Decorating an artificial tree for the New Year gained significant popularity in Russia over the last years. A reversed dynamic was recorded for the natural New Year’s tree, which was named by one quarter of the surveyed population in 2019. On average, 15 percent of participants did not have a New Year’s tree at all over the observed period.

Release Date: 2021-03-11.The Census Bureau has reviewed this data product for unauthorized disclosure of confidential information and has approved the disclosure avoidance practices applied (Approval ID: CBDRB-FY20-424)...Release Schedule:.Data in this file come from estimates of technology use of employer firms from the 2019 Annual Business Survey (ABS) collection. Data are also obtained from administrative records, the 2017 Economic Census, and other economic surveys...Note: The collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2019 ABS collection year produces statistics for the 2018 reference year. The "Year" column in the table is the reference year...For more information about ABS planned data product releases, see Tentative ABS Schedule...Key Table Information:.This is one of twenty tables in the 2019 ABS technology series to provide detailed technology use and production statistics with select economic and demographic characteristics of businesses (TCB) for U.S. employer firms that reported the sex, ethnicity, race, and veteran status for up to four persons owning the largest percentage(s) of the business. The data include U.S. firms with paid employees operating during the reference year with receipts of $1,000 or more, which are classified in the North American Industry Classification System (NAICS), Sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Employer firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. Firms are asked to report their employees as of the March 12 pay period...Data Items and Other Identifying Records:.Data include estimates on:.Number of employer firms (firms with paid employees). Percent of employer firms (%). Sales and receipts of employer firms (reported in $1,000s of dollars). Percent of sales and receipts of employer firms (%). Number of employees (during the March 12 pay period). Percent of employees (%). Annual payroll (reported in $1,000s of dollars). Percent of annual payroll (%)...Data Notes:.. For each technology group, this table shows estimates from only the subsample of firms that reported low use, moderate use, or high use for the corresponding technology tabulated in this table, AB1800TCB01A, for reference year 2018. For example, the firms that reported either low, moderate, or high use of Artificial Intelligence technology from table AB1800TCB01A are the subsample of firms that also responded to and are tabulated for the Artificial Intelligence estimates in this table.. Percentage statistics are based on the share of firms that reported data in each technology group. The total number of firms that reported data for each technology are captured in the Total Reporting counts. For example, the total number of firms that selected any response for the Impact of Artificial Intelligence Technology Use on the Type of Worker on the 2019 ABS questionnaire, represent the number of firms in Artificial Intelligence: Total reporting statistic....Technology Characteristics:.The ABS was designed to include select questions about technology, innovation, and research and development from multiple reference periods and to incorporate new content each survey year based on topics of relevance...Estimates are derived from firms reporting the characteristics tabulated in this dataset. Percentages are always based on total reporting (defined above) and are not recalculated when the dataset is resorted...Industry and Geography Coverage:.The data are shown for the total for all sectors (00) and the 2-digit NAICS code levels for:..United States. States and the District of Columbia...Footnotes:.Footnote 660 - Agriculture, forestry, fishing and hunting (Sector 11): Crop and Animal Production (NAICS 111 and 112) are out of scope..Footnote 661 - Transportation and warehousing (Sector 48-49): Rail Transportation (NAICS 482) and the Postal Service (NAICS 491) are out of scope..Footnote 662 - Finance and insurance (Sector 52): Monetary Authorities-Central Banks (NAICS 521) and Funds, Trusts, and Other Financial Vehicles (NAICS 525) are out of scope..Footnote 663 - Other services, except public administration (Sector 81): Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813) and Private Households (NAICS 814) are out of scope...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/abs/data/2018/AB1800TCB04A.zip...API Information:.Annual Business Survey data are housed in the Census Bureau API. For more information, see https://api.census.gov/data/2018/abstcb.html...Methodology:.To maintain confidentiality, the Census Bureau suppresses data to protect the identity of any ...

According to a survey on consumption trend during Spring Festival in China conducted in December 2019, approximately 39 percent of the Chinese respondents who were born between 1980 and 1989 said that they would most likely buy a smart device as a gift for themselves. The survey also revealed that the respondents from this age group had a budget of up to 3,000 yuan (approximately 426 U.S. dollars) to buy gifts for themselves.

