While the standard image of the nuclear family with two parents and 2.5 children has persisted in the American imagination, the number of births in the U.S. has steadily been decreasing since 1990, with about 3.67 million babies born in 2022. In 1990, this figure was 4.16 million. Birth and replacement rates A country’s birth rate is defined as the number of live births per 1,000 inhabitants, and it is this particularly important number that has been decreasing over the past few decades. The declining birth rate is not solely an American problem, with EU member states showing comparable rates to the U.S. Additionally, each country has what is called a “replacement rate.” The replacement rate is the rate of fertility needed to keep a population stable when compared with the death rate. In the U.S., the fertility rate needed to keep the population stable is around 2.1 children per woman, but this figure was at 1.67 in 2022. Falling birth rates Currently, there is much discussion as to what exactly is causing the birth rate to decrease in the United States. There seem to be several factors in play, including longer life expectancies, financial concerns (such as the economic crisis of 2008), and an increased focus on careers, all of which are causing people to wait longer to start a family. How international governments will handle falling populations remains to be seen, but what is clear is that the declining birth rate is a multifaceted problem without an easy solution.

Niger had the highest birth rate in the world in 2024, with a birth rate of 46.6 births per 1,000 inhabitants. Angola, Benin, Mali, and Uganda followed. Except for Afghanistan, all the 20 countries with the highest birth rates in the world were located in Sub-Saharan Africa. High infant mortality The reasons behind the high birth rates in many Sub-Saharan African countries are manyfold, but a major reason is that infant mortality remains high on the continent, despite decreasing steadily over the past decades, resulting in high birth rates to counter death rates. Moreover, many nations in Sub-Saharan Africa are highly reliant on small-scale farming, meaning that more hands are of importance. Additionally, polygamy is not uncommon in the region, and having many children is often seen as a symbol of status. Fastest growing populations As the high fertility rates coincide with decreasing death rates, countries in Sub-Saharan Africa have the highest population growth rates in the world. As a result, with Africa's population forecast to increase from 1.4 billion in 2022 to over 3.9 billion by 2100.

Chart and table of the World birth rate from 1950 to 2025. United Nations projections are also included through the year 2100.

The variables included in this dataset are for the census usually resident population count (unless otherwise stated). All data is for level 1 of the classification (unless otherwise stated).The variables for part 1 of the dataset are:Census usually resident population countCensus night population countAge (5-year groups)Age (life cycle groups)Median ageBirthplace (NZ born/overseas born)Birthplace (broad geographic areas)Ethnicity (total responses) for level 1 and ‘Other Ethnicity’ grouped by ‘New Zealander’ and ‘Other Ethnicity nec’Māori descent indicatorLanguages spoken (total responses)Official language indicatorGenderCisgender and transgender status – census usually resident population count aged 15 years and overSex at birthRainbow/LGBTIQ+ indicator for the census usually resident population count aged 15 years and overSexual identity for the census usually resident population count aged 15 years and overLegally registered relationship status for the census usually resident population count aged 15 years and overPartnership status in current relationship for the census usually resident population count aged 15 years and overNumber of children born for the sex at birth female census usually resident population count aged 15 years and overAverage number of children born for the sex at birth female census usually resident population count aged 15 years and overReligious affiliation (total responses)Cigarette smoking behaviour for the census usually resident population count aged 15 years and overDisability indicator for the census usually resident population count aged 5 years and overDifficulty communicating for the census usually resident population count aged 5 years and overDifficulty hearing for the census usually resident population count aged 5 years and overDifficulty remembering or concentrating for the census usually resident population count aged 5 years and overDifficulty seeing for the census usually resident population count aged 5 years and overDifficulty walking for the census usually resident population count aged 5 years and overDifficulty washing for the census usually resident population count aged 5 years and over.The variables for part 2 of the dataset are:Individual home ownership for the census usually resident population count aged 15 years and overUsual residence 1 year ago indicatorUsual residence 5 years ago indicatorYears at usual residenceAverage years at usual residenceYears since arrival in New Zealand for the overseas-born census usually resident population countAverage years since arrival in New Zealand for the overseas-born census usually resident population countStudy participationMain means of travel to education, by usual residence address for the census usually resident population who are studyingMain means of travel to education, by education address for the census usually resident population who are studyingHighest qualification for the census usually resident population count aged 15 years and overPost-school qualification in New Zealand indicator for the census usually resident population count aged 15 years and overHighest secondary school qualification for the census usually resident population count aged 15 years and overPost-school qualification level of attainment for the census usually resident population count aged 15 years and overSources of personal income (total responses) for the census usually resident population count aged 15 years and overTotal personal income for the census usually resident population count aged 15 years and overMedian ($) total personal income for the census usually resident population count aged 15 years and overWork and labour force status for the census usually resident population count aged 15 years and overJob search methods (total responses) for the unemployed census usually resident population count aged 15 years and overStatus in employment for the employed census usually resident population count aged 15 years and overUnpaid activities (total responses) for the census usually resident population count aged 15 years and overHours worked in employment per week for the employed census usually resident population count aged 15 years and overAverage hours worked in employment per week for the employed census usually resident population count aged 15 years and overIndustry, by usual residence address for the employed census usually resident population count aged 15 years and overIndustry, by workplace address for the employed census usually resident population count aged 15 years and overOccupation, by usual residence address for the employed census usually resident population count aged 15 years and overOccupation, by workplace address for the employed census usually resident population count aged 15 years and overMain means of travel to work, by usual residence address for the employed census usually resident population count aged 15 years and overMain means of travel to work, by workplace address for the employed census usually resident population count aged 15 years and overSector of ownership for the employed census usually resident population count aged 15 years and overIndividual unit data source.Download lookup file for part 1 from Stats NZ ArcGIS Online or Stats NZ geographic data service.Download lookup file for part 2 from Stats NZ ArcGIS Online or Stats NZ geographic data service.FootnotesTe Whata Under the Mana Ōrite Relationship Agreement, Te Kāhui Raraunga (TKR) will be publishing Māori descent and iwi affiliation data from the 2023 Census in partnership with Stats NZ. This will be available on Te Whata, a TKR platform.Geographical boundaries Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018. Subnational census usually resident population The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. Population counts Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. Caution using time series Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data). Study participation time seriesIn the 2013 Census study participation was only collected for the census usually resident population count aged 15 years and over.About the 2023 Census dataset For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings. Data quality The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.Concept descriptions and quality ratingsData quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.Disability indicatorThis data should not be used as an official measure of disability prevalence. Disability prevalence estimates are only available from the 2023 Household Disability Survey. Household Disability Survey 2023: Final content has more information about the survey.Activity limitations are measured using the Washington Group Short Set (WGSS). The WGSS asks about six basic activities that a person might have difficulty with: seeing, hearing, walking or climbing stairs, remembering or concentrating, washing all over or dressing, and communicating. A person was classified as disabled in the 2023 Census if there was at least one of these activities that they had a lot of difficulty with or could not do at all.Using data for good Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.Confidentiality The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3)

