Connecticut Nurses Census 1917

The Connecticut Nurses Census is a part of State Archives https://cslarchives.ctstatelibrary.org/repositories/2/resources/443">Record Group 029: Records of the Military Census Department. The census forms may give basic details such as birthplace, age, marital status, maiden name, and current residence, as well as more specific information such as the name of the nursing school attended, medical specialty, and year of licensure. This census included the registration of both female and male nurses.

This index includes the name, birthplace, age, current residence, form number and box number. If a field is left blank, it is because the person who submitted the form did not answer that question (e.g. age, anybody!) People may request a copy of a census form by contacting us by telephone (860) 757-6580 or email. Please include the name of the individual and form number.

Connecticut Nurses Census 1917 The Connecticut Nurses Census is a part of State Archives Record Group 029: Records of the Military Census Department. The census forms may give basic details such as birthplace, age, marital status, maiden name, and current residence, as well as more specific information such as the name of the nursing school attended, medical specialty, and year of licensure. This census included the registration of both female and male nurses. This index includes the name, birthplace, age, current residence, form number and box number. If a field is left blank, it is because the person who submitted the form did not answer that question (e.g. age, anybody!) People may request a copy of a census form by contacting us by telephone (860) 757-6580 or email. Please include the name of the individual and form number.

This dataset is made up of images containing handwritten 3-digit occupation codes from the Norwegian population census of 1950. The occupation codes were added to the census sheets by Statistics Norway after the census was concluded for the purpose of creating aggregated occupational statistics for the entire population. The coding standard used in the 1950 census is, according to Statistics Norway’s official publications (https://www.ssb.no/historisk-statistikk/folketellinger/folketellingen-1950, booklet 4, page 81), very similar to the standards used in the census for 1920. Cf. the 13th booklet published for the 1920 census (https://www.ssb.no/historisk-statistikk/folketellinger/folketellingen-1920, note that this booklet is only available in Norwegian). In short, an occupation code is a 3-digit number that corresponds to a given occupation or type of occupation. According to the official list of occupation codes provided by Statistics Norway there are 339 unique codes. These are not all necessarily sequential or hierarchical in general, but some subgroupings are. This list can be found under Files. It is also worth noting that these images were extracted from the original census sheet images algorithmically. This process was not flawless and lead to additional images being extracted, these can contain written occupation titles or be left entirely blank. The dataset consists of 90,000 unique images, and 9,000 images that were randomly selected and copied from the unique images. These were all used for a research project (link to preprint article: https://doi.org/10.48550/arXiv.2306.16126) where we (author list can be found in preprint) tried to find a more efficient way of reviewing and correcting classification results from a Machine Learning model, where the results did not pass a pre-set confidence threshold. This was a follow-up to our previous article where we describe the initial project and creating of our model in more detail, if it is of interest (“Lessons Learned Developing and Using a Machine Learning Model to Automatically Transcribe 2.3 Million Handwritten Occupation Codes”, https://doi.org/10.51964/hlcs11331).

Abstract

The primary mission of the 2006 Population and Housing Census (PHC) of Nigeria was to provide data for policy-making, evidence-based planning and good governance. The Government at all tiers, researchers, the academia, civil society organizations and the international agencies will find the sets of socio-demographic data useful in formulating developmental policies and planning. The 2006 data will certainly provide benchmarks for monitoring the Millennium Development Goals (MDGs). Enumeration in the 2006 PHC was conducted between March 21st and 27th 2006. It was designed to collect information on the quality of the population and housing, under the following broad categories: demographic and social, education, disability, household composition, economic activity, migration, housing and amenities, mortality and fertility. The results of the exercise are being released as per the Commission's Tabulation Plan which began with the release of the total enumerated persons by administrative areas in the country in the Official Gazette of the Federal Republic of Nigeria No.2, Vol 96 of February 2,2009 and followed with the release of Priority Tables that provide some detailed characteristics of the population of Nigeria by State and LGA.

Geographic coverage

National

Analysis unit

Individuals Households

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Census 2006 Processing: The Technology and Methodology:-

Unlike the data capture method used for the country’s previous censuses, where information from the census forms are typed into the computer system, data capture for census 2006 was carried out by OMR/OCR/ICR systems where questionnaires are scanned through high speed optical scanners. The choice of the scanning system was because it is faster and more accurate than the data keying method.

OMR/OCR/ICR Technology

Definition of terms

• OMR (Optical Mark Recognition) - This means the ability of the scanning machine to detect pencil marks made on the questionnaires by the Enumerators in accordance with the responses given by the respondents.
• OCR (Optical Character Recognition) - This means the ability of the scanning machine to recognize machine printed characters on the questionnaires.
• ICR (Intelligent Character Recognition) - This means the ability of the scanner to recognize characters hand written by the Enumerators in accordance with the responses given by the respondents.

Processing Procedures of Census 2006 at the DPCs:- Data processing took place in the Commission’s seven (7) Data Processing Centres located in different geographical zones in the country. There was absolute uniformity in the processing procedures in the seven DPCs.

(a) Questionnaire Retrieval/Archiving Questionnaires from the fields were taken directly from the Local Government Areas to designated DPCs. The forms on arrival at the DPCs were counted, archived and labeled. Retrieval of the questionnaires at the DPCs were carried out based on the EA frame received from the Cartography Department. Necessary Transmittal Forms are completed on receipt of the Forms at the DPCs. The Transmittal Forms are also used to keep track of questionnaires movement within the DPC.

(b) Forms Preparation The scanning machine has been designed to handle A4 size paper. And the Census form being twice that size has to be split into two through the dotted lines at the middle of the form. This forms preparation procedure is to get the questionnaires, for each Enumeration Areas (EAs), ready for scanning. There is a Batch Header to identify each batch.

(c) Scanning Each Batch on getting to the Scanning Room was placed on joggers (a vibrating machine)to properly align the forms, and get rid of dust or particles that might be on the forms.

The forms are thereafter fed into the scanner. There were security codes in form of bar codes on each questionnaire to identify its genuineness. There was electronic editing and coding for badly coded or poorly shaded questionnaires by the Data Editors. Torn, stained or mutilated forms are rejected by the scanner. These categories of forms were later manually keyed into the system.

Re-archiving of Scanned Forms:- Scanned forms were placed in their appropriate marked envelopes in batches, and thereafter returned to the Archiving Section for re-archiving.

Data Output from the Scanning Machine:- The OMR/OCR Software interprets the output from the scanner and translates it into an XML file from where it is further translated into the desired ASCII output that is compatible for use by the CSPro Package for further processing and tabulation.

Data back-up and transfer:- After being sure that the data are edited for each EA batch in an LGA, data then was exported to the SAN (Storage Area Network) of the Server. Two copies of images of the questionnaires for each EA copied to the LTO tapes as backup and then transferred to the Headquarters. The ASCII data files for each LGA are zipped and encrypted, and thereafter transfer to the Data Validation Unit (DVU) at the Headquarters in Abuja.

Data appraisal

Data collation and validation:- The Data Validation Unit at the Headquarters was responsible for collating these data into EAs, LGAs, States and National levels. The data are edited/validated for consistency errors and invalid entries. The Census and Survey Processing (CSPro) software is used for this process. The edited, and error free data are thereafter processed into desired tables.

Activities of the Data Validation unit (DVU):-

Decryption of each LGA Data File Concatenation/merging of Data Files Check each EA batch file for EA completeness within an LGA and State Check for File/Data Structure Check for Range and Invalid Data items Check for Blank and empty questionnaire Check for inter and intra record consistency Check for Skip Patterns Perform Data Validation and Imputation Generate Statistics Report of each function/activity Generate Statistical Tables on LGA, State and National levels.

Abstract

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation.The census plays an essential role in public administration. The results are used to ensure: • equity in distribution of government services • distributing and allocating government funds among various regions and districts for education and health services • delineating electoral districts at national and local levels, and • measuring the impact of industrial development, to name a few The census also provides the benchmark for all surveys conducted by the national statistical office. Without the sampling frame derived from the census, the national statistical system would face difficulties in providing reliable official statistics for use by government and the public. Census also provides information on small areas and population groups with minimum sampling errors. This is important, for example, in planning the location of a school or clinic. Census information is also invaluable for use in the private sector for activities such as business planning and market analyses. The information is used as a benchmark in research and analysis.

Census 2011 was the third democratic census to be conducted in South Africa. Census 2011 specific objectives included: - To provide statistics on population, demographic, social, economic and housing characteristics; - To provide a base for the selection of a new sampling frame; - To provide data at lowest geographical level; and - To provide a primary base for the mid-year projections.

Geographic coverage

National

Analysis unit

Households, Individuals

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

About the Questionnaire : Much emphasis has been placed on the need for a population census to help government direct its development programmes, but less has been written about how the census questionnaire is compiled. The main focus of a population and housing census is to take stock and produce a total count of the population without omission or duplication. Another major focus is to be able to provide accurate demographic and socio-economic characteristics pertaining to each individual enumerated. Apart from individuals, the focus is on collecting accurate data on housing characteristics and services.A population and housing census provides data needed to facilitate informed decision-making as far as policy formulation and implementation are concerned, as well as to monitor and evaluate their programmes at the smallest area level possible. It is therefore important that Statistics South Africa collects statistical data that comply with the United Nations recommendations and other relevant stakeholder needs.

The United Nations underscores the following factors in determining the selection of topics to be investigated in population censuses: a) The needs of a broad range of data users in the country; b) Achievement of the maximum degree of international comparability, both within regions and on a worldwide basis; c) The probable willingness and ability of the public to give adequate information on the topics; and d) The total national resources available for conducting a census.