Maine/New Hampshire Inshore Trawl Survey: Catch Data from the F/V Robert Michael,F/V Tara Lynn NEC-JS2000-1 from the the Maine and New Hampshire coasts, 2000-2004 (NEC-CoopRes project) access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=Two surveys were conducted; one in the fall beginning October 30, 2000 and a second in the spring, beginning on April 23, 2001. Each cruise required 25 days over a period of five weeks. . Descriptive data, including geo- references, trawl duration, depth, salinity and temperature for each survey are presented. Obviously, a single year of data affords no ability to develop a time series to be used for more than anything but the most general of conclusions. Also, since this was the first year, the first few weeks of the fall survey, especially, was a period in which the crew was testing and developing skills, procedures, and methods. Nevertheless, data collected from this first year does reveal some interesting findings. Ninety-nine taxonomic groups of fish and invertebrates were caught. For this report, we have selected examples for which we can report results. The complete catch result summaries are presented by species for each stratum. Fall 2000 Summary Seventy eight of the 96 planned tows were made. Untowable bottom and presence of fixed gear prevented us from towing the 18 not towed. The volume of the total mixed catch varied from a minimum of 4 kg to a maximum of 640 kg per tow. The average weight of catch was about 122 kg per tow. The total number of species caught in the fall was 80 with a low of 7 and high of 31 in any particular tow. Relative coastwide ranking for the top 10 species is reported below in descending order. By Number | By Weight
---|---
Herring* | Silver Hake*
Silver Hake* | Lobster
Mixed Shrimp | Herring*
Alewife | Dogfish*
Lobster | Alewife
Rainbow Smelt | Winter Flounder*
Scallop* | Red Hake*
Winter Flounder* | Longhorn Sculpin
Longhorn Sculpin | Monkfish*
Menhaden | White Hake*
* Species managed by the New England Fisheries Management Council

Species managed by the New England Fisheries Management Council Spring 2001 One hundred eleven tows were made in the spring. We were able to achieve this by anticipating untowable bottom and planning 1 extra randomly selected alternate tow per stratum for a total of 115 planned tows. Weight of total mixed catch varied from a minimum of 4.5 kg to a maximum of 5,007 kg per tow, with an average of 87 kg per tow. Number of species caught per tow ranged from 4 to 31. Total number of species caught during the Spring 2001 survey was 87. Relative coastwide ranking for the top 10 species is reported below in descending order. By Number | By Weight
---|---
Herring* | Herring*
Mixed Shrimp | Lobster
Alewife | Longhorn Sculpin
Silver Hake* | Sea Cucumber
Blue-back herring | Silver Hake*
Longhorn Sculpin | Alewife
Lobster | Winter Flounder*
Scallops* | American Plaice*
Winter Flounder* | Sea Scallop*
American Plaice* | Sea Raven
* Species managed by the New England Fisheries Management Council.