The COVID-19 pandemic resulted in an increase in the global death rate, but had little to no significant impact on birth rates, causing population growth to dip slightly. On a global level, population growth is determined by the difference between the birth and death rate, and this is known as the rate of natural change - on a national or regional level, population change is also affected by migration. Ongoing trends Since the middle of the 20th century, the global birth rate has been well above the global death rate, however, the gap between these figures has grown closer in recent years. The death rate is projected to overtake the birth rate in the 2080s, which means that the world's population will then go into decline. In the future, death rates will increase due to ageing populations across the world and a plateau in life expectancy. Why does this change? There are many reasons for falling death and birth rates in recent decades. Falling death rates have been driven by a reduction in infant and child mortality, as well as increased life expectancy. Falling birth rates were also driven by the reduction in child mortality, whereby mothers would have fewer children as survival rates rose - other factors include the drop in child marriage, improved contraception access and efficacy, and women choosing to have children later in life.

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 IPUMS 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

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 1940 census data was collected in April 1940. 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 SPLIT40, reconstructed using the variable SERIAL40, and the original count is found in the variable NUMPREC40.
• Some variables are missing from this data set for specific enumeration districts. The enumeration districts with missing data can be identified using the variable EDMISS. These variables will be added in a future release.
• Coded variables derived from string variables are still in progress. These variables include: occupation, industry and migration status.
• Missing observations have been allocated and some inconsistencies have been edited for the following variables: Missing observations have been allocated and some inconsistencies have been edited for the following variables: SURSIM, SEX, SCHOOL, RELATE, RACE, OCC1950, MTONGUE, MBPL, FBPL, BPL, MARST, EMPSTAT, CITIZEN, OWNERSHP. 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 edited for this release, thus there are observations outside of the universe for many variables. In particular, the variables GQ, and GQTYPE have known inconsistencies and will be improved with the next r

Number and percentage of live births, by month of birth, 1991 to most recent year.

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.

With Vanuatu, as many of her Pacific island neighbours increasingly embracing a culture of informed, or evidence-based policy development and decision-making, national census databases, and the possibility to extract complex cross-tabulations as well as a host of important sub-regional and small-area relevant information, are essential to feed a growing demand for data and information in both public and private sectors.

Educational, health and manpower planning, for example, including assessments of future demands for staffing, facilities, and programmed budgets, would not be possible without periodic censuses, and Government efforts to monitor development progress, such as in the context of its Millennium Development Goal (MDG) commitments, would also suffer greatly, if not be outright impossible, without reliable data provided by regular national population counts and updates.

While regular national-level surveys, such as Household Income and Expenditure Surveys, Labour force surveys, agriculture surveys and demographic and health surveys - to name but just a few - provide important data and information across specific sectors, these surveys could not be sustained or managed without a national sampling frame (which a census data provides). And the calculation and measurement of all population-based development indicators, such as most MDG indicators, would not be possible without up-to-date population statistics, which usually come from a census or from projections and estimates that are based on census data.

With most of this information now already 9 years old (and thus quite outdated), and in the absence of reliable population-register type databases, such as those provided from well-functional civil registration (births and deaths) and migration-recording systems, the 2009 Vanuatu census of population and housing, will provide much needed demographic, social and economic statistics that are essential for policy development, national development planning, and the regular monitoring of development progress.