In addition, the UN stipulates that census-takers should avoid collecting information that is no longer required simply because it was traditionally collected in the past, but rather focus on key demographic, social and socio-economic variables.It becomes necessary, therefore, in consultation with a broad range of users of census data, to review periodically the topics traditionally investigated and to re-evaluate the need for the series to which they contribute, particularly in the light of new data needs and alternative data sources that may have become available for investigating topics formerly covered in the population census. It was against this background that Statistics South Africa conducted user consultations in 2008 after the release of some of the Community Survey products. However, some groundwork in relation to core questions recommended by all countries in Africa has been done. In line with users' meetings, the crucial demands of the Millennium Development Goals (MDGs) should also be met. It is also imperative that Stats SA meet the demands of the users that require small area data.

Accuracy of data depends on a well-designed questionnaire that is short and to the point. The interview to complete the questionnaire should not take longer than 18 minutes per household. Accuracy also depends on the diligence of the enumerator and honesty of the respondent.On the other hand, disadvantaged populations, owing to their small numbers, are best covered in the census and not in household sample surveys.Variables such as employment/unemployment, religion, income, and language are more accurately covered in household surveys than in censuses.Users'/stakeholders' input in terms of providing information in the planning phase of the census is crucial in making it a success. However, the information provided should be within the scope of the census.

1. The Household Questionnaire is divided into the following sections:
2. Household identification particulars

3. Individual particulars Section A: Demographics Section B: Migration Section C: General Health and Functioning Section D: Parental Survival and Income Section E: Education Section F: Employment Section G: Fertility (Women 12-50 Years Listed) Section H: Housing, Household Goods and Services and Agricultural Activities Section I: Mortality in the Last 12 Months The Household Questionnaire is available in Afrikaans; English; isiZulu; IsiNdebele; Sepedi; SeSotho; SiSwati;Tshivenda;Xitsonga

4. The Transient and Tourist Hotel Questionnaire (English) is divided into the following sections:

5. Name, Age, Gender, Date of Birth, Marital Status, Population Group, Country of birth, Citizenship, Province.

6. The Questionnaire for Institutions (English) is divided into the following sections:

7. Particulars of the institution

8. Availability of piped water for the institution

9. Main source of water for domestic use

10. Main type of toilet facility

11. Type of energy/fuel used for cooking, heating and lighting at the institution

12. Disposal of refuse or rubbish

13. Asset ownership (TV, Radio, Landline telephone, Refrigerator, Internet facilities)

14. List of persons in the institution on census night (name, date of birth, sex, population group, marital status, barcode number)

15. The Post Enumeration Survey Questionnaire (English)

These questionnaires are provided as external resources.

Cleaning operations

Data editing and validation system The execution of each phase of Census operations introduces some form of errors in Census data. Despite quality assurance methodologies embedded in all the phases; data collection, data capturing (both manual and automated), coding, and editing, a number of errors creep in and distort the collected information. To promote consistency and improve on data quality, editing is a paramount phase in identifying and minimising errors such as invalid values, inconsistent entries or unknown/missing values. The editing process for Census 2011 was based on defined rules (specifications).

The editing of Census 2011 data involved a number of sequential processes: selection of members of the editing team, review of Census 2001 and 2007 Community Survey editing specifications, development of editing specifications for the Census 2011 pre-tests (2009 pilot and 2010 Dress Rehearsal), development of firewall editing specifications and finalisation of specifications for the main Census.

Editing team The Census 2011 editing team was drawn from various divisions of the organisation based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors. Census 2011 editing team was drawn from various divisions of the organization based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors.

The Census 2011 questionnaire was very complex, characterised by many sections, interlinked questions and skipping instructions. Editing of such complex, interlinked data items required application of a combination of editing techniques. Errors relating to structure were resolved using structural query language (SQL) in Oracle dataset. CSPro software was used to resolve content related errors. The strategy used for Census 2011 data editing was implementation of automated error detection and correction with minimal changes. Combinations of logical and dynamic imputation/editing were used. Logical imputations were preferred, and in many cases substantial effort was undertaken to deduce a consistent value based on the rest of the household’s information. To profile the extent of changes in the dataset and assess the effects of imputation, a set of imputation flags are included in the edited dataset. Imputation flags values include the following: 0 no imputation was performed; raw data were preserved 1 Logical editing was performed, raw data were blank 2 logical editing was performed, raw data were not blank 3 hot-deck imputation was performed, raw data were blank 4 hot-deck imputation was performed, raw data were not blank

Data appraisal

Independent monitoring and evaluation of Census field activities Independent monitoring of the Census 2011 field activities was carried out by a team of 31 professionals and 381 Monitoring

Abstract

Census of Population and Housing refers to the entire process of collecting, compiling, evaluating, analyzing, and publishing data about the population and the living quarters in a country. It entails the listing and recording of the characteristics of each individual and each living quarter as of a specified time and within a specified territory.

Census 2000 is designed to take an inventory of the total population and housing units in the Philippines and to collect information about their characteristics. The census of population is the source of information on the size and distribution of the population as well as information about the demographic, social, economic and cultural characteristics. The census of housing, on the other hand, provides information on the supply of housing units, their structural characteristics and facilities which have bearing on the maintenance of privacy, health and the development of normal family living conditions. These information are vital for making rational plans and programs for national and local development.

The Census 2000 aims to provide government planners, policy makers and administrators with data on which to base their social and economic development plans and programs.

May 1, 2000 has been designated as Census Day for the 2000 Census of Population and Housing or Census 2000, on which date the enumeration of the population and the collection of all pertinent data on housing in the Philippines shall refer.

Geographic coverage

National Coverage Regions Provinces Cities and Municipalities Barangays

Analysis unit

Individuals Households Housing units

Universe

The Census 2000 covered all persons who were alive as of 12:01 a.m. of May 1, 2000 and who are: - Filipino nationals permanently residing in the Philippines; - Filipino nationals who are temporarily at sea or are temporarily abroad as of census date; - Filipino overseas workers as of census date, even though expected to be away for more than a year; - Philippine government officials, both military and civilian, including Philippine diplomatic personnel and their families, assigned abroad; and - Civilian citizens of foreign countries having their usual residence in the Philippines or foreign visitors who have stayed or are expected to stay for at least a year from the time of their arrival in this country.

Kind of data

Census/enumeration data [cen]

Sampling procedure

In the Census 2000, there are basically two types of questionnaires to be used for the enumeration of hosueholds memmbers. These are CPH Form 2 or the Common Household Questionnaire and the CPH Form 3 or the Sample Household Questionnaire. There are procedures for selecting those households to whom CPH Form 3 will be administered. All enumerators are required to strictly follow these procedures.

The sampling rate, or the proportion of households to be selected as samples within each EA, varies from one EA to another. It can be either 100%, 20% or 10%. If the sampling rate applied to an EA is 100%, it means that all households in that EA will use CPH Form 3. IF it is 20% or 10%, it means that one-fifth or one-tenth, respectively, of all households will use CPH Form 3 while the rest will use CPH Form 2.

The scheme for the selection of sample households is known as systematic sampling with clusters as the sampling units. Under this scheme, the households in an EA are grouped in clusters of size 5. Clusters are formed by grouping together households that have been assigned consecutive serial numbers as they are listed in the Listing Page.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for 2000 Census of Population and Housing were basically patterned from previous censuses except that it should be in Intelligent Character Recognition (ICR) format. The basic questionnaires designed for this undertaking were as follows:

CPH Form 1 - Listing Page This is a sheet wherein all buildings, housing units, households and institutional living quarters within an enumeration area (EA) will be listed. Other information pertaining to the population of households and institutional living quarters will also be recorded in this form.

CPH Form 2 - Common Household Questionnaire This is the basic census questionnaire, which will be used for interview and for recording information about the common or non-sample households. This questionnaire gathers information on the following demographic and social characteristics of the population: relationship to household head, family nucleus, date of birth, age, birth registration, sex, marital status, religious affiliation, disability, ethnicity, residence five years ago and highest educational attainment. This also gathers information on building and housing unit characteristics.

CPH Form 3 - Sample Household Questionnaire This is the basic census questionnaire, which will be used for interview and for recording information about the sample households. This questionnaire contains the same question as in CPH Form 2 and additional questions, namely: citizenship, language, literacy, school attendance, type of school, place of school, usual activity/occupation, kind of business/industry, place of work and some items on fertility. It also asks additional questions on household characteristics and amenities and residence five years ago.

CPH Form 4 - Institutional Population Questionnaire This questionnaire records information about persons considered part of the institutional population. It contains questions on residence status, date of birth, age, sex, marital status, religious affiliation, disability, ethnicity and highest educational attainment.

CPH Form 5 - Barangay Schedule This questionnaire will gather indicators to update the characteristics of all barangays which will determine its urbanity.

CPH Form 6 - Notice of Listing/Enumeration This is the sticker that will be posted in a very conspicuous place, preferably in front of the house or gate of the building after listing and interviewing. This sticker indicates that the Building/Housing Unit/Household has already been enumerated.

CPH Form 7 - Common Household Questionnaire Self Administered Questionnaire (SAQ) Instructions This form contains the detailed instructions on how to fill up/answer CPH Form 2. It will accompany CPH Form 2 to be distributed to households who will answer the form themselves, such as those in designated SAQ areas or those where three callbacks or four visits have been made.

CPH Form 8 - Institutional Population Questionnaire SAQ Instructions This form describes the instructions on how to accomplish CPH Form 4 - Institutional Population Questionnaire. It will accompany CPH Form 4 to be distributed to head of institutions who will accomplish the form.