With 61 finfish species and 38 types of invertebrates sampled, a species by species presentation of results is not practical for this report. However, following are some examples of the sorts of results that this survey can produce. Note that we include some examples of non-groundfish species to demonstrate another attribute of a fisheries independent survey; that the survey can provide information beneficial for management of the system and not focus soley on a select suite of target species. Information is gathered on an ecological community level. Rainbow smelt, for example, may not be directly exploited commercially but it provides enjoyment to upland recreational anglers and on an ecological level is a forage species for higher trophic levels. Sculpins, cartilaginous species, and predator-prey ratios, for example, have been used as indicators of system-wide health. Landings data do not include information on these species. Over the long term, system shifts as a result of climate change may be assessed as exemplified when the Fall Survey encountered species such as barracudina and scup that historically have not been common north of Cape Ann, Massachusetts. By looking at population structure as well as distribution, the importance of shallow inshore habitat for cod becomes clear. The Fall 2000 portion of Figure 8 shows a year class of cod that probably hatched in February-April 1999. Most are still in the shallowest strata. As the fish grow, they move offshore and disperse into deeper water. In the Spring 2001 portion of Figure 8, one can see young of the year in the shallow strata. Offshore in the spring, there appears to be more cod in the deeper strata but certainly not in the numbers that were observed the previous fall. From a single year\u2019s tow, it is not possible to know whether or not the spring survey missed the next year class due to late inshore migration or whether there simply was a weak year class. Cod, and most other groundfish species, move into deeper (warmer) water in late fall to return in the spring as inshore waters warm. Whether the fish were still farther offshore and had not migrated in at the time of the spring survey, we cannot determine. The spring of 2001 was cooler than normal. Subsequent year\u2019s tows and comparisons with the offshore NMFS data set will help to resolve this question. As the Maine spring spawning closure for groundfish 'sunsets' at the end of 2002, trawl survey data will be used to evaluate the need to extend the closure during the next Maine legislative session. awards_0_award_nid=55021 awards_0_award_number=unknown NEC-CoopRes NEC awards_0_funder_name=NorthEast Consortium awards_0_funding_acronym=NEC awards_0_funding_source_nid=383 cdm_data_type=Other comment=Inshore Trawl Catch data P.I. John Sowles Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.2797.1 Easternmost_Easting=-66.9519 geospatial_lat_max=44.7117 geospatial_lat_min=42.863 geospatial_lat_units=degrees_north geospatial_lon_max=-66.9519 geospatial_lon_min=-70.7545 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/2797 institution=BCO-DMO instruments_0_acronym=Custom Trawl instruments_0_dataset_instrument_description=The net is a scaled down version of the most common shrimp and modified shrimp net design used by Maine’s dragger fleet. The net was designed by the vessel owner and his net designer, Jeff Flagg, to fish effectively, be easily maintained, and be towed by vessels ranging from 45 - 70 ft. with nominal horsepower. Net tapers were cut to permit the shape of the net to get maximum height, while allowing the net to remain tight on the bottom. The net is shackled from the footrope to the frame using two 3/8-inch shackles to a banded wire that runs parallel with the footrope. Heavy rubber wing bobbins retard bottom wing lift. The top leg is 3/8th inch wire, 15 fathoms long, and the bottom leg is 15 fathoms. The net is constructed of 2 inch mesh overall with a 1/2 inch mesh liner in the cod end. Doors are #7.5 Bisons. The 70 ft. footrope includes 70’ of 6 inch cookies. Chain sweeps were not used. Between surveys, the net is sent back to the manufacturer where it is returned to specification. instruments_0_dataset_instrument_nid=5480 instruments_0_description=A net towed through the water column designed to sample free-swimming nekton or fish, varies in design depending on the research project. instruments_0_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L05/current/23/ instruments_0_instrument_name=Trawl_custom instruments_0_instrument_nid=634 instruments_0_supplied_name=Trawl_custom metadata_source=https://www.bco-dmo.org/api/dataset/2797 Northernmost_Northing=44.7117 param_mapping={'2797': {'lat': 'master - latitude', 'lon': 'master - longitude', 'depth_fm': 'flag - depth'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/2797/parameters people_0_person_name=John Sowles people_0_person_nid=50906 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Woods Hole Oceanographic Institution people_1_affiliation_acronym=WHOI BCO-DMO people_1_person_name=Nancy Copley people_1_person_nid=50396 people_1_role=BCO-DMO Data Manager people_1_role_type=related project=NEC-CoopRes projects_0_acronym=NEC-CoopRes projects_0_description=The Northeast Consortium encourages and funds cooperative research and monitoring projects in the Gulf of Maine and Georges Bank that have effective, equal partnerships among fishermen, scientists, educators, and marine resource managers. The Northeast Consortium seeks to fund projects that will be conducted in a responsible manner. Cooperative research projects are designed to minimize any negative impacts to ecosystems or marine organisms, and be consistent with accepted ethical research practices, including the use of animals and human subjects in research, scrutiny of research protocols by an institutional board of review, etc. projects_0_geolocation=Georges Bank, Gulf of Maine projects_0_name=Northeast Consortium: Cooperative Research projects_0_project_nid=2045 projects_0_project_website=http://northeastconsortium.org/ projects_0_start_date=1999-01 sourceUrl=(local files) Southernmost_Northing=42.863 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-70.7545 xml_source=osprey2erddap.update_xml() v1.3

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly data; Secure Access datasets; household datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.

An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022" style="background-color: rgb(255, 255, 255);">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