Apart from achieving its general aims and objectives in delivering updated population, social and economic statistics, the 2009 census also represented a major national capacity building exercise, with most Vanuatu National Statistics Office (VNSO) staff who were involved with the census, having no prior census experience. Having been carefully planned and resourced, all 2009 census activities have potentially provided very useful (and desired) on-the-job-training for VNSO staff, right across the spectrum of professional rank and responsibilities. It also provided for short-term overseas training and professional attachments (at SPC or ABS, or elsewhere) for a limited number of professional staff, who subsequently mentored other staff in the Vanuatu National Statistics Office (VNSO).

With some key senior VNSO members involved with the 1999 census, they provided a wealth of experience that was available in-house and not to mention the ongoing surveys such HIES and Agriculture Census that the office has conducted before the census proper. The VNSO has also professional officers who have qualified in the fields of Population and Demography who had manned the project, and with this type of resources, we managed to conduct yet another successful project of the 2009 census.

While some short-term census advisory missions were fielded from SPC Demography/ Population programme staff, standard SPC technical assistance policy arrangements could not cater for long-term, or repeated in-country assignments. However, other relevant donors were invited for the longer-term attachments of TA expertise to the VNSO.

Geographic coverage

The 2009 Population and Housing Census Geographical Coverage included:

• National (Vanuatu)
• Provinces (Torba, Sanma, Penama, Malampa, Shefa, tafea)
• Inhabited Islands (From Hiu, Torres Islands to Aneityum, Southern Islands)
• Ennumeration Areas (EA assigned to each enumerator)
• Villages / Towns
• Household or Dwelling

Analysis unit

The Unit Analysis of the 2009 Population and Housing Census included: - Household - Person (Population)

Universe

The census covered all households and individuals throguhout Vanuatu

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire basically has 5 sections; the geographical identifiers, the general population questions and education, labour force questions, the women and fertility questions and the housing questions.The geographical identifiers include the Village name, GPS code, EA number, household number and the Enumerator ID.The Person questions contain the person demographics including the education level and labour force status. A section on fertility for women in the reproductive age is also included. All have been guided by 'skip patterns' to guide the flow of questions asked.Household questions contained the basic description of the house materials, tenure, access to water and sanitation, energy, durables, use of treated mosquito nest and internet access.

Cleaning operations

In the Census proper, the Optical Character Recognition (OCR) system (ReadSoft Application System) was used to capture information from the completed forms. The captured data were then exported to MS Access database system for further editing and cleaning before the final data is transferred to CSPro for more editing and quality checks before the data was finalised. All system files and data files were stored in the server under 2009PopCensus folder. Three temporary data operators were hired to do the job, under the supervision of Rara Soro, the system analyst for VNSO. No data was stored in work stations, because all data were directly written to the DATA folder in the server.

Range checks and basic checks (online edits) were built in the manual data entry system, while the complex edits were written in a separate batch edit program. If the system encounter and error during data entry, an error message will be displayed and the data operator cannot proceed unless the error displayed is fixed. e.g Males + Females = Total Persons. Please re-enter. It was strongly recommended to the data operators not to make up answers but consult the supervisor if he/she cannot fix it. Listed below are the checks that were built into the data entry system.

01 Person 1 must be the head of household 02 Sex against relationship 03 Age against date of birth 04 Marital status - Married people should be age 15+ 05 Spouse should be married 06 P9, P10, P11 against village enumerated 07 Never been to school but can use internet - Is this possible 08 Check for multiple head or spouse in the household 09 Husband and wife of same sex 10 Total persons match total people in personal form 11 Total children born and live in household (F2a) against total persons total 12 Age difference of head and child is less than 13 13 Total children born (F4) against total alive(F2) + total died(F3)

A separate batch edit program was developed for further data cleaning. All online edits were also re-written in this program to make sure that all errors flagged out during data entry were fixed. Some of the errors detected are not really errors, but still requires double checking, and if the answer recorded is the correct answer, don't change it. The batch edit was performed on each batch, and also on the concatenated batch. Below is the summary list of errors generated from manual data entry data before batch editing.

MDE Error message summary
Age does not match date of birth 272 Total children born and living in household (F2a) > total in 1
Attend school full-time in P12 but also working 16
Too young for highest education recorded 14
Highest education completed does not match with grade currently attending 80

Age had the highest errors rate, and this is due to an error in the logic statement, otherwise all ages that do not match their date of birth are corrected during data entry.

The Data capturing (Scanning) and Editing process took about 6 months to be completed but then more checks were made after that to finalise the dataset before publishing the results.

During re-coding of zero's and blanks, a couple of batch edit statement written in the batch edit program were wrong, and it created errors in the scanned data. The batch edit was suppose to recode only those people that didn't answer questions P19, P23 - P25, but instead it recoded valid codes as well to blanks. This was only picked up when tables were generated and numbers were found to be so much different in manual data entry and scanned data. Another batch edit program was developed to recode and fix this problem.