CPH Form 9 - Appointment Slip This form will be used to set an appointment with the household head or any responsible member of the household in case you were unable to interview any one during your first visit or second visit. You will indicate in this form the date and time of your next visit.

Blank Barangay Map This form will be used to enlarge map of each block of an enumeration area/barangay especially if congested areas are being enumerated.

The main questionnaires were developed in English and were translated to major dialects: Bicol, Cebuano, Hiligaynon, Ifugao, Ilocano, Kapampangan, Tagalog, and Waray.

Dataset contains occupied and unoccupied private dwelling counts from the 2013, 2018, and 2023 Censuses, as well as the percentage change in the occupied and unoccupied private dwelling counts between the 2013 and 2018 Censuses, and between the 2018 and 2023 Censuses. Data is available by regional council.

Map shows the percentage change in number of occupied and unoccupied private dwellings between the 2018 and 2023 Censuses.

Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

​

Footnotes

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.

​

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).

​

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.

​

Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

​

Dwelling occupancy status quality rating

Dwelling occupancy status is rated as high quality.

Dwelling occupancy status – 2023 Census: Information by concept has more information, for example, definitions and data quality.

​

Dwelling type quality rating

Dwelling type is rated as moderate quality.

Dwelling type – 2023 Census: Information by concept has more information, for example, definitions and data quality.

​

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) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

​

Abstract

The latest National Statistics on United Kingdom agriculture and horticulture produced by Defra on behalf of the agriculture departments of the United Kingdom were released on 16 December 2010 according to the arrangements approved by the UK Statistics Authority. The release shows the final estimates of the 2010 June Survey of Agriculture and Horticulture carried out by each of the UK agriculture departments. It includes estimates for land use, crop areas and livestock populations. The other release shows the final estimates of land use, crop areas and livestock numbers on agricultural holdings on 1 June 2010.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding, defined as "a single unit, both technically and economically, which has a single management and which produces agricultural products".

Kind of data

Census/enumeration data [cen]

Sampling procedure

a. Modality for conducting the census The AC 2010 data were collected using the traditional (classical) approach, along with the use of administrative registers as a source of census data. In general terms, organic production data, cattle information, rural development payments and common land data are all collected from administrative systems.

b. Frame The frame of the CA 2010 was the population of holdings recorded as "live" on the UK Farm Register in spring 2010, which met the minimum thresholds criteria. Holdings with temporarily reduced levels of activity (such as seasonally let out land, temporarily empty pig or poultry sheds) were also included in the census frame.The CA was an enumeration of all holdings above predefined thresholds.

Mode of data collection

Face-to-face [f2f]

Research instrument

Ten questionnaires were used for the CA 2010 and SAPM in the UK. England had three questionnaires (SAPM 2010 form, Irrigation 2010 form, Census 2010 form). Scotland had three questionnaires (Main Census 2010 form, Special Census 2010 form, SAPM 2010 form). Wales and Northern Ireland had two questionnaires each (Census 2010 form, SAPM 2010 form). The questionnaires covered all 16 core items recommended in the WCA 2010.

1. Land area on this holding on 1 June 2010
2. Grassland and non-agricultural land on 1 June 2010 (include grazing land in this section)
3. Crops and fallow land on 1 June 2010
4. Horticulture on 1 June 2010
5. Livestock on 1 June
6. People working on the holding on 1 June 2010 7-12. Labour and diversification (July 2009 to June 2010)
7. Renewable energy production

Cleaning operations

a. DATA PROCESSING AND ARCHIVING Most of the data were collected using printed survey forms. All of the forms were returned to a professional data capture company that either keyed or scanned the questionnaires. Once the data were captured, they were returned to the institutions and a number of validation checks were carried out. Survey support teams worked to correct issues with the data by contacting farmers and/or using additional data about the farm collected from other sources. Despite the efforts made, there was some nonresponse, such that a degree of imputation was required for the core CA 2010 items. Labour and diversification items were mainly imputed using donor imputation, whereas the land and livestock items were imputed using a ratio-raising process.

b. CENSUS DATA QUALITY Comparisons of the CA 2010 with other data sources for the reference year were not possible. The items collected on the CA 2010 survey forms were not collected elsewhere with sufficient coverage or a sufficiently enough sample to permit comparisons. Generally, however, the data were judged to be comparable with information from earlier years and no significant issues were noted.

Data appraisal

A number of publications are produced by each of the four UK administrations that relate to the situation in their own country. Provisional results for the UK were issued in September 2010. Final results were released in December 2010 according to the arrangements approved by the UK Statistics Authority. Final census results for UK were published in December 2010. Detailed labour results were published in October 2011.

This dataset contains the digitised censuses which were created in the NWO-funded Replication Study '(Re)counting the Uncounted. Replication and Contextualisation of Dutch and Belgian Premodern Population Estimates (1350-1800)'.

In total, close to 2,000 premodern censuses (of hearths, houses, communicants, individuals, etc.) in the Low Countries were identified and catalogued. Around 750 of these were used by one or more of the four studies that were replicated in the study. The first batch of completed censuses can be found in this dataset. More data will be added incrementally.

All files are plain text files that contain tab-separated-values (TSV). A period (.) is used as decimal separator (where applicable). The file names of the censuses refer to the census identifier which is defined in our catalogue. That catalogue also contains definitions of the units that are being counted (up to 15). For contextual information on the census, we refer to the typology, which is under development and will be made available here in due course. Links between the census observations and GIS polygons of pseudo-territories in the Historical Atlas of the Low Countries, 1350-1800 will be available here.

A codebook for the census files, definitions of the bibliographical references and the two-letter territorial codes, and an empty data entry form, we refer to the project's documentation. Note that some of the censuses have space to include fifteen unit variables, whereas older ones have only ten. Other than this, the older censuses are fully compatible with the newer census files.

Searching for specific census files works best by using an asterisk: *HO for all censuses pertaining to Holland, or *1469 for all censuses associated with the year 1469. However, be aware that the census identifiers (a two-letter territorial code and the year) carry no meaning of their own. Observations within a census can be linked to more than one year and to localities that are located in different sovereign territories.

Last but not least, especially when you use specific digitised censuses from this dataset, it is considerate and generally good practice to also cite the original works that published the census data. Hundreds of authors have worked hard to unlock and sometimes analyse these censuses and it is important to continuously give credit to their efforts.

Dataset contains occupied and unoccupied private dwelling counts from the 2013, 2018, and 2023 Censuses, as well as the percentage change in the occupied and unoccupied private dwelling counts between the 2013 and 2018 Censuses, and between the 2018 and 2023 Censuses. Data is available by territorial authority and Auckland local board

Map shows the percentage change in number of occupied and unoccupied private dwellings between the 2018 and 2023 Censuses.

Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

​

Footnotes

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.

​

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).

​

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.

​

Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

​

Dwelling occupancy status quality rating

Dwelling occupancy status is rated as high quality.

Dwelling occupancy status – 2023 Census: Information by concept has more information, for example, definitions and data quality.

​

Dwelling type quality rating

Dwelling type is rated as moderate quality.

Dwelling type – 2023 Census: Information by concept has more information, for example, definitions and data quality.

​

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) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