Abstract copyright UK Data Service and data collection copyright owner. Background The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation. Secure Access QLFS data Secure Access datasets for the QLFS are available from the April-June 1992 quarter, and include additional, detailed variables not included in the standard 'End User Licence' (EUL) versions (see under GN 33246). Extra variables that typically can be found in the Secure Access versions but not in the EUL relate to:geography (see 'Spatial Units' below)date of birth, including dayeducation and training: including type of 'other qualifications', more detail regarding the number of O'levels/GCSE passes, type of qualification gained in last 12 months, class of first degree, type of degree held, UK country of highest degree, type of current educational institution, level of Welsh baccalaureate, activities to improve knowledge or skills in last 12 months, attendance at adult learning taught courses, attendance at leisure or educational classes, self-teaching, payment of job-related training feeshousehold and family characteristics: including number of family units (and extended family units) with dependent children only, and with non-dependent children only, total number of family units with more than one person, total number of eligible people, type of household, type of family unit, number of bedroomsemployment: including industry code of main job, whether working full-time or part-time, reason job is temporary, payment of own National Insurance and tax, when started working at previous job, whether paid or self-employed in previous job, contracts with employment agencyunemployment and job hunting: including main reason for not being employed prior to current job, reasons for leaving job (provision of care or other personal/family reasons), use of internet for job hunting, if and when will work in the futuretemporary leave from work: including proportion of salary received and duration of leaveaccidents at work and work-related health problemsnationality, national identity and country of birth: including whether lived continuously in UK, month of most recent arrival to UK, frequency of Welsh speakingoccurrence of learning difficulty or disabilitybenefits, including additional variables on type of benefits claimed and tax credit paymentsSecure Access versions of QLFS household datasets are available from 2009 under SN 7674. Prospective users of a Secure Access version of the QLFS will need to fulfil additional requirements, commencing with the completion of an extra application form to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access users must also complete face-to-face training and agree to Secure Access' User Agreement (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access version. Well-Being variables are not included in the LFS Users should note that subjective well-being variables (Satis, Worth, Happy, Anxious and Sad) are not available on the LFS, despite being referenced in the questionnaire. Users who wish to analyse well-being variables should apply for the Annual Population Survey instead (see SNs 6721 and 7961). LFS Documentation The documentation available from the Archive to accompany LFS datasets largely consists of the relevant versions of each volume of the user guide. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.The study documentation presented in the Documentation section includes the most recent documentation for the LFS only, due to available space. Documentation for previous years is provided alongside the data for access and is also available upon request. Variables DISEA and LNGLST Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.Latest Edition InformationFor the thirty-eighth edition (October 2023), a new data file for April-June 2023 and a new 2023 variable catalogue have been added to the study. Main Topics: The QLFS questionnaire comprises a 'core' of questions which are included in every survey, together with some 'non-core' questions which vary from quarter to quarter. The questionnaire can be split into two main parts. The first part contains questions on the respondent's household, family structure, basic housing information and demographic details of household members. The second part contains questions covering economic activity, education and health, and also may include a few questions asked on behalf of other government departments (for example the Department for Work and Pensions and the Home Office). Until 1997, the questions on health focussed on problems that affect the respondent's work. Since then, the questions have covered all health problems. Detailed questions on income have also been included in each quarter since 1993. The basic questionnaire is revised each year, and a new version published, along with a transitional version that details changes from the previous year's questionnaire. Four sampling frames are used. See documentation for details.

Labour Force Survey 2007 - merged year file.

As of the 1st quarter of 1972, SSB has conducted official quarterly labour force surveys (AKU). These surveys aim to give the labour force authorities (and other people interested) knowledge of the occupational structure of the population and how it develops over time. The surveys are meant to give a foundation and statistical material for occupational prognoses and labour research. The samples in AKU are from 1992 representative at county level. In the period 1972-1991 they were representative on county pair level.

As from January 2006 some major changes were introduced to AKU in order to enhance its comparability to similar surveys in other countries. The changes consist of minor definitional adjustments of unemployment, some adjustments and enlargement of the questionnaire and a change in age definition (age at reference point instead of at the end of the year). Simultaneously the lower age limit to be included in AKU was lowered from 16 to 15 years. This led to some breaks in the time series in the aforementioned areas.

Originally, AKU respondents were interviewed in two consecutive quarters of a year, followed by a pause of two quarters, and then another two quarters of interviews. The sample was approximately 10-11.000 respondents in each quarter up until 1988. Originally, AKU was intended to be an analytical supplement to the monthly occupational statistics that was based on the social security membership index file. However, the social security-based statistics disappeared when the sickness benefit was included in the National Insurance as of 1st of January 1971, and AKU has after gradually developed into the most significant source of knowledge of the state of the labour market and its development.

In 1975, Statistics Norway changed the sampling frame of survey research, see article 37: “Om bruk av stikkprøver ved kontoret for intervjuundersøkelser”, SSB (About the Use of Random Samples at the Office for Survey Research, Statistics Norway) by Steinar Tamsfoss, and SØS 33: “Prinsipper og metoder for Statistisk sentralbyrås utvalgsundersøkelse (Principles and Methods for Statistics Norway's sample research) by Ib Thomsen. Simultaneously, the method for estimation of inflation to national numbers was changed, so that reasonable numbers for regions do exist from 1975 and onwards. The change in 1975 led to a different way of interviewing in groups. This caused amongst other things a break with the AKU panel systematics.

In the AKU survey of 1976, a slightly changed questionnaire was introduced. Also, there was a return to the original 6-quarter rotation scheme. The new questionnaire implied a better identification of family workers and persons that are temporarily without paid work. Thus, 30-35 000 more people were defined as employed. The group of "job-seekers without income" were also extended to include persons that were on an involuntary leave of absence. The questions concerning underemployment and “over employment” in the original questionnaire were abandoned.

From the 1st quarter of 1987, the estimation method (inflation to national numbers) was slightly changed. There was also a minor adjustment in the definition of employment. In order to ensure that the numbers were to be comparable to earlier surveys, new versions of the 1980-1986 AKU-files were drawn up. Consequently two versions of the 1980-1987 files - respectively with the old and new methods of estimation - exist. The “old” means that the data are comparable to the original numbers published in the period of 1972 - 1987, whilst the “new” implies that the data are comparable to numbers published after 1987.