Data appraisal

Household characteristics and basic demographic variables for the census data was used in comparision with the 1999 census data to determine the accuracy of the pilot data. Some of the key indicators used for comparision are the household size, sex ratio, educational attainment, employment status. A pyramid was also used

Title VI of the Civil Rights Act and the Executive Order on Environmental Justice (#12898) do not provide specific guidance to evaluate EJ issues within a region's transportation planning process. Therefore, MPOs must devise their own methods for ensuring that EJ issues are investigated and evaluated in transportation decision-making. In 2001, DVRPC developed an EJ technical assessment to identify direct and disparate impacts of its plans, programs, and planning process on defined population groups in the Delaware Valley region. This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:YouthOlder AdultsFemaleRacial MinorityEthnic MinorityForeign-BornDisabledLimited English ProficiencyLow-IncomeCensus tables used to gather data from the 2018-2022 American Community Survey 5-Year EstimatesUsing U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group.Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates.For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipdSource of tract boundaries: 2020 US Census Bureau, TIGER/Line ShapefilesNote: Tracts with null values should be symbolized as "Insufficient or No Data".Data Dictionary for Attributes:(Source = DVRPC indicates a calculated field)FieldAliasDescriptionSourceyearIPD analysis yearDVRPCgeoid2011-digit tract GEOIDCensus tract identifierACS 5-yearstatefp2-digit state GEOIDFIPS Code for StateACS 5-yearcountyfp3-digit county GEOIDFIPS Code for CountyACS 5-yeartractceTract numberTract NumberACS 5-yearnameTract numberCensus tract identifier with decimal placesACS 5-yearnamelsadTract nameCensus tract name with decimal placesACS 5-yeard_classDisabled percentile classClassification of tract's disabled percentage as: well below average, below average, average, above average, or well above averagecalculatedd_estDisabled count estimateEstimated count of disabled populationACS 5-yeard_est_moeDisabled count margin of errorMargin of error for estimated count of disabled populationACS 5-yeard_pctDisabled percent estimateEstimated percentage of disabled populationACS 5-yeard_pct_moeDisabled percent margin of errorMargin of error for percentage of disabled populationACS 5-yeard_pctileDisabled percentileTract's regional percentile for percentage disabledcalculatedd_scoreDisabled percentile scoreCorresponding numeric score for tract's disabled classification: 0, 1, 2, 3, 4calculatedem_classEthnic minority percentile classClassification of tract's Hispanic/Latino percentage as: well below average, below average, average, above average, or well above averagecalculatedem_estEthnic minority count estimateEstimated count of Hispanic/Latino populationACS 5-yearem_est_moeEthnic minority count margin of errorMargin of error for estimated count of Hispanic/Latino populationACS 5-yearem_pctEthnic minority percent estimateEstimated percentage of Hispanic/Latino populationcalculatedem_pct_moeEthnic minority percent margin of errorMargin of error for percentage of Hispanic/Latino populationcalculatedem_pctileEthnic minority percentileTract's regional percentile for percentage Hispanic/Latinocalculatedem_scoreEthnic minority percentile scoreCorresponding numeric score for tract's Hispanic/Latino classification: 0, 1, 2, 3, 4calculatedf_classFemale percentile classClassification of tract's female percentage as: well below average, below average, average, above average, or well above averagecalculatedf_estFemale count estimateEstimated count of female populationACS 5-yearf_est_moeFemale count margin of errorMargin of error for estimated count of female populationACS 5-yearf_pctFemale percent estimateEstimated percentage of female populationACS 5-yearf_pct_moeFemale percent margin of errorMargin of error for percentage of female populationACS 5-yearf_pctileFemale percentileTract's regional percentile for percentage femalecalculatedf_scoreFemale percentile scoreCorresponding numeric score for tract's female classification: 0, 1, 2, 3, 4calculatedfb_classForeign-born percentile classClassification of tract's foreign born percentage as: well below average, below average, average, above average, or well above averagecalculatedfb_estForeign-born count estimateEstimated count of foreign born populationACS 5-yearfb_est_moeForeign-born count margin of errorMargin of error for estimated count of foreign born populationACS 5-yearfb_pctForeign-born percent estimateEstimated percentage of foreign born populationcalculatedfb_pct_moeForeign-born percent margin of errorMargin of error for percentage of foreign born populationcalculatedfb_pctileForeign-born percentileTract's regional percentile for percentage foreign borncalculatedfb_scoreForeign-born percentile scoreCorresponding numeric score for tract's foreign born classification: 0, 1, 2, 3, 4calculatedle_classLimited English proficiency percentile classClassification of tract's limited english proficiency percentage as: well below average, below average, average, above average, or well above averagecalculatedle_estLimited English proficiency count estimateEstimated count of limited english proficiency populationACS 5-yearle_est_moeLimited English proficiency count margin of errorMargin of error for estimated count of limited english proficiency populationACS 5-yearle_pctLimited English proficiency percent estimateEstimated percentage of limited english proficiency populationACS 5-yearle_pct_moeLimited English proficiency percent margin of errorMargin of error for percentage of limited english proficiency populationACS 5-yearle_pctileLimited English proficiency percentileTract's regional percentile for percentage limited english proficiencycalculatedle_scoreLimited English proficiency percentile scoreCorresponding numeric score for tract's limited english proficiency classification: 0, 1, 2, 3, 4calculatedli_classLow-income percentile classClassification of tract's low income percentage as: well below average, below average, average, above average, or well above averagecalculatedli_estLow-income count estimateEstimated count of low income (below 200% of poverty level) populationACS 5-yearli_est_moeLow-income count margin of errorMargin of error for estimated count of low income populationACS 5-yearli_pctLow-income percent estimateEstimated percentage of low income (below 200% of poverty level) populationcalculatedli_pct_moeLow-income percent margin of errorMargin of error for percentage of low income populationcalculatedli_pctileLow-income percentileTract's regional percentile for percentage low incomecalculatedli_scoreLow-income percentile scoreCorresponding numeric score for tract's low income classification: 0, 1, 2, 3, 4calculatedoa_classOlder adult percentile classClassification of tract's older adult percentage as: well below average, below average, average, above average, or well above averagecalculatedoa_estOlder adult count estimateEstimated count of older adult population (65 years or older)ACS 5-yearoa_est_moeOlder adult count margin of errorMargin of error for estimated count of older adult populationACS 5-yearoa_pctOlder adult percent estimateEstimated percentage of older adult population (65 years or older)ACS 5-yearoa_pct_moeOlder adult percent margin of errorMargin of error for percentage of older adult populationACS 5-yearoa_pctileOlder adult percentileTract's regional percentile for percentage older adultcalculatedoa_scoreOlder adult percentile scoreCorresponding numeric score for tract's older adult classification: 0, 1, 2, 3, 4calculatedrm_classRacial minority percentile classClassification of tract's non-white percentage as: well below average, below average, average, above average, or well above averagecalculatedrm_estRacial minority count estimateEstimated count of non-white populationACS 5-yearrm_est_moeRacial minority count margin of errorMargin of error for estimated count of non-white populationACS 5-yearrm_pctRacial minority percent estimateEstimated percentage of non-white populationcalculatedrm_pct_moeRacial minority percent margin of errorMargin of error for percentage of non-white populationcalculatedrm_pctileRacial minority percentileTract's regional percentile for percentage non-whitecalculatedrm_scoreRacial minority percentile scoreCorresponding numeric score for tract's non-white classification: 0, 1, 2, 3, 4calculatedtot_ppTotal population estimateEstimated total population of tract (universe [or denominator] for youth, older adult, female, racial minoriry, ethnic minority, & foreign born)ACS 5-yeartot_pp_moeTotal population margin of errorMargin of error for estimated total population of tractACS 5-yeary_classYouth percentile classClassification of tract's youth percentage as: well below average, below average, average, above average, or well above averagecalculatedy_estYouth count estimateEstimated count of youth population (under 18 years)ACS 5-yeary_est_moeYouth count margin of errorMargin of error for estimated count of youth populationACS 5-yeary_pctYouth population percentage estimateEstimated percentage of youth population (under 18 years)calculatedy_pct_moeYouth population percentage margin of