This datalayer displays the Urbanized Areas (UAs) for the state based on a January 1, 1990 ground condition. Note that the Census Bureau made significant changes in Urban/Rural designations for the Census 2000 data layers. Some of these delineations and definitions are explained below. 1990 Urban/Rural The U.S. Census Bureau defined urban for the 1990 census as consisting of all territory and population in urbanized areas (UAs) and in the urban portion of places with 2,500 or more people located outside of the UAs. The 1990 urban and rural classification applied to the 50 states, the District of Columbia, and Puerto Rico. 1990 Urbanized Areas A 1990 urbanized area (UA) consisted of at least one central place and the adjacent densely settled surrounding territory that together had a minimum population of 50,000 people. The densely settled surrounding territory generally consisted of an area with continuous residential development and a general overall population density of at least 1,000 people per square mile. 1990 Extended Cities For the 1990 census, the U.S. Census Bureau distinguished the urban and rural population within incorporated places whose boundaries contained large, sparsely populated, or even unpopulated area. Under the 1990 criteria, an extended city had to contain either 25 percent of the total land area or at least 25 square miles with an overall population density lower than 100 people per square mile. Such pieces of territory had to cover at least 5 square miles. This low-density area was classified as rural and the other, more densely settled portion of the incorporated place was classified as urban. Unlike previous censuses where the U.S. Census Bureau defined extended cities only within UAs, for the 1990 census the U.S. Census Bureau applied the extended city criteria to qualifying incorporated places located outside UAs. 1990 Urbanized Area Codes Each 1990 UA was assigned a 4-digit numeric census code in alphabetical sequence on a nationwide basis based on the metropolitan area codes. Note that in Record Type C, the 1990 UA 4-digit numeric census code and Census 2000 UA 5-digit numeric census code share a 5-character field. Because of this, the 1990 4-digit UA code, in Record Type C only, appears with a trailing blank. For Census 2000 the U.S. Census Bureau classifies as urban all territory, population, and housing units located within urbanized areas (UAs) and urban clusters (UCs). It delineates UA and UC boundaries to encompass densely settled territory, which generally consists of: - A cluster of one or more block groups or census blocks each of which has a population density of at least 1,000 people per square mile at the time - Surrounding block groups and census blocks each of which has a population density of at least 500 people per square mile at the time, and - Less densely settled blocks that form enclaves or indentations, or are used to connect discontiguous areas with qualifying densities. Rural consists of all territory, population, and housing units located outside of UAs and UCs. For Census 2000 this urban and rural classification applies to the 50 states, the District of Columbia, Puerto Rico, American Samoa, Guam, the Northern Mariana Islands, and the Virgin Islands of the United States. Urbanized Areas (UAs) An urbanized area consists of densely settled territory that contains 50,000 or more people. The U.S. Census Bureau delineates UAs to provide a better separation of urban and rural territory, population, and housing in the vicinity of large places. For Census 2000, the UA criteria were extensively revised and the delineations were performed using a zero-based approach. Because of more stringent density requirements, some territory that was classified as urbanized for the 1990 census has been reclassified as rural. (Area that was part of a 1990 UA has not been automatically grandfathered into the 2000 UA.) In addition, some areas that were identified as UAs for the 1990 census have been reclassified as urban clusters. Urban Clusters (UCs) An urban cluster consists of densely settled territory that has at least 2,500 people but fewer than 50,000 people. The U.S. Census Bureau introduced the UC for Census 2000 to provide a more consistent and accurate measure of the population concentration in and around places. UCs are defined using the same criteria that are used to define UAs. UCs replace the provision in the 1990 and previous censuses that defined as urban only those places with 2,500 or more people located outside of urbanized areas. Urban Area Title and Code The title of each UA and UC may contain up to three incorporated place names, and will include the two-letter U.S. Postal Service abbreviation for each state into which the UA or UC extends. However, if the UA or UC does not contain an incorporated place, the urban area title will include the single name of a census designated place (CDP), minor civil division, or populated place recognized by the U.S. Geological Survey's Geographic Names Information System. Each UC and UA is assigned a 5-digit numeric code, based on a national alphabetical sequence of all urban area names. For the 1990 census, the U.S. Census Bureau assigned as four-digit UA code based on the metropolitan area codes. Urban Area Central Places A central place functions as the dominant center of an urban area. The U.S. Census Bureau identifies one or more central places for each UA or UC that contains a place. Any incorporated place or census designated place (CDP) that is in the title of the urban area is a central place of that UA or UC. In addition, any other incorporated place or CDP that has an urban population of 50,000 or an urban population of at least 2,500 people and is at least 2/3 the size of the largest place within the urban area also is a central place. Extended Places As a result of the UA and UC delineations, an incorporated place or census designated place (CDP) may be partially within and partially outside of a UA or UC. Any place that is split by a UA or UC is referred t o as an extended place.

Abstract

Over the past decade, Albania has been seeking to develop the framework for a market economy and more open society. It has faced severe internal and external challenges in the interim - extremely low income levels and a lack of basic infrastructure, the rapid collapse of output and inflation rise after the shift in regime in 1991, the turmoil during the 1997 pyramid crisis, and the social and economic shocks accompanying the 1999 Kosovo crisis. In the face of these challenges, Albania has made notable progress in creating conditions conducive to growth and poverty reduction. A poverty profile based on 1996 data (the most recent available) showed that some 30 percent of the rural and some 15 percent of the urban population are poor, with many others vulnerable to poverty due to their incomes being close to the poverty threshold. Income related poverty is compounded by the severe lack of access to basic infrastructure, education and health services, clean water, etc., and the ability of the Government to address these issues is complicated by high levels of internal and external migration that are not well understood. To date, the paucity of household-level information has been a constraining factor in the design, implementation and evaluation of economic and social programs in Albania. Multi-purpose household surveys are one of the main sources of information to determine living conditions and measure the poverty situation of a country and provide an indispensable tool to assist policymakers in monitoring and targeting social programs. Two recent surveys carried out by the Albanian Institute of Statistics (INSTAT) - the 1998 Living Conditions Survey (LCS) and the 2000 Household Budget Survey (HBS) - drew attention, once again, to the need for accurately measuring household welfare according to well accepted standards, and for monitoring these trends on a regular basis. In spite of their narrow scope and limitations, these two surveys have provided the country with an invaluable training ground towards the development of a permanent household survey system to support the government strategic planning in its fight against poverty. In the process leading to its first Poverty Reduction Strategy Paper (PRSP; also known in Albania as Growth and Poverty Reduction Strategy, GPRS), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyse on a regular basis the information it needs to inform policy-making. In its first phase (2001-2006), this monitoring system will include the following data collection instruments:

(i) Population and Housing Census (ii) Living Standards Measurement Surveys every 3 years (iii) annual panel surveys.

The Population and Housing Census (PHC) conducted in April 2001, provided the country with a much needed updated sampling frame which is one of the building blocks for the household survey structure. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a sub-sample of LSMS households (in 2003, 2004 and 2006), drawing heavily on the 2001 census information. The possibility to include a panel component in the second LSMS will be considered at a later stage, based on the experience accumulated with the first panels. The 2002 LSMS was in the field between April and early July, with some field activities (the community and price questionnaires) extending into August and September. The survey work was undertaken by the Living Standards unit of INSTAT, with the technical assistance of the World Bank. The present document provides detailed information on this survey. Section II summarizes the content of the survey instruments used. Section III focuses on the details of the sample design. Sections IV describes the pilot test and fieldwork procedures of the survey, as well as the training received by survey staff. Section V reviews data entry and data cleaning issues. Finally, section VI contains a series of annotations that all those interested in using the data should read.

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLING FRAME

The Republic of Albania is divided geographically into 12 Prefectures (Prefekturat). The latter are divided into Districts (Rrethet) which are, in turn, divided into Cities (Qyteti) and Communes (Komunat). The Communes contain all the rural villages and the very small cities. For the April 2001 General Census of Population and Housing census purposes, the cities and the villages were divided into Enumeration Areas (EAs). These formed the basis for the LSMS sampling frame. The EAs in the frame are classified by Prefecture, District, City or Commune. The frame also contains, for every EA, the number of Housing Units (HUs), the number of occupied HUs, the number of unoccupied HUs, and the number of households. Occupied dwellings rather than total number of dwellings were used since many census EAs contain a large number of empty dwellings. The Housing Unit (defined as the space occupied by one household) was taken as the sampling unit, instead of the household, because the HU is more permanent and easier to identify in the field. A detailed review of the list of censuses EAs shows that many have zero population. In order to obtain EAs with a minimum of 50 and a maximum of 120 occupied housing units, the EAs with zero population were first removed from the sampling frame. Then, the smallest EAs (with less than 50 HU) were collapsed with geographically adjacent ones and the largest EAs (with more than 120 HU) were split into two or more EAs. Subsequently, maps identifying the boundaries of every split and collapsed EA were prepared Sample Size and Implementation Since the 2002 LSMS had been conducted about a year after the April 2001 census, a listing operation to update the sample EAs was not conducted. However, given the rapid speed at which new constructions and demolitions of buildings take place in the city of Tirana and its suburbs, a quick count of the 75 sample EAs was carried out followed by a listing operation. The listing sheets prepared during the listing operation became the sampling frame for the final stage of selection. The final sample design for the 2002 LSMS included 450 Primary Sampling Units (PSUs) and 8 households in each PSU, for a total of 3600 households. Four reserve units were selected in each sample PSU to act as replacement unit in non-response cases. In a few cases in which the rate of migration was particularly high and more than four of the originally selected households could not be found for the interview, additional households for the same PSU were randomly selected. During the implementation of the survey there was a problem with the management of the questionnaires for a household that had initially refused, but later accepted, to fill in the food diary. The original household questionnaire was lost in the process and it was not possible to match the diary with a valid household questionnaire. The household had therefore to be dropped from the sample (this happened in Shkoder, PSU 16). The final sample size is therefore of 3599 households.

(b) STRATIFICATION

The sampling frame was divided in four regions (strata), Coastal Area, Central Area, and Mountain Area, and Tirana (urban and other urban). These four strata were further divided into major cities, other urban, and other rural. The EAs were selected proportionately to the number of housing units in these areas. In the city of Tirana and its suburbs, implicit stratification was used to improve the efficiency of the sample design. The implicit stratification was performed by ordering the EAs in the sampling frame in a geographic serpentine fashion within each stratum used for the independent selection of EAs.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

(a) QUALITY CHECKS Besides the checks built-in in the DE program and those performed on the preliminary versions of the dataset as it was building up, and additional round of in depth checks on the household questionnaire and the food diary was performed in late September and early October in Tirana. Wherever possible data entry errors or inconsistencies in the dataset were spotted, the original questionnaires or diary were retrieved, and the information contained therein checked. Changes were made to the August version of the dataset as needed and the dataset was finalized in October.

(b) DATA ENTRY Data Entry Operations Data entry for all the survey instruments was performed using custom made applications developed in CS-Pro. Data entry for the household questionnaire was performed in a decentralized fashion in parallel with the enumeration, so as to allow for 'real-time' checking of the data collected. This allowed a further tier of quality control checks on the data. Where errors in the data were spotted during data entry, it was possible to instruct enumerators and supervisors to correct the information, if necessary, revisiting the household, when the teams were still in the field. A further round of checks was performed by the core team in Tirana and Bank staff in Washington as the data were gathered from the field and the entire dataset started building up. All but one of the 16 teams in the districts had one DEO, the Fier team had two, and there were four DEO's for Tirana. Each DEO worked with a laptop computer, and was given office space in the regional Statistics Offices, or in INSTAT headquarters for the Tirana teams. The DEO's received Part 1 of the household questionnaire from the supervisor once the supervisor had checked the enumerator's work, within two

Abstract

The 2010 Saint Lucia Population and Housing Census is conducted by the Central Statistical Office staff. The island-nation of Saint Lucia recorded an overall household population increase of 5 percent from May 2001 to May 2010 based on estimates derived from a complete enumeration of the population of Saint Lucia during the conduct of the recently completed 2010 Population and Housing Census. Saint Lucia's total resident population as at midnight on Census Day, the 10th May 2010 stood at 166,526 persons. Saint Lucia's total population including non-resident persons was estimated to be 173,720, the total number of non-resident persons was 7,194. The preliminary count of Saint Lucia's enumerated population was 151,864 persons reflecting a response rate to the census of 92%. The total resident population of St. Lucia is comprised of 82,926 males and 83,600 females. Out of this sum, there were 165,595 individuals residing in private households, 931 persons living in institutions.