Between the 1st and 2nd quarter of 1988, the AKU file description was changed. The variable “Labour-market status” was given a different coding. In addition, adjustments in the data collections were made - from interviewing a specific week every quarter to carry out continuous weekly interviews. SSB also started up an escalation scheme to increase the sample size. This affected the weights, and from the 2nd quarter of 1988, these were recalculated monthly. To balance out the quarterly or yearly files to total national numbers, the monthly weights therefore had to be divided in three or twelve to give the correct total number.

In 1996, AKU was significantly revised: The questionnaire, the file description and the standard for coding of industry and occupation. The data collection also changed to CATI - Computer Assisted Telephone Interviewing. A new classification of industry was put into use (NOS C 182, based on the EU standard NACE, Rev.1). This standard was updated in 2002 and 2007. Also, the new Norwegian standard classification of occupations (STYRK) based on ISCO 88 was used from 1996 and onwards. The variable indicating socio-economic status was omitted, as a...

Release Date: 2023-09-28.Release Schedule:.The data in this file was released in September 2023...Key Table Information:.The estimates presented are based on data from the 2021 Vehicle Inventory and Use Survey (VIUS). These estimates only cover vehicles registered during 2001 in one of the fifty United States (except New Hampshire) or the District of Columbia that are classified by vehicle manufacturers as trucks, minivans, vans, or sports utility vehicles. Additionally, vehicles owned by federal, state, and local governments, ambulances, buses, motor homes, farm tractors, unpowered trailer units, and any vehicle reported to have been disposed prior to January 1, 2021, are considered out of scope for the VIUS..There are no additional scope conditions for the estimates on this table..Estimates may not be additive due to rounding..The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7527235, Disclosure Review Board (DRB) approval number: CBDRB-FY23-032)...Data Items and Other Identifying Records:.Primary characteristics that appear in this table:..Body/Trailer type.Model year.Gross vehicle weight rating class.Transmission.Cylinders.Primary axle configuration.Towing capacity.Months operated.Vehicle Acquisition.Lease status.Driving axles.Cab heights...Secondary characteristics that appear in this table:..Standard features (under body/trailer type).Fuel economy features (under body/trailer type).Driving control assistance features (under body/trailer type).Collision warning features (under body/trailer type).Collision intervention features (under body/trailer type).Parking assistance features (under body/trailer type).Other driver assistance systems features (under body/trailer type).Other features (under body/trailer type)...Estimates on this table:..Number of vehicles (thousands).Vehicle miles (millions).Average miles per vehicle (thousands).Coefficients of variation for all of the above estimates (percentages)...Data Item Notes:..Trailer Configuration.Estimates of 'Single Trailer Pulled', 'Double Trailer Pulled', 'Triple Trailer Pulled', and 'Trailer Pulled' exclude vehicles that pulled a trailer for less than half of all miles driven. Estimates of 'No Trailer Pulled or Vehicle Not Used' include vehicles that pulled a trailer for less than half of all miles driven..Standard Features, Fuel Economy Features, Driving Control Assistance Features, Collision Warning Features, Collision Intervention Features, Parking Assistance Features, Other Driver Assistance Systems Features, Other Features.Detail lines do not add to total because multiple responses were possible..Model Year, Gross Vehicle Weight Rating Class, Cylinders.Data were derived from administrative records....Geography Coverage:.On this table, geography refers to the address on a given vehicle's registration..Data are shown for the United States, 49 states (every state except New Hampshire), and the District of Columbia..Note that estimates at the 'United States' level also do not include vehicles with registration addresses in New Hampshire because the state did not consent to sharing registrant data for this survey. See https://www.census.gov/programs-surveys/vius/data.html for model-based estimates at the United States level that do include New Hampshire...Industry Coverage:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/vius/data/2021/VIUS211A.zip..API Information:.Vehicle Inventory and Use Survey data are housed in the Census Bureau API. For more information, see https://api.census.gov/data/2021/viusa.html..Methodology:.Estimates are based on a sample of in-scope vehicles and are subject to both sampling and nonsampling error. Estimated measures of sampling variability are provided in the tables. For information on sampling or nonsampling error or and other design and methodological details, see Vehicle Inventory and Use Survey (VIUS): Technical Documentation: Vehicle Inventory and Use Survey Methodology...Symbols:.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see Vehicle Inventory and Use Survey (VIUS): Technical Documentation: Vehicle Inventory and Use Survey Methodology..Z - Round to Zero..X - Not Applicable..For a complete list of all economic programs symbols, see the Economic Census: Technical Documentation: Data Dictionary. ..Source:.Suggested Citation: U.S. Department of Transportation, Bureau of Transportation Statistics; and, U.S. Department of Commerce, U.S. Census Bureau. (9/28/23). All Vehicles by Registration State, Vehicle Typ...

