus the Margin of Error defines the 90% confidence interval of the item value. Many of the Margin of Error values are significant relative to the size of the Estimate value. This table contains 15 item(s) extracted from a larger sequence table. This extracted subset represents that portion of the sequence that is considered high priority. Other portions of this sequence that are not included can be identified in the data dictionary information provided in the Supplemental Information section below. This table information is also provided as a customized layer file: B08007_AREA_SEXPLACEWORKPLSTATECO.lyr where the table information is joined to the 2010 TRACTS_AREA census geography on the GEOID item. Both the table and customized lyr file name do not contain the year descriptor (i.e. 2012-2016) for the current ACS series. This is intentional in order to maintain the same table name in each successive ACS update. The alias of each item's (E)stimate and (M)easure of Error value stores this year date information as beginning YY and ending YY, i.e., 'E1216' and 'M1216' followed by the rest of the alias description. In this way users of the data tables or lyr files that support field aliases can determine which ACS series is being represented by the current table contents.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show population by sex and age by US Congress in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

Attributes and definitions available below under "Attributes" section and in Infrastructure Manifest (due to text box constraints, attributes cannot be displayed here). Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

Abstract

The main aim for the establishment census 2012 is to enumerate all of the economic establishments operating in Palestine in 2012, except for those establishments engaged in farming activities, and building a new updated a classified establishment register according to the geographical distribution, main economic activity according to international recommendations.

The goals for the Establishment Census could be summarized as follows: 1. Distribution of establishments by various economic activities. 2. Distribution of establishments by the Palestinian governorates. 3. The size of employment in various economic activities and its distribution by sex. 4. Distribution establishments in terms of economic organization, legal status, ownership and operation status. 5. The value of capital invested in establishments. 6. Distribution establishments in terms of registration status with the official authorities. 7. The rate of growth in the number of economic establishments.

Geographic coverage

The Establishment Census 2012 includes all of the establishments in Palestine, whether those of the government or international organizations and institutions, non-profit, and establishments engaged in economic activities in the markets or in factories and companies, or those that exercise an economic activity in houses and have the definition of an establishment, with the exception of those establishments engaged in the agriculture, forestry, fishing and animal husbandry

Analysis unit

Establishment

Universe

The establishment considered the statistical unit that the data collection was upon, which is an institution or part of it, which is located in one place and specializes mainly in one major activity (non Assistant) which will bring most of the added value, classified within the same activity (with probability of production of secondary activities) and for which data are available, allowing for calculating of operating surplus account, which provides data for both: workers, and expenses, production and revenue, and fixed assets. An establishment must provide the following requirements: 1.Participation in an economic activity, any establishment should provide good or service to the market. 2.The presence in a fixed place. 3.A holder of an establishment, whether an individual or a legal entity. 4.The presence of a single management of the establishment

Kind of data

Census/enumeration data [cen]

Sampling procedure

comprehensive census of all economic establishment in palestine

Mode of data collection

Face-to-face [f2f]

Research instrument

The establishment Census form consisted of two sections:

Part one: Identification data, which included basic information about the establishments, governorate, Locality, number of enumeration area, Building No. in the enumeration area, serial number of establishment in the enumeration area, establishment commercial name, name of holder or director, sex of the holder or director, phone number, location and description, including the name of the neighborhood and the street and the name of the building or the owner of the building, and the working status of the establishment.

Part two: data on operating establishments only, which include: (Description of the main economic activity, Ownership, Economic organization, Legal status, Establishment Year, Number of employees, Preparing of accounting records, Licensing and registration, No. workers, present value of capital, owner Identity No. or director of the of establishment).

Special form for Jerusalem Governorate area (J1) Due to the special situation in the Jerusalem governorate, specially J1 area (those parts of Jerusalem which were annexed by Israel in 1967) a short form for census questionnaire has been designed, which include the following questions: (Identification data for the establishment, working status, main economic activity, ownership of establishment, economic organization, establishment year, the number of employees in the establishment (paid, unpaid)).