A modern population and housing census is the process of collecting, compiling, analyzing, and publishing demographic, socio-economic, and environmental data pertaining to all persons in a country and the national housing stock at a specified time. A census is a form of national stocktaking. Since the census is a complete count of the population and living quarters, it provides detailed benchmark data on the size of the population, age structure, educational attainment, economic activity, disability, housing, and household amenities as well as other major socio-economic characteristics.

Geographic coverage

National Coverage includes all Administrative Districts and Political Constituencies

Analysis unit

• Households,
• Individuals.

Universe

The Census covered all de jure household members (usual residents of St Lucia based on the six month criteria). The fertility of all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household. The Census also collected data on dwelling and housing conditions of all resident householders. In the Census Visitation record all de jure household members were counted by sex, in addition, persons present in St Lucia at the time of the census who were not usual residents were also counted to produce the de facto population of St Lucia on census day May 10, 2010.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires were bound together into booklets. Each booklet contained a cover page (for identification and the Record of Visits), page 2 for Listing the names of the members of the Household and for any comments needed concerning any member of the household or any part of the enumeration. NATIONAL ARCHIVES, INTERNATIONAL MIGRATION and HOUSING spread over pages 3 to 5.

After these sections, three individual questionnaires (6 pages each) complete the booklet. These booklets provide for three (3) persons and are to be used for households consisting of three (3) or fewer persons. If the household comprises more than three persons, the main booklet plus the number of additional person questionnaires were required. For example,

For a 1, 2, 3-person household, use one booklet;

For a 4-person household, use one booklet plus one additional person questionnaire.

For a 5-person household, use one booklet plus two additional person questionnaires and so on.

The ED Number and the Household number contained on the front cover page of the main questionnaire was transferred to the top of the front page of EVERY person questionnaire whether or not it was an individual questionnaire within the main booklet or whether it was an individual questionnaire applicable to a household with more than three persons.

STRUCTURE OF THE INDIVIDUAL QUESTIONNAIRE

The individual questionnaire starts at Section 3. The questions are divided into eleven groups, each having a central theme and given a section number as follows:

Section 3: Personal Characteristics (for all persons) Section 4: Birthplace & Residence (for all persons) Section 5: Disability (for all persons) Section 6: Health (for all persons) Section 7: Education and Internet Access (for all persons) Section 8: Professional, Technical & Vocational Training (for persons 15 years and over) Section 9: Economic Activity (for persons 15 years and over) Section 10: Income and Livelihood (for females 15 years and over) Section 11: Marital Status and Union Status (for persons 15 years and over) Section 12: Fertility (for persons 15 years and over) Section 13: Where Spent Census Night (for all persons)

Cleaning operations

Data editing took place at a number of stages throughout the processing, including (See External Resource for more information on this item):

a) Field Editing by interviewers and field supervisors The guidelines for the conduct of these edits were laid out in PART IX: Checking Your Questionnaires for Errors in the Enumerators Manual. These instructions are repeated in the supervisors manual and also stated in the contract for payment of enumerators and supervisors. A number of elements of the edits outlined formed the basis for the payment of enumerators and supervisors.

b) Office editing and questionnaire re-numbering When a full set of questionnaires from a completed ED was recieved by the office, persons assigned as census evaluators had the responsibility to review the content of each Questionnaire to check for completeness. They were required to perform checks on the questionnaires and the visitation records for the key geographic variables and perform other checks in line with the requirements of a Census Evaluation form which laid out quality standards for the approval of a completed ED for payment. The Census evaluation form is provided as an external resource for information.

c) Data Capture, Editing and Coding during scanning and data verification The data was captured using TELEform V10.4.1 and the data from the forms was exported to a SQL Server 2005 database as was all other census related information captured on forms, such as the census 2010 Evaluation form, referred to previously, the census visitation record etc.

The names of the SQL Server Databases are as follows: 1) Census2010 containing Tables: Census2010Persons, Census2010House, Census2010Visit, Census2010Evaluation, Census2010ApplicationForms, CensusTestScores, Census2010Institutions 2) Census2010_Validated contained data which was validated on several metrics outline in a VBA program built into the TELEform v10.4 software used to capture the data after scanning.

The correction of geographic variables was completed during this process. The scanner operator would manually enter the ED code for the batch being scanned, he would also enter the first and last household for the batch manually. Later the verifier would independantly verify the ED and the household number entered by the enumerator against the values entered by the scanner operator to ensure that they were either the same as in the case of the ED number or within the range of households expected in the batch as in the case of the household number. This was done using VBA validation code written within the TELEform 10.4.1 software used for the scanning and capture of the data from the Census.

Computer Assisted Coding was built into the TELEform template, this method assisted the enumerator using keywords to identify the code for the entry of the appropriate settlement, industry or occupation code. A listing of the codes used is attached to this document as an external resource. Occupation codes are in the international format of ISCO-08 while the industry code applied is based on ISIC Rev4.

d) Structure checking and completeness in Foxpro

The data was exported to MS Access and then on to MS Foxpro where some basic editing was done.

1) This involved the conversion of descriptions of settlement, ISCO and ISIC data collected in fields to codes 2) Standardizing the lenghts and format of all fields in the dataset in preparation for conversion to CSPRO ASCII data format 3) Transposing data on Migration, deaths, disability and births in the last 12 months to variables in the household and person files 4) Removal of blank and very incomplete records 5) Removal of all duplicates and the cleaning of all inconsistent records between the household and the person file. 6) Creation of CSPRO 4.0 compatible format data file for use in further editing and cleaning

e) Detailed variable level editing using CSPRO 4.0 and hotdecking Detailed programs were developed to clean census data on critical variables in the housing section of the questionnaire such as Type of Dwelling, household assets etc, demographic variables such as age, sex, education and economic activiity variables were cleaned in the first version of the CSPRO 4.0 *.bch program file developed. After the first version of the cleaning program was complete the Statistical Office published the Preliminary Census 2010 Report (Updated April 2010). The first version of this publication released in January contained only data on population counts from the census visitation records. The updated April 2010 Preliminary Census report contained information on all the main variables cleaned in the first version of the cleaning program. The CSPRO 4.0 program employed the use of many 3-dimensional hotdecking programs to correct for items not stated or recorded.

f) Checking of data files using the Tabulation Features of CSPRO 4.0 and SPSS 19 Crosstabulations of variables were used to identify inconsistent data and improve CSPRO 4.0 editing programs

Detailed documentation of

Census 2021 data on housing characteristics at the dwelling level, in England and Wales. Characteristics include dwelling occupancy, shared dwellings, accommodation type, tenure, central heating type and number of bedrooms. We also compare with the 2011 Census, where appropriate. Figures are based on geography boundaries as of December 2021.

Total counts for some dwelling groups may not match between published tables. This is to protect the confidentiality of dwellings' data. Dwelling counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

This dataset provides Census 2021 estimates that classify all dwellings (excluding communal establishments) in England and Wales by housing characteristics, where possible. Some characteristics are only available for occupied dwellings i.e. tenure, central heating type and number of bedrooms.The estimates are as at Census Day, 21 March 2021.

Quality considerations along with the strengths and limitations of Census 2021, more generally, can be found in the Quality and Methodology Information (QMI) for Census 2021.

We do not separate social rent into ‘housing association, housing co-operative, charitable trust, registered social landlord’ and ‘council or local authority’ as it is evident in the data that there is respondent error in identifying the type of landlord. This is particularly clear in results for areas which have no local authority housing stock, but there are households responding as having a ‘council or local authority’ landlord type. Estimates are likely to be accurate when the social rent category is combined.

Dwellings

A dwelling is a self-contained unit of accommodation that may be empty or being lived in, for example houses or flats. They are usually made up of one household, but those with more than one household are shared and called a “shared dwelling”.

Unoccupied Dwelling

An unoccupied dwelling refers to a unit of accommodation with no usual residents, although they may be used by short term residents or visitors on census night. An occupied dwelling has usual residents.

Shared Dwelling

A dwelling is shared if there are two or more households at the same address that are not self-contained. This means that not all of the rooms (including the kitchen, bathroom and toilet, if any) are behind a door only that household can use. Households combine to form a shared dwelling that is self-contained.

Statistical Area 1 2023 update

SA1 2023 is the first major update of the geography since it was first created in 2018. The update is to ensure SA1s are relevant and meet criteria before each five-yearly population and dwelling census. SA1 2023 contains 3,251 new SA1s. Updates were made to reflect real world changes including new subdivisions and motorways, improve the delineation of urban rural and other statistical areas and to ensure they meet population criteria by reducing the number of SA1s with small or large populations.

​

Description

This dataset is the definitive version of the annually released statistical area 1 (SA1) boundaries as at 1 January 2023, as defined by Stats NZ. This version contains 33,164 SA1s (33,148 digitised and 16 with empty or null geometries (non-digitised).