Abstract copyright UK Data Service and data collection copyright owner.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

New reweighting policy
Following the new reweighting policy ONS has reviewed the latest population estimates made available during 2019 and have decided not to carry out a 2019 LFS and APS reweighting exercise. Therefore, the next reweighting exercise will take place in 2020. These will incorporate the 2019 Sub-National Population Projection data (published in May 2020) and 2019 Mid-Year Estimates (published in June 2020). It is expected that reweighted Labour Market aggregates and microdata will be published towards the end of 2020/early 2021.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

Additional data derived from the QLFS
The Archive also holds further QLFS series: End User Licence (EUL) quarterly data; Secure Access datasets; household datasets; quarterly, annual and ad hoc module datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.

Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will...

The National Crime Victimization Survey (NCVS), previously called the National Crime Survey (NCS), has been collecting data on personal and household victimization through an ongoing survey of a nationally-representative sample of residential addresses since 1973. The NCVS was designed with four primary objectives: (1) to develop detailed information about the victims and consequences of crime, (2) to estimate the number and types of crimes not reported to the police, (3) to provide uniform measures of selected types of crimes, and (4) to permit comparisons over time and types of areas. Beginning in 1992, the survey categorizes crimes as "personal" or "property." Personal crimes include rape and sexual assault, robbery, aggravated and simple assault, and purse-snatching/pocket-picking, while property crimes include burglary, theft, motor vehicle theft, and vandalism. Each respondent is asked a series of screen questions designed to determine whether she or he was victimized during the six-month period preceding the first day of the month of the interview. A "household respondent" is also asked to report on crimes against the household as a whole (e.g., burglary, motor vehicle theft). The data include type of crime, month, time, and location of the crime, relationship between victim and offender, characteristics of the offender, self-protective actions taken by the victim during the incident and results of those actions, consequences of the victimization, type of property lost, whether the crime was reported to police and reasons for reporting or not reporting, and offender use of weapons, drugs, and alcohol. Basic demographic information such as age, race, gender, and income is also collected to enable analysis of crime by various subpopulations. This dataset represents the revised concatenated version of the NCVS on a collection year basis for 1992-2016. A collection year contains records from interviews conducted in the 12 months of the given year. Under the collection year format, victimizations are counted in the year the interview is conducted, regardless of the year when the crime incident occurred. The 2016 National Crime Victimization Survey (NCVS) violent and property crime estimates were significantly higher than 2015, but it was not possible to determine the degree to which the change in rates resulted from the sample redesign rather than real changes in U.S. victimization levels. Therefore, the Bureau of Justice Statistics (BJS) examined the 2015 and 2016 victimization rates separately for new and continuing sample counties in the 2016 Criminal Victimization bulletin. The BJS requested that the U.S. Census Bureau create a 2016 revised file with outgoing county interviews from July-December 2015, continuing county interviews from January-June 2016, and all interviews (continuing and new counties) from July-December 2016. In other words, the outgoing 2015 cases replaced the new 2016 cases in the first half of 2016. The files in this study serve as a separate research file to allow data users to make comparisons between 2015, 2016, and 2017 NCVS estimates using a nationally representative sample. It provides a sample that still represents the entire country but does not have the inflated crime rates seen in the new counties in 2016. For additional information on the dataset, please see the documentation for the data from the most current year of the NCVS, ICPSR Study 37296.