Abstract

The objective of the Population and housing census 1989 is to provide comprehensive and basic statistical data required to study changes in economic, social and demographic status of Mongolia for the last 10 years and its reasons and determinants, to plan economic and formulate state policies to implement such planned measures and make researches and analysis.

Geographic coverage

All aimags, cities, soums, khoroos, districts and local cities.

Analysis unit

-Individuals -Families/households -Houses

Universe

By 00.00 hours of 4-5th of January, 1989 the following population shall be counted: - people who permanently reside in a household whether they are present or absent (the people who are described by Articles 109 and 110 of Mongolia_1989_Census_Enumerator_Manual); - people temporarily residing in a household or flats or dormitories despite having jurisdiction in a different administrative unit (i.e people who are described by Articles 109 and 110 of Mongolia_1989_Census_Enumerator_Manual); - all children who were born prior to the monitoring period whether they were registered into citizen's family organization or not; people who passed away during the census period after the monitoring period; (therefore, children who were born prior to the monitoring period and people who passed away prior to the monitoring period shall not be counted); - people who are being present in a household or flats or dormitories but unable to specifically name a place of his/her permanent residence. - People who have not yet registered in the administrative unit they moved in or people who have been living in a Soum or District or Khoroo more than 6 months shall be counted as the permanent residence of the place. - Students of Universities or Institutes or colleges or schools or courses (of more 6 months) shall be counted as permanent residents of an administrative unit on the territory of which the Universities or Institutes or colleges or schools or courses (of more 6 months) are located at. -The Population Census Commission established by The Ministry of Defense and The Ministry of State Security shall count soldiers and students of Military Schools. However, Generals or officers or sergeants, who are staying on or Police Officers, or Firemen or Supervisors who are working for The Ministry of Defense and The Ministry of State Security or organizations under their jurisdiction, shall be counted as citizens in the place of their respective residences. - Citizens of the People’s Republic of Mongolia who is living abroad shall be counted by their respective Census Commission established either Embassy of Mongolia or Diplomatic Corp. -The Foreign Ministry of the People’s Republic of Mongolia shall count foreign citizens and their family members who are working at invitation (of government or other bodies) as well as foreign citizens who are visiting at their invitation. Foreign citizens who entered into the territory of Mongolia before 00.00 hours of the 4-5 January, 1989 on transit visit shall be counted. However, foreign citizens who entered into the territory of Mongolia after the monitoring period (i.eafter 00.00 hours of the 4-5 January, 1989 on transit visit shall be not be counted, but Mongolia citizens shall be counted. - Census Commission established at the Ministry of State Security shall count people who are in jail. However, people who are being detained for petty crimes for up to 30 days shall be double counted by the Commission at to be temporarily residing in their respective administrative units of their place of detention as well as by the enumerators of their residential administrative unit as to be absentees. - Foreign citizens permanently residing in the territory of the People’s Republic of Mongolia or the people accompanied them or people without citizenship shall be counted as citizens of the country.