​

SA1 is an output geography that allows the release of more low-level data than is available at the meshblock level. Built by joining meshblocks, SA1s have an ideal size range of 100–200 residents, and a maximum population of about 500. This is to minimise suppression of population data in multivariate statistics tables.

​

The SA1 should:

form a contiguous cluster of one or more meshblocks,

be either urban, rural, or water in character,

be small enough to:

• allow flexibility for aggregation to other statistical geographies,

• allow users to aggregate areas into their own defined communities of interest,

form a nested hierarchy with statistical output geographies and administrative boundaries. It must:

• be built from meshblocks,

• either define or aggregate to define SA2s, urban rural areas, territorial authorities, and regional councils.

​

SA1s generally have a population of 100–200 residents, with some exceptions:

• SA1s with nil or nominal resident populations are created to represent remote mainland areas, unpopulated islands, inland water, inlets, or oceanic areas.

• Some SA1s in remote rural areas and urban industrial or business areas have fewer than 100 residents.

• Some SA1s that contain apartment blocks, retirement villages, and large non-residential facilities (prisons, boarding schools, etc) have more than 500 residents.

​

SA1 numbering

SA1s are not named. SA1 codes have seven digits starting with a 7 and are numbered approximately north to south. Non-digitised codes start with 79.

​

As new SA1s are created, they are given the next available numeric code. If the composition of an SA1 changes through splitting or amalgamating different meshblocks, the SA1 is given a new code. The previous code no longer exists within that version and future versions of the SA1 classification.

​

Digitised and non-digitised SA1s

The digital geographic boundaries are defined and maintained by Stats NZ.

​

Aggregated from meshblocks, SA1s cover the land area of New Zealand, the water area to the 12-mile limit, the Chatham Islands, Kermadec Islands, sub-Antarctic islands, off-shore oil rigs, and Ross Dependency. The following 16 SA1s are held in non-digitised form.

​

7999901; New Zealand Economic Zone, 7999902; Oceanic Kermadec Islands,7999903; Kermadec Islands, 7999904; Oceanic Oil Rig Taranaki,7999905; Oceanic Campbell Island, 7999906; Campbell Island, 7999907; Oceanic Oil Rig Southland, 7999908; Oceanic Auckland Islands, 7999909; Auckland Islands, 7999910; Oceanic Bounty Islands, 7999911; Bounty Islands, 7999912; Oceanic Snares Islands, 7999913; Snares Islands, 7999914; Oceanic Antipodes Islands, 7999915; Antipodes Islands, 7999916; Ross Dependency.

​

For more information please refer to the Statistical standard for geographic areas 2023.

​

Generalised version

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

​

Digital data

Digital boundary data became freely available on 1 July 2007.

​

To download geographic classifications in table formats such as CSV please use Ariā

In 1800, the region of Germany was not a single, unified nation, but a collection of decentralized, independent states, bound together as part of the Holy Roman Empire. This empire was dissolved, however, in 1806, during the Revolutionary and Napoleonic eras in Europe, and the German Confederation was established in 1815. Napoleonic reforms led to the abolition of serfdom, extension of voting rights to property-owners, and an overall increase in living standards. The population grew throughout the remainder of the century, as improvements in sanitation and medicine (namely, mandatory vaccination policies) saw child mortality rates fall in later decades. As Germany industrialized and the economy grew, so too did the argument for nationhood; calls for pan-Germanism (the unification of all German-speaking lands) grew more popular among the lower classes in the mid-1800s, especially following the revolutions of 1948-49. In contrast, industrialization and poor harvests also saw high unemployment in rural regions, which led to waves of mass migration, particularly to the U.S.. In 1886, the Austro-Prussian War united northern Germany under a new Confederation, while the remaining German states (excluding Austria and Switzerland) joined following the Franco-Prussian War in 1871; this established the German Empire, under the Prussian leadership of Emperor Wilhelm I and Chancellor Otto von Bismarck. 1871 to 1945 - Unification to the Second World War The first decades of unification saw Germany rise to become one of Europe's strongest and most advanced nations, and challenge other world powers on an international scale, establishing colonies in Africa and the Pacific. These endeavors were cut short, however, when the Austro-Hungarian heir apparent was assassinated in Sarajevo; Germany promised a "blank check" of support for Austria's retaliation, who subsequently declared war on Serbia and set the First World War in motion. Viewed as the strongest of the Central Powers, Germany mobilized over 11 million men throughout the war, and its army fought in all theaters. As the war progressed, both the military and civilian populations grew increasingly weakened due to malnutrition, as Germany's resources became stretched. By the war's end in 1918, Germany suffered over 2 million civilian and military deaths due to conflict, and several hundred thousand more during the accompanying influenza pandemic. Mass displacement and the restructuring of Europe's borders through the Treaty of Versailles saw the population drop by several million more.

Reparations and economic mismanagement also financially crippled Germany and led to bitter indignation among many Germans in the interwar period; something that was exploited by Adolf Hitler on his rise to power. Reckless printing of money caused hyperinflation in 1923, when the currency became so worthless that basic items were priced at trillions of Marks; the introduction of the Rentenmark then stabilized the economy before the Great Depression of 1929 sent it back into dramatic decline. When Hitler became Chancellor of Germany in 1933, the Nazi government disregarded the Treaty of Versailles' restrictions and Germany rose once more to become an emerging superpower. Hitler's desire for territorial expansion into eastern Europe and the creation of an ethnically-homogenous German empire then led to the invasion of Poland in 1939, which is considered the beginning of the Second World War in Europe. Again, almost every aspect of German life contributed to the war effort, and more than 13 million men were mobilized. After six years of war, and over seven million German deaths, the Axis powers were defeated and Germany was divided into four zones administered by France, the Soviet Union, the UK, and the U.S.. Mass displacement, shifting borders, and the relocation of peoples based on ethnicity also greatly affected the population during this time. 1945 to 2020 - Partition and Reunification In the late 1940s, cold war tensions led to two distinct states emerging in Germany; the Soviet-controlled east became the communist German Democratic Republic (DDR), and the three western zones merged to form the democratic Federal Republic of Germany. Additionally, Berlin was split in a similar fashion, although its location deep inside DDR territory created series of problems and opportunities for the those on either side. Life quickly changed depending on which side of the border one lived. Within a decade, rapid economic recovery saw West Germany become western Europe's strongest economy and a key international player. In the east, living standards were much lower, although unemployment was almost non-existent; internationally, East Germany was the strongest economy in the Eastern Bloc (after the USSR), though it eventually fell behind the West by the 1970s. The restriction of movement between the two states also led to labor shortages in t...

Abstract

The 2016 Timor-Leste Demographic and Health Survey (TLDHS) was implemented by the General Directorate of Statistics (GDS) of the Ministry of Finance in collaboration with the Ministry of Health (MOH). Data collection took place from 16 September to 22 December, 2016.

The primary objective of the 2016 TLDHS project is to provide up-to-date estimates of basic demographic and health indicators. The TLDHS provides a comprehensive overview of population, maternal, and child health issues in Timor-Leste. More specifically, the 2016 TLDHS: • Collected data at the national level, which allows the calculation of key demographic indicators, particularly fertility, and child, adult, and maternal mortality rates • Provided data to explore the direct and indirect factors that determine the levels and trends of fertility and child mortality • Measured the levels of contraceptive knowledge and practice • Obtained data on key aspects of maternal and child health, including immunization coverage, prevalence and treatment of diarrhea and other diseases among children under age 5, and maternity care, including antenatal visits and assistance at delivery • Obtained data on child feeding practices, including breastfeeding, and collected anthropometric measures to assess nutritional status in children, women, and men • Tested for anemia in children, women, and men • Collected data on the knowledge and attitudes of women and men about sexually-transmitted diseases and HIV/AIDS, potential exposure to the risk of HIV infection (risk behaviors and condom use), and coverage of HIV testing and counseling • Measured key education indicators, including school attendance ratios, level of educational attainment, and literacy levels • Collected information on the extent of disability • Collected information on non-communicable diseases • Collected information on early childhood development • Collected information on domestic violence • The information collected through the 2016 TLDHS is intended to assist policy makers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.

Geographic coverage

National

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the TLDHS 2016 survey is the 2015 Timor-Leste Population and Housing Census (TLPHC 2015), provided by the General Directorate of Statistics. The sampling frame is a complete list of 2320 non-empty Enumeration Areas (EAs) created for the 2015 population census. An EA is a geographic area made up of a convenient number of dwelling units which served as counting units for the census, with an average size of 89 households per EA. The sampling frame contains information about the administrative unit, the type of residence, the number of residential households and the number of male and female population for each of the EAs. Among the 2320 EAs, 413 are urban residence and 1907 are rural residence.

There are five geographic regions in Timor-Leste, and these are subdivided into 12 municipalities and special administrative region (SAR) of Oecussi. The 2016 TLDHS sample was designed to produce reliable estimates of indicators for the country as a whole, for urban and rural areas, and for each of the 13 municipalities. A representative probability sample of approximately 12,000 households was drawn; the sample was stratified and selected in two stages. In the first stage, 455 EAs were selected with probability proportional to EA size from the 2015 TLPHC: 129 EAs in urban areas and 326 EAs in rural areas. In the second stage, 26 households were randomly selected within each of the 455 EAs; the sampling frame for this household selection was the 2015 TLPHC household listing available from the census database.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used for the 2016 TLDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to Timor-Leste.

Cleaning operations

The data processing operation included registering and checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning included structure and consistency checks to ensure completeness of work in the field. The central office also conducted secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by two staff who took part in the main fieldwork training. Data editing was accomplished with CSPro software. Secondary editing and data processing were initiated in October 2016 and completed in February 2017.