Release Date: 2023-09-28.Release Schedule:.The data in this file was released in September 2023...Key Table Information:.The estimates presented are based on data from the 2021 Vehicle Inventory and Use Survey (VIUS)..These estimates only cover vehicles registered during 2021 in one of the fifty United States (except New Hampshire) or the District of Columbia that are classified by vehicle manufacturers as trucks, minivans, vans, or sport utility vehicles. Additionally, vehicles owned by federal, state, and local governments, ambulances, buses, motor homes, farm tractors, unpowered trailer units, and any vehicle reported to have been disposed prior to January 1, 2021, are considered out of scope for the VIUS..There are no additional scope conditions for the estimates on this table..Estimates may not be additive due to rounding..The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7527235, Disclosure Review Board (DRB) approval number: CBDRB-FY23-032)...Data Items and Other Identifying Records:.Primary characteristics that appear in this table:..Body/Trailer type.Model year.Transmission.Cylinders.Towing capacity.Months operated.Vehicle Acquisition.Lease status.Driving axles.Cab height.Vehicle type and trailer configuration...Secondary characteristics that appear in this table:.Standard features (under body/trailer type).Fuel economy features (under body/trailer type).Driving control assistance features (under body/trailer type).Collision warning features (under body/trailer type).Collision intervention features (under body/trailer type).Parking assistance features (under body/trailer type).Other driver assistance systems features (under body/trailer type).Other features (under body/trailer type).Primary axle configuration (under vehicle type and trailer configuration)...Estimates on this table:.Number of vehicles (thousands).Vehicle miles (millions).Average miles per vehicle (thousands).Coefficients of variation for all of the above estimates (percentages)...Data Item Notes:..Vehicle Type and Trailer Configuration.Estimates of 'Truck Tractor, Single Trailer Pulled', 'Truck Tractor, Double Trailer Pulled', 'Truck Tractor, Triple Trailer Pulled', and 'Single Unit Truck, Trailer Pulled' exclude vehicles that pulled a trailer for less than half of all miles driven. Estimates of 'Any Vehicle Type, No Trailer Pulled or Vehicle Not in Use' include vehicles that pulled a trailer for less than half of all miles driven..Model Year, Cylinders.Data were derived from administrative records....Geography Coverage:.On this table, geography refers to the address on a given vehicle's registration..Data are shown for the United States, 49 states (every state except New Hampshire), and the District of Columbia..Note that estimates at the 'United States' level also do not include vehicles with registration addresses in New Hampshire because the state did not consent to sharing registrant data for this survey. See https://www.census.gov/programs-surveys/vius/data.html for model-based estimates at the United States level that do include New Hampshire...Industry Coverage:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/vius/data/2021/VIUS213A.zip..API Information:.Vehicle Inventory and Use Survey data are housed in the Census Bureau API. For more information, see https://api.census.gov/data/2021/viusa.html..Methodology:.Estimates are based on a sample of in-scope vehicles and are subject to both sampling and nonsampling error. Estimated measures of sampling variability are provided in the tables. For information on sampling or nonsampling error and other design and methodological details, see Vehicle Inventory and Use Survey (VIUS): Technical Documentation: Vehicle Inventory and Use Survey Methodology...Symbols:.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see Vehicle Inventory and Use Survey (VIUS): Technical Documentation: Vehicle Inventory and Use Survey Methodology..Z - Rounds to Zero..X - Not Applicable..For a complete list of all economic programs symbols, see Economic Census: Technical Documentation: Data Dictionary...Source:.Suggested Citation: U.S. Department of Transportation, Bureau of Transportation Statistics; and, U.S. Department of Commerce, U.S. Census Bureau. (9/28/23). All Vehicles by Registration State and Vehicle Size: 2021 [VIUSA2021]. 2021 Vehicle Inventory and Use Survey. U.S. Department of Transportation, Bureau of Transportation Statistics; U.S. Department of Commerce, U.S. Census Bureau; U.S. Depar...

Labour Force Survey 2001 - merged year file.

As of the 1st quarter of 1972, SSB has conducted official quarterly labour force surveys (AKU). These surveys aim to give the labour force authorities (and other people interested) knowledge of the occupational structure of the population and how it develops over time. The surveys are meant to give a foundation and statistical material for occupational prognoses and labour research. The samples in AKU are from 1992 representative at county level. In the period 1972-1991 they were representative on county pair level.

Originally, AKU respondents were interviewed in two consecutive quarters of a year, followed by a pause of two quarters, and then another two quarters of interviews. The sample was approximately 10-11.000 respondents in each quarter up until 1988. Originally, AKU was intended to be an analytical supplement to the monthly occupational statistics that was based on the social security membership index file. However, the social security-based statistics disappeared when the sickness benefit was included in the National Insurance as of 1st of January 1971, and AKU has after gradually developed into the most significant source of knowledge of the state of the labour market and its development.

In 1975, Statistics Norway changed the sampling frame of survey research, see article 37: “Om bruk av stikkprøver ved kontoret for intervjuundersøkelser”, SSB (About the Use of Random Samples at the Office for Survey Research, Statistics Norway) by Steinar Tamsfoss, and SØS 33: “Prinsipper og metoder for Statistisk sentralbyrås utvalgsundersøkelse (Principles and Methods for Statistics Norway's sample research) by Ib Thomsen. Simultaneously, the method for estimation of inflation to national numbers was changed, so that reasonable numbers for regions do exist from 1975 and onwards. The change in 1975 led to a different way of interviewing in groups. This caused amongst other things a break with the AKU panel systematics.

In the AKU survey of 1976, a slightly changed questionnaire was introduced. Also, there was a return to the original 6-quarter rotation scheme. The new questionnaire implied a better identification of family workers and persons that are temporarily without paid work. Thus, 30-35 000 more people were defined as employed. The group of "job-seekers without income" were also extended to include persons that were on an involuntary leave of absence. The questions concerning underemployment and “over employment” in the original questionnaire were abandoned.