Kind of data

Census/enumeration data [cen]

Sampling procedure

None

Sampling deviation

None reported

Mode of data collection

Face-to-face [f2f]

Research instrument

The PHC 1989 questionnaire consists of questions on household and housing characteristics.

Census questionnaire includes: - Household address - Relationship to household head - Sex - Date of birth - Ethnicity - Citizenship - Place of work and place of studying - Occupation - Social origin - Social group - Income Source - Education - Profession - Marital status - Number of children ever born

Household questionnaire includes: - Household size - Age of household members - Sex of household members - Social group of household members - Marital status of household members - Type of activities at place of work of household members - Source of household income

Housing questionnaire includes: - House characteristics - Ger characteristics - Summer house characteristics

Population and Housing Census 1989 questionnaires and Enumerator_Manual are provided as external resources.

Cleaning operations

The Dataset in this documentation is not a dataset created at the time of the census since the full database of the census is not available anymore. Therefore, the NSO re-entried data of 42783 households and 190631 persons (10 percent of all of population in 1989) from the completed questionnaires that had archived in the National Center Archives in 2007.

Response rate

None reported

Sampling error estimates

Information is not available.

SA4 based data for Status in Employment by Country of Birth of Person by Sex, in Working Population Profile (WPP), 2016 Census. Count of employed persons aged 15 years and over. W05 is broken up …Show full descriptionSA4 based data for Status in Employment by Country of Birth of Person by Sex, in Working Population Profile (WPP), 2016 Census. Count of employed persons aged 15 years and over. W05 is broken up into 4 sections (W05a - W05d), this section contains 'Females Sri Lanka Employee' - 'Persons Pakistan Total'. The data is by SA4 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

SA2 based data for Ancestry by Country of Birth of Parents, in Place of Enumeration Profile (PEP), 2016 Census. Count of responses and persons (excluding overseas visitors) in the following categories with corresponding ancestry: both parents born overseas, father born overseas, mother born overseas, both parents born in Australia, parents birthplace not stated. The list of ancestries consists of the most common 30 Ancestry responses reported in the 2011 Census. This is a multi-response dataset and therefore the total responses count will not equal the total persons count. If two responses from one person are categorised in the 'Other' category only one response is counted. If either or both parents birthplace is not stated then a single response is tallied in the 'not stated' category. The data is by SA2 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.

GCCSA based data for Country of Birth of Person by Year of Arrival in Australia, in General Community Profile (GCP), 2016 Census. Count of persons born overseas. G10 is broken up into 3 sections …Show full descriptionGCCSA based data for Country of Birth of Person by Year of Arrival in Australia, in General Community Profile (GCP), 2016 Census. Count of persons born overseas. G10 is broken up into 3 sections (G10a - G10c), this section contains 'Ireland Year of arrival 2011' - 'Singapore Total'. Where arrival is stated as the year 2016 it corresponds to the period 1 January 2016 to 9 August 2016. The list of countries consists of the most common Country of Birth responses (excluding Australia) reported in the 2011 Census. The data is by GCCSA 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

LGA based data for Status in Employment by Country of Birth of Person by Sex, in Working Population Profile (WPP), 2016 Census. Count of employed persons aged 15 years and over. W05 is broken up …Show full descriptionLGA based data for Status in Employment by Country of Birth of Person by Sex, in Working Population Profile (WPP), 2016 Census. Count of employed persons aged 15 years and over. W05 is broken up into 4 sections (W05a - W05d), this section contains 'Females Sri Lanka Employee' - 'Persons Pakistan Total'. The data is by LGA 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

SA1 based data for Country of Birth of Person by Age by Sex, in General Community Profile (GCP), 2016 Census. Count of persons. G09 is broken up into 8 sections (G09a - G09h), this section contains …Show full descriptionSA1 based data for Country of Birth of Person by Age by Sex, in General Community Profile (GCP), 2016 Census. Count of persons. G09 is broken up into 8 sections (G09a - G09h), this section contains 'Males Afghanistan Age 0-4 years' - 'Males Iraq Total'. The list of countries consists of the 50 most common Country of Birth responses reported in the 2011 Census. The data is by SA1 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

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.

With Vanuatu, as many of her Pacific island neighbours increasingly embracing a culture of informed, or evidence-based policy development and decision-making, national census databases, and the possibility to extract complex cross-tabulations as well as a host of important sub-regional and small-area relevant information, are essential to feed a growing demand for data and information in both public and private sectors.