Response rate

A total of 11,829 households were selected for the sample, of which 11,660 were occupied. Of the occupied households, 11,502 were successfully interviewed, which yielded a response rate of 99 percent.

In the interviewed households, 12,998 eligible women were identified for individual interviews. Interviews were completed with 12,607 women, yielding a response rate of 97 percent. In the subsample of households selected for the men’s interviews, 4,878 eligible men were identified and 4,622 were successfully interviewed, yielding a response rate of 95 percent. Response rates were higher in rural than in urban areas, with the difference being more pronounced among men (97 percent versus 90 percent, respectively) than among women (98 percent versus 94 percent, respectively). The lower response rates for men were likely due to their more frequent and longer absences from the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the TLDHS 2016 to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the TLDHS 2016 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the TLDHS 2016 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the TLDHS 2016 is a SAS program. This program used the Taylor linearization method of variance estimation for survey estimates that are means, proportions or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Height and weight data completeness and quality for children - Completeness of information on siblings - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends

See details of the data quality tables in Appendix C of the survey final report.

Abstract

The Bosnia and Herzegovina Multiple Indicator Cluster Survey 2000 (B&H MICS 2000) is a nationally representative survey of households, women and children (aged 0 – 18 years). The main objectives of the survey were to provide up-to-date information for assessing the situation of children and women in Bosnia and Herzegovina at the end of the decade, and to furnish the data needed for monitoring progress toward the goals established at the World Summit for Children and as a basis for future action. Data on breast-feeding and salt iodination are available from previous UNICEF supported surveys. 1-4 Data on the remaining End of Decade Goals are available from other sources and are presented in the Bosnia and Herzegovina End of Decade Report. The B&H MICS 2000 survey covered the territory of Bosnia and Herzegovina minus the district of Brèko. This was omitted for sampling and organisational reasons. The survey was carried out in mid 2000 in a joint process with input from two entity field teams, from the Federation of Bosnia and Herzegovina and Republika Srpska. State level and entity level data are presented in this report. The survey sampled 10 772 households across the territory with a very high response rate of 98 percent. A total of 35 571 people lived in the households that responded, making this the largest such survey conducted in Bosnia and Herzegovina in the past ten years. The level of completion of the questionnaires was very high, and the data was subjected to multiple quality checks at all stages of the survey.

Geographic coverage

The B&H MICS 2000 survey covered the territory of Bosnia and Herzegovina minus the district of Brèko. This was omitted for sampling and organisational reasons.

Analysis unit

Household, Women, Children

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the survey was designed to provide estimates of the indicators at the national level, for urban and rural areas, and for the two entities - the Federation ofBosnia and Herzegovina and Republika Srpska. The district of Brèko in the North East corner of the State was not included in the survey, due to organisational and statistical sampling difficulties. Developing a sampling frame was perhaps the single biggest challenge in this survey. The most recent complete census data were from 1991. Subsequently, there had been widespread conflict and massive population movements both within and from the state. A two stage sampling method was used and this is explained in detail in Appendix C of the final report.

Stage 1

The geographical area of Bosnia and Herzegovina (with the exception of Brèko district) was selected. The enumeration areas from the 1991 census were taken as the basis for developing the sampling frame. This was updated in the Federation using three additional sources of information, the OSCE voter lists, population estimates from UNHCR and municipality registration data. Additionally, the sampling frame was adjusted in RS using the results of a 1997 census of refugees and displaced people. The entire geographical area of the survey was then divided into segments using probability proportional to size at the municipality level. Each segment covered approximately 110 households. The segments were then randomly selected and an additional number of alternate segments were identified so that in the case of a segment being unusable (empty, mined etc.) an alternate segment could be assigned.

Stage 2

The fieldwork teams then went to their allocated segments and made a listing of all households in each segment. From these, the fieldwork supervisors with assistance from the entity statistical institutes updated the old maps if necessary, and in some cases made new maps. Where segments were empty of households, had fewer than 80 households or were heavily mined, they were excluded and an alternate segment selected from the reserve list. Adjustments to the sampling plan are described in detail in Appendix C of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The three questionnaires (household, women aged 15 - 49 and children under the age of five) for the B&H MICS 2000 were based on the MICS Model questionnaires with minor modifications and additions. A household questionnaire was administered in each household, which collected information on household members including sex, age, literacy, marital status and orphanhood status. The household questionnaire also included education, child labour and water and sanitation modules. The questionnaire for women contained the following modules: · Child mortality · Maternal and new-born health · Contraceptive use · HIV/AIDS.

The questionnaire for children under the age of five was administered to the mother or carer of the child and included modules on: · Birth registration and early learning · Care during illness · Immunisation · Anthropometry

The MICS Model Questionnaires were translated from English into Bosnian/Croatian (Roman script) and Serbian (Cyrillic script). The questionnaires were then pre-tested in 100 households in each entity during June 2000. Based on the results of these pre-tests, modifications were made to the wording and translation of the questionnaires. For the full questionnaires, see Appendix D of the report which is provided as External Resources.

Response rate

The survey sampled 10 772 households across the territory with a very high response rate of 98 percent. A total of 35 571 people lived in the households that responded, making this the largest such survey conducted in Bosnia and Herzegovina in the past ten years. The level of completion of the questionnaires was very high, and the data was subjected to multiple quality checks at all stages of the survey.

Data appraisal

Of the 10 772 households selected for the survey sample, 10 742 were found to be occupied (Table 1). Of these, 10 546 were successfully interviewed to give a household response rate of 98 percent. The response rate was slightly higher in rural areas (99 %) than in urban areas (97%). In the interviewed households, 8 912 eligible women aged 15-49 years were identified. Of these, 8 726 were successfully interviewed, yielding a response rate of 98 percent. In addition, 2 642 children under the age of five years were listed in the household questionnaire.

Sources of income by census family type (x 1,000) c (1, 2, 3, 4, 5)Frequency: AnnualTable: 11-10-0014-01 (formerly CANSIM 111-0014)Release date: 2024-06-27Geography: Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration partSymbol legend: .. not available for a specific reference periodTable Corrections: Note On August 11, 2021, a correction was made to the values associated with the total income and the other income concepts for 2019.Footnotes: 1 The data source for this table is the final version of the T1 Family File, created by the Centre for Income and Socio-Economic Well-being Statistics of Statistics Canada. Because they are based on a different methodology, estimates of the number of individuals and census families presented in this table differ from estimates produced by the Centre for Demography. Information on the data source, the historical availability, definitions of the terms used, and the geographies available can be found at Technical Reference Guide for the Annual Income Estimates for Census Families Individuals and Seniors - opens in a new browser window." 2 Families are comprised of: 1) couples (married or common-law, including same-sex couples) living in the same dwelling with or without children, and 2) single parents (male or female) living with one or more children. Persons who are not matched to a family become persons not in census families. They may be living alone, with a family to whom they are related, with a family to whom they are unrelated or with other persons not in census families. Beginning in 2001, same-sex couples reporting as couples are counted as couple families. 3 A couple family consists of a couple living together (married or common-law, including same-sex couples) living at the same address with or without children. Beginning in 2001, same-sex couples reporting as couples are counted as couple families. 4 A lone-parent family is a family with only one parent, male or female, and with at least one child. 5 A person not in census families is an individual who is not part of a census family, couple family or lone-parent family. Persons not in census families may live with their married children or with their children who have children of their own. They may be living with a family to whom they are related or unrelated. They may also be living alone or with other non-family persons. 6 The Census Standard Geographical Classification (SGC) is used for data dissemination of the census metropolitan areas and the census agglomerations: from 1997 to 2001, SGC 1996; from 2002 to 2006, SGC 2001; from 2007 to 2011, SGC 2006; from 2012 to 2016, SGC 2011; from 2016 to 2020, SGC 2016; as of 2021, SGC 2021. Please note that census agglomerations were introduced in this CANSIM table in 2008. 7 Family income is the sum of the incomes of all members of the family. As of 2020, COVID-19 - Government income support and benefits are included in income estimates. A detailed definition of what is included in total income is available from the Technical Reference Guide for the Annual Income Estimates for Census Families Individuals and Seniors - opens in a new browser window." 8 As of 2020, COVID benefits are included in income estimates. For more information, consult the Technical Reference Guide for the Annual Income Estimates for Census Families Individuals and Seniors - opens in a new browser window." 9 Total income is income from all sources. As of 2020, COVID-19 - Government income support and benefits are included in income estimates. A detailed definition of what is included in total income is available from the Technical Reference Guide for the Preliminary Estimates from the T1 Family File (T1FF) - opens in a new browser window." 10 Employment income includes wages and salaries, commissions from employment, training allowances, tips and gratuities, and net self-employment income (business, professional, commission, farming and fishing income). 11 This includes dividend income reported on line 12000 of the tax return and/or interest and other investment income reported on line 12100. Dividend income consists of dividends from taxable Canadian corporations (as stocks or mutual funds). Interest and other investment income includes interest from Canada Savings bonds, bank accounts, treasury bills, investment certificates, term deposits, earnings on life insurance policies, and foreign interest and dividend income. 12 Government transfer payments are payments to individuals by the federal or provincial governments. They include: Employment Insurance (EI) benefits, Goods and Services Tax Credit (GST) and Harmonized Tax Credit (HST), Federal Child Benefits, Old Age Security (OAS) and Net Federal Supplements, Canada Pension Plan (CPP) and Quebec Pension Plan (QPP) benefits, Workers' Compensation Benefits, Social Assistance Benefits, Provincial Refundable Tax Credits and Family Benefits, and Other Government Transfers. Definitions of the transfer payments are available from the Technical Reference Guide for the Annual Income Estimates for Census Families Individuals and Seniors - opens in a new browser window." 13 The Old Age Security (OAS) pension is part of the OAS program, a federal government program that guarantees a degree of financial security to seniors. All persons in Canada aged 65 years and over, who are Canadian citizens or legal residents, may qualify for a full OAS pension, depending on their years of residence in Canada after reaching the age of 18. OAS benefits include all benefits reported for the reference year, excluding Guaranteed Income Supplements (GIS) and Spousal Allowance benefits (SPA). Starting with 1994 data, OAS income of non-filing spouses was estimated and included in the tables. 14 The net federal supplements are part of the Old Age Security (OAS) pension program, intended to supplement the income of pensioners and spouses with lower income. Payments take the form of a Guaranteed Income Supplement (GIS) or a Spouse's Allowance (SPA). 15 The Canada Pension Plan (CPP) and Quebec Pension Plan (QPP) benefits are compulsory contributory social insurance plans that protect workers and their families against loss of income due to retirement, disability or death. CPP and QPP benefits include all benefits reported for the reference year. 16 In 2020, COVID benefits are included in income estimates. For more information, consult the Technical Reference Guide for the Annual Income Estimates for Census Families Individuals and Seniors - opens in a new browser window." 17 In 2021, COVID benefits are included in income estimates. For more information, consult the Technical Reference Guide for the Annual Income Estimates for Census Families Individuals and Seniors - opens in a new browser window." 18 As of 2018, changes in how some provincial refundable tax credits aimed at seniors are tabulated could affect statistics for provincial refundable tax credits in New Brunswick, Ontario, Manitoba, Saskatchewan, and Alberta. These changes also apply to British Columbia as of 2021. For more details consult the glossary section of the Technical Reference Guide for the Annual Income Estimates for Census Families Individuals and Seniors - opens in a new browser window." 19 Other government transfers includes the Working Income Tax Benefit from 2010 to 2018, the Children’s Fitness Tax Credit for 2015 and 2016, the Eligible Educator School Supply Tax Credit as of 2016, the Refundable Medical Expense Supplement as of 2018, the Climate Action Incentive (for select provinces) as of 2018, Canada Workers Benefit (which replaced the Working Income Tax Benefit) as of 2019, Canada training credit as of 2020, Canadian journalism labour tax credit as of 2020, COVID benefits as of 2020 and other refundable credits as of 2021. 20 Private pensions include pension benefits other than Old Age Security (OAS), Canada Pension Plan (CPP) and Quebec Pension Plan (QPP) benefits. 21 RRSP income is money withdrawn from a Registered Retirement Savings Plan (RRSP), either as a lump sum or as a periodic payment. Only RRSP income of persons aged 65 years or older is included. 22 Other income includes taxable income not reported elsewhere, such as net rental income, support payments, retiring allowances and scholarships.