Cleaning operations

The data processing stage includes editing, coding, data entry, reviewing lists and checking all previous operations of data entry for all enumeration areas. All procedures and instructions were conducted to check the consistency of the data and coding fields and ensure the entry of all enumeration areas and booklets and questionnaires, with their content of establishment data. As booklets and questionnaires required checking and moving from one operation site to another, a store was prepared for all the documents to be indexed and categorized and the store keeper controlled the flow of documents.

Coding manuals were prepared and examined beforehand, as well as the instructions for editing and coding procedures to check the consistency of the data and how to detect and correct errors. All editing and coding employees were selected from among the best fieldworkers who collected the data from establishments owners or manager. Training was conducted centrally to ensure uniform concepts and to eliminate disparities in fieldwork in all governorates. Editing, coding and testing the consistency of 100% of the questionnaires was conducted, in addition to desk reviewing, editing and coding (100%) in order to eliminate differences between individual editors and to discover and correct errors and circulate them daily.

Tests were held for all applicants for data entry and those who performed best were trained centrally in a uniform procedure of data entry. During the first three days, all date entered were deleted and re-entered again to correct errors and inform employees so as to avoid such errors in the future. Certain procedures were adopted to ensure correct data entry: in the first stage a unique separate file was prepared for each enumeration area that included identification data (to ensure coverage), the number of establishments and the total number of booklets to ensure that all booklets and all households had been entered. Upon data entry, a thorough examination of the identification data and the range of each digital key question was conducted so that the computer did not accept any figure outside this range. For example, the operation status, sex and all the pre-coded questions in the establishment questionnaire, and the type of building in the buildings questionnaire. The remaining questions were exposed to a comprehensive re-examination of the range of each question after data entry and the extraction of error lists resulting from data inconsistency.

After data entry, certain lists were extracted to ensure the coverage of all enumeration areas, and establishments, and to examine the internal consistency of the data of each unit. The procedures used were to extract error lists that must be corrected or questioned These lists were submitted to the best reviewers under full supervision of the technical operations in the census directorate.

Specific programs previously prepared were used to detect errors according to the following procedures: 1. An instruction manual was prepared for desk editing and procedures for the establishments' questionnaire. A set of desk editing instructions were printed and the procedures for the questionnaire containing tests designed to ensure the coverage of data entry, to detect inconsistencies or to detect abnormal and rare cases. These were reviewed and printed with a name and number given to every error in the manual. 2. A list was extracted for each enumeration area, including the identification data of each establishment message (type of check) and the number printed in the manual. The auditor could then recognize the message name and type of error, location and procedures of editing and audit procedures patch, which consists of several checks on several stages. 3. Lists were submitted to the reviewers to return to the original booklets. If the error was caused by data entry, it would be corrected on the list. If the error was due to fieldwork, all associated questions should be considered for correction. For example, if the operation status of the establishment was closed, it must be no answers on the questions after it. The first check would be conducted through manual editing, then extracting the electronic lists after data entry for such types of tests, then they would be corrected manually on the original booklets and data re-entered correctly. As for the coverage test, there is a key reference that contains all enumeration areas and shows the number of booklets and establishments in the enumeration area to be entered on the computer. At this point, if there was a variation between the number of booklets and establishments actually entered and the total number of establishments in the file of each area, an error message appears to request correction. Through this method, we ensured that 100% of the establishments were entered.

All lists for the enumeration areas were extracted in this way and all kinds of tests. 1. Amended lists were sent back to data entry to be entered and corrected and a copy of the daily entered data was kept in several different places. 2. Previous stages were conducted twice or more until the data of each enumeration area became clean. 3. All files were compiled for enumeration areas for each locality and governorate. Then, all tables and any additional tests were conducted to test the data before the final tabulation in order to correct errors according to the aforementioned procedures.

Sampling error estimates

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Data appraisal

There are two types of error that can occur: statistical errors and