Educational, health and manpower planning, for example, including assessments of future demands for staffing, facilities, and programmed budgets, would not be possible without periodic censuses, and Government efforts to monitor development progress, such as in the context of its Millennium Development Goal (MDG) commitments, would also suffer greatly, if not be outright impossible, without reliable data provided by regular national population counts and updates.

While regular national-level surveys, such as Household Income and Expenditure Surveys, Labour force surveys, agriculture surveys and demographic and health surveys - to name but just a few - provide important data and information across specific sectors, these surveys could not be sustained or managed without a national sampling frame (which a census data provides). And the calculation and measurement of all population-based development indicators, such as most MDG indicators, would not be possible without up-to-date population statistics, which usually come from a census or from projections and estimates that are based on census data.

With most of this information now already 9 years old (and thus quite outdated), and in the absence of reliable population-register type databases, such as those provided from well-functional civil registration (births and deaths) and migration-recording systems, the 2009 Vanuatu census of population and housing, will provide much needed demographic, social and economic statistics that are essential for policy development, national development planning, and the regular monitoring of development progress.

Apart from achieving its general aims and objectives in delivering updated population, social and economic statistics, the 2009 census also represented a major national capacity building exercise, with most Vanuatu National Statistics Office (VNSO) staff who were involved with the census, having no prior census experience. Having been carefully planned and resourced, all 2009 census activities have potentially provided very useful (and desired) on-the-job-training for VNSO staff, right across the spectrum of professional rank and responsibilities. It also provided for short-term overseas training and professional attachments (at SPC or ABS, or elsewhere) for a limited number of professional staff, who subsequently mentored other staff in the Vanuatu National Statistics Office (VNSO).

With some key senior VNSO members involved with the 1999 census, provides a wealth of experience that was available in-house and not to mention the ongoing surveys such HIES and Agriculture Census that the office has conducted before the census proper. The VNSO has also professional officers who have qualified in the fields of Population and Demography who had manned the project, and with this type of resources, we managed to conduct yet another successful project of the 2009 census.

While some short-term census advisory missions were fielded from SPC Demography/ Population programme staff, standard SPC technical assistance policy arrangements could not cater for long-term, or repeated in-country assignments. However, other relevant donors were invited for the longer-term attachments of TA expertise to the VNSO.

Geographic coverage

The 2009 Population and Housing Census Geographical Coverage included:

• National (Vanuatu)
• Provinces (Torba, Sanma, Penama, Malampa, Shefa, tafea)
• Inhabited Islands (From Hiu, Torres Islands to Aneityum, Southern Islands)
• Enumeration Areas (EA assigned to each enumerator)
• Villages / Towns

Analysis unit

The Unit Analysis of the 2009 Population and Housing Census included: - Household - Person (Population)

Universe

The census cover all households and individuals throughout Vanuatu.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not Applicable

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire basically has 5 sections; the geographical identifiers, the general population questions and education, labour force questions, the women and fertility questions and the housing questions.

The geographical identifiers contains the Village name, GPS code, EA number, household number and the Enumerator ID The Person questions contain the person demographics including the education level and labour force status. A section on fertility for women in the reproductive age is also included. all have been guided by 'skips' to guide the flow of questions asked

Household questions contains the basic description of the house materials, tenure, access to water and sanitation, energy, durables, use of treated mosquito nest and internet access.

Cleaning operations

In the Census proper, the Optical Character Recognition (OCR) system (ReadSoft Application System) was used to capture information from the completed forms. The captured data were then exported to MS Access database system for further editing and cleaning before the final data is transferred to CSPro for more editing and quality checks before the data was finalised. All system files and data files were stored in the server under 2009PopCensus folder. Three temporary data operators were hired to do the job, under the supervision of Rara Soro, the system analyst for VNSO. No data was stored in work stations, because all data were directly written to the DATA folder in the server.

Range checks and basic checks (online edits) were built in the manual data entry system, while the complex edits were written in a separate batch edit program. If the system encounter and error during data entry, an error message will be displayed and the data operator cannot proceed unless the error displayed is fixed. e.g Males + Females = Total Persons. Please re-enter. It was strongly recommended to the data operators not to make up answers but consult the supervisor if he/she cannot fix it. Listed below are the checks that were built into the data entry system.

01 Person 1 must be the head of household 02 Sex against relationship 03 Age against date of birth 04 Marital status - Married people should be age 15+ 05 Spouse should be married 06 P9, P10, P11 against village enumerated 07 Never been to school but can use internet - Is this possible 08 Check for multiple head or spouse in the household 09 Husband and wife of same sex 10 Total persons match total people in personal form 11 Total children born and live in household (F2a) against total persons total 12 Age difference of head and child is less than 13 13 Total children born (F4) against total alive(F2) + total died(F3)

A separate batch edit program was developed for further data cleaning. All online edits were also re-written in this program to make sure that all errors flagged out during data entry were fixed. Some of the errors detected are not really errors, but still requires double checking, and if the answer recorded is the correct answer, don't change it. The batch edit was performed on each batch, and also on the concatenated batch. Below is the summary list of errors generated from manual data entry data before batch editing.