Abstract

The Employment and Unemployment surveys of National sample Survey (NSS) are primary sources of data on various indicators of labour force at National and State levels. These are used for planning, policy formulation, decision support and as input for further statistical exercises by various Government organizations, academicians, researchers and scholars. NSS surveys on employment and un-employment with large sample size of households have been conducted quinquennially from 27th. round(October'1972 - September'1973) onwards. Cotinuing in this series the fourth such all-india survey on the situation of employment and unemployment in India was carried out during the period july 1987 - june 1988 . The working Group set up for planning of the entire scheme of the survey, among other things, examined also in detail some of the key results generated from the 38th round data and recommended some stream-lining of the 38th round schedule for the use in the 43rd round. Further, it felt no need for changing the engaging the easting conceptual frame work. However, some additional items were recommended to be included in the schedule to obtain the necessary and relevant information for generating results to see the effects on participation rates in view of the ILO suggestions.5.0.1. The NSSO Governing Council approved the recommendations of the working Group and also the schedule of enquiry in its 44th meeting held on 16 January, 1987. In this survey, a nation-wide enquiry was conducted to provide estimates on various characteristics pertaining to employment and unemployment in India and some characteristics associated with them at the national and state levels. Information on various facets of employment and unemployment in India was collected through a schedule of enquiry (schedule 10).

Geographic coverage

The survey covered the whole of Indian Union excepting i) Ladakh and Kargil districts of Jammu & Kashmir ii) Rural areas of Nagaland

Analysis unit

Randomly selected households based on sampling procedure and members of the household

Universe

The survey used the interview method of data collection from a sample of randomly selected households and members of the household

Kind of data

Sample survey data [ssd]

Sampling procedure

It may be mentioned here that in order to net more households of the upper income bracket in the Sample , significant changes have been made in the sample design in this round (compares to the design of the 38th round).

SAMPLE DESIGN AND SAMPLE SIZE The survey had a two-stage stratified design. The first stage units (f.s.u.'s) are villages in the rural sector and urban blocks in the urban sector. The second stage units are households in both the sectors. Sampling frame for f.s.u.'s : The lists of 1981 census villages constituted the sampling frame for rural sector in most districts. But the 1981 census frame could not be used for a few districts because, either the 1981 census was not held there or the list of 1981 census villages could not be obtained or the lists obtained from the census authorities were found to be grossly incomplete. In such cases 1971 census frame were used. In the urban sector , the Urban Frame Survey (U.F.S.) blocks constituted the sampling frame. STRATIFICATION : States were first divided into agro-economic regions which are groups of contiguous districts , similar with respect to population density and crop pattern. In Gujarat, however , some districts have been split for the purpose of region formation In consideration of the location of dry areas and the distribution of the tribal population in the state. The composition of the regions is given in the Appendix. RURAL SECTOR: In the rural sector, within each region, each district with 1981Census rural population less 1.8 million formed a single stratum. Districts with larger population were divided into two or more strata, depending on population, by grouping contiguous tehsils similar, as for as possible, in respect of rural population Density and crop pattern. (In Gujarat, however , in the case of districts extending over more than one region, even if the rural population was less than 1.8 million, the portion of a district falling in each region constituted a separate stratum. Further ,in Assam the old "basic strata" formed on the basis of 1971 census rural population exactly in the above manner, but with cut-off population as 1.5 million have been retained as the strata for rural sampling.)
URBAN SECTOR : In the urban sector , strata were formed , again within NSS region , on the basis of the population size class of towns . Each city with population 10 lakhs or more is self-representative , as in the earlier rounds . For the purpose of stratification, in towns with '81 census population 4 lakhs or more , the blocks have been divided into two categories , viz . : One consisting of blocks in areas inhabited by the relatively affluent section of the population and the other consisting of the remaining blocks. The strata within each region were constituted as follows :

Table (1.2)  : Composition of urban strata

Stratum population class of town

number

(1) (2)

1 all towns with population less than 50,000 2 -do- 50,000 - 199,999 3 -do- 200,000 - 399,999 4 -do- 400,000 - 999,999 ( affluent area) 5 (other area) 6 a single city with population 1 million and above (affluent area)
7 " (other area) 8 another city with population 1 million and above
9 " (other area)

Note : There is no region with more than one city with population 1 million and above. The stratum number have been retained as above even if in some regions some of the strata are empty. Allocation for first stage units : The total all-India sample size was allocated to the states /U.T.'s proportionate to the strength of central field staff. This was allocated to the rural and urban sectors considering the relative size of the rural and urban population. Now the rural samples were allocated to the rural strata in proportion to rural population. The urban samples were allocated to the urban strata in proportion to urban population with double weight age given to those strata of towns with population 4 lakhs or more which lie in area inhabited by the relatively affluent section. All allocations have been adjusted such that the sample size for stratum was at least a multiple of 4 (preferably multiple of 8) and the total sample size of a region is a multiple of 8 for the rural and urban sectors separately.
Selection of f.s.u.'s : The sample villages have been selected circular systematically with probability proportional to population in the form of two independent interpenetrating sub-samples (IPNS) . The sample blocks have been selected circular systematically with equal probability , also in the form of two IPNS' s. As regards the rural areas of Arunachal Pradesh, the procedure of 'cluster sampling' was:- The field staff will be supplied with a list of the nucleus villages of each cluster and they selected the remaining villages of the cluster according to the procedure described in Section Two. The nucleus villages were selected circular systematically with equal probability, in the form of two IPNS 's. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Hamlet-group and sub-blocks : Large villages and blocks were sub- divided into a suitable number of hamlet-groups and sub-blocks respectively having equal population convent and one them was selected at random for surveys. Selection of households : rural : In order to have adequate number of sample households from the affluent section of the society, some new procedures were introduced for selection of sample households, both in the rural and urban sectors. In the rural sector , while listing households, the investigator identified the households in village/ selected hamlet- group which may be considered to be relatively more affluent than the rest. This was done largely on the basis of his own judgment but while exercising his judgment considered factors generally associated with rich people in the localitysuch as : living in large pucca house in well-maintained state, ownership/possession of cultivated/irrigated land in excess of certain norms. ( e.g.20 acres of cultivated land or 10 acres of irrigated land), ownership of motor vehicles and costly consumer durables like T.V. , VCR, VCP AND refrigerator, ownership of large business establishment , etc. Now these "rich" households will form sub-stratum 1. (If the total number of households listed is 80 or more , 10 relatively most affluent households will form sub-stratum 1. If it is below 80, 8 such households will form sub-stratum 1. The remaining households will 'constitute sub-stratum 2. At the time of listing, information relating to each household' s major sources of income will be