MDE Error message summary
Age does not match date of birth 272 Total children born and living in household (F2a) > total in 1
Attend school full-time in P12 but also working 16
Too young for highest education recorded 14
Highest ed completed do not match with grade currently attending 80

Age had the highest errors rate, and this is due to an error in the logic statement, otherwise all ages that do not match their date of birth are corrected during data entry.

The Data capturing (Scanning) and Editing process took about 6 months to be completed but then more checks were made after that to finalise the dataset before publishing the results.

During re-coding of zero's and blanks, a couple of batch edit statement written in the batch edit program were wrong, and it created errors in the scanned data. The batch edit was suppose to recode only those people that didn't answer questions P19, P23 - P25, but instead it recoded valid codes as well to blanks. This was only picked up when tables were generated and numbers were found to be so much different in manual data entry and scanned data. Another batch edit program was developed to recode and fix this problem.

Sampling error estimates

Not Applicable

Data appraisal

Household characteristics and basic demographic variables for the census data was used in comparision with the 1999 census data to determine the accuracy of the pilot data. Some of the key indicators used for comparision are the

BackgroundAccording to the Seventh National Census, China’s fertility rate is less than 1.5, marking a significant national issue with potential risks. To counter this low birth rate, the Chinese government has relaxed family planning policies and introduced supportive measures.PurposeChanges in birth policy have attracted considerable attention from the people of China. This article aims to study the public’s response to the three-child support policy using Weibo as a window. The goal is to provide a more balanced evaluation of current perspectives, enabling policymakers to formulate better fertility information, particularly when anticipating a poor public response to controversial policies.MethodologyThis research uses a crawler to gather data from Sina Weibo. Through opinion mining of Weibo posts on the three-child policy, Weibo users’ online opinions on the three-child policy are analyzed from two perspectives: their attention content and sentiment tendency. Using an interrupted time series, it examines changes in online views on the policy, matching policy documents to the time nodes of Weibo posts.FindingsThe public has shown great interest in and provided short-term positive feedback on policies related to improving maternity insurance, birth rewards, and housing subsidies. In contrast, there has been a continuous negative response to policies such as extending maternity leave, which has particularly sparked concerns among women regarding future employment and marital rights protection. On social media, the public’s attention to the three-child birth policy has focused mainly on the protection of women’s rights, especially legal rights after childbirth, and issues related to physical and mental health. Child-rearing support and economic pressure are also hot topics, involving the daily expenses of multichild families, childcare services, and housing pressure. However, this study also revealed that infertile or single women express a strong desire to have children, but due to limitations in the personal medical insurance system, this desire has not been fully satisfied.ContributionsOur study demonstrates the feasibility of a rapid and flexible method for evaluating the public response to various three-child supportive policies in China using near real-time social media data. This information can help policy makers anticipate public responses to future pandemic three-child policies and ensure that adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed but controversial, restrictions.

SA4 based data for Country of Birth of Person by Year of Arrival in Australia, in Place of Enumeration Profile (PEP), 2016 Census. Count of persons born overseas (excludes overseas visitors). P10 is …Show full descriptionSA4 based data for Country of Birth of Person by Year of Arrival in Australia, in Place of Enumeration Profile (PEP), 2016 Census. Count of persons born overseas (excludes overseas visitors). P10 is broken up into 3 sections (P10a - P10c), this section contains 'South Africa Year of arrival Before 1946' - 'Total Total'. Where arrival is stated as the year 2016 it corresponds to the period 1 January 2016 to 9 August 2016. The list of countries consists of the most common Country of Birth responses (excluding Australia) reported in the 2011 Census. The data is by SA4 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.

As of January 1, 2025, more than 146 million people were estimated to be residing on the Russian territory, down approximately 30,000 from the previous year. From the second half of the 20th century, the population steadily grew until 1995. Furthermore, the population size saw an increase from 2009, getting closer to the 1995 figures. In which regions do most Russians live? With some parts of Russia known for their harsh climate, most people choose regions which offer more comfortable conditions. The largest share of the Russian population, or 40 million, reside in the Central Federal District. Moscow, the capital, is particularly populated, counting nearly 13 million residents. Russia’s population projections Despite having the largest country area worldwide, Russia’s population was predicted to follow a negative trend under both low and medium expectation forecasts. Under the low expectation forecast, the country’s population was expected to drop from 146 million in 2022 to 134 million in 2036. The medium expectation scenario projected a milder drop to 143 million in 2036. The issues of low birth rates and high death rates in Russia are aggravated by the increasing desire to emigrate among young people. In 2023, more than 20 percent of the residents aged 18 to 24 years expressed their willingness to leave Russia.

SA2 based data for Country of Birth of Person by Age by Sex, in General Community Profile (GCP), 2016 Census. Count of persons. G09 is broken up into 8 sections (G09a - G09h), this section contains …Show full descriptionSA2 based data for Country of Birth of Person by Age by Sex, in General Community Profile (GCP), 2016 Census. Count of persons. G09 is broken up into 8 sections (G09a - G09h), this section contains 'Persons Papua New Guinea Age 0-4 years' - 'Persons Total Total'. The list of countries consists of the 50 most common Country of Birth responses reported in the 2011 Census. The data is by SA2 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)