From the 1st quarter of 1987, the estimation method (inflation to national numbers) was slightly changed. There was also a minor adjustment in the definition of employment. In order to ensure that the numbers were to be comparable to earlier surveys, new versions of the 1980-1986 AKU-files were drawn up. Consequently two versions of the 1980-1987 files - respectively with the old and new methods of estimation - exist. The “old” means that the data are comparable to the original numbers published in the period of 1972 - 1987, whilst the “new” implies that the data are comparable to numbers published after 1987.

Between the 1st and 2nd quarter of 1988, the AKU file description was changed. The variable “Labour-market status” was given a different coding. In addition, adjustments in the data collections were made - from interviewing a specific week every quarter to carry out continuous weekly interviews. SSB also started up an escalation scheme to increase the sample size. This affected the weights, and from the 2nd quarter of 1988, these were recalculated monthly. To balance out the quarterly or yearly files to total national numbers, the monthly weights therefore had to be divided in three or twelve to give the correct total number.

In 1996, AKU was significantly revised: The questionnaire, the file description and the standard for coding of industry and occupation. The data collection also changed to CATI - Computer Assisted Telephone Interviewing. A new classification of industry was put into use (NOS C 182, based on the EU standard NACE, Rev.1). This standard was updated in 2002 and 2007. Also, the new occupational classification (STYRK) based on ISCO 88 was used from 1996 and onwards. The variable indicating socio-economic status was omitted, as a similar variable was not developed in the new occupational classification.

As from January 2006 some major changes were introduced to AKU in order to enhance its comparability to similar surveys in other countries. The changes consist of minor definitional adjustments of unemployment, some adjustments and enlargement of the questionnaire and a change in age definition (age at reference point instead of at the end of the year). Simultaneously the lower age limit to be included in AKU was lowered from 16 to 15 years. This led to some breaks in the time series in the mentioned areas.

According to a survey conducted in China in January 2023, nearly half of the respondents intended to cook some food at home for the upcoming Lunar New Year's dinner, but also to purchase some ready-to-eat dishes. Meanwhile, only 25 percent said they were going to prepare all dishes from scratch by themselves.

Abstract copyright UK Data Service and data collection copyright owner.

The Farm Business Survey (FBS) is conducted annually to collect business information from c.2,400 farms in England and Wales. The FBS provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers. The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer etc.

Up to and including the 2001/02 survey, FBS estimates were based on matching of the sample between two adjacent years. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample.

The 2019/20, to 2021/22 survey samples are slightly smaller as COVID-19 impacted data collection. In 2022/23, a new survey contract stipulated a sample of 1,500 farm records for England.

The typology used to determine the FBS farm type classification was revised for 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification has been based on 2007 standard output (SO) coefficients. From 2012 to 2016, FBS farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classifications are based on 2013 SO coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available from the 'FBS Documents' section on the gov.uk Farm Business Survey – technical notes and guidance webpage.

The Farm Business Survey is available under Special Licence access conditions. See the' Access data' section for further details on how to apply for access to the data.

Latest edition information

For the second edition (July 2021) a new version of the database was deposited, with previously unavailable sections F3, K, N and R added. The documentation has also been updated.

Variables cover general and physical farm characteristics, labour, crops (previous and current harvest year, set-aside, by-products, forage and cultivations); miscellaneous receipts, livestock (dairy and beef cattle, sheep, pigs, poultry, miscellaneous livestock), variable and fixed costs, assets, investment, liabilities, income from diversified activities (integrated and semi-integrated into the farm business), farmer and spouse off-farm hours and incomes, subsidies.

Property ID, name(s), document type, and description: BO0326; Big Giant Mine, New Year; map: Property Location Map.

Labour Force Survey 1.quarter 2007-4.quarter 2008, panel

As of the 1st quarter of 1972, SSB has conducted official quarterly labour force surveys (AKU). These surveys aim to give the labour force authorities (and other people interested) knowledge of the occupational structure of the population and how it develops over time. The surveys are meant to give a foundation and statistical material for occupational prognoses and labour research. The samples in AKU are from 1992 representative at county level. In the period 1972-1991 they were representative on county pair level.

As from January 2006 some major changes were introduced to AKU in order to enhance its comparability to similar surveys in other countries. The changes consist of minor definitional adjustments of unemployment and educational level, some adjustments and enlargement of the questionnaire and a change in age definition (age at reference point instead of at the end of the year). Simultaneously the lower age limit to be included in AKU was lowered from 16 to 15 years. This led to some breaks in the time series in the aforementioned areas.

As of the 1st quarter of 2009 the new classification of economic activities: SN2007/ISIC rev 5 replaces SN2002/ISIC Rev 4.

Originally, AKU respondents were interviewed in two consecutive quarters of a year, followed by a pause of two quarters, and then another two quarters of interviews. The sample was approximately 10-11.000 respondents in each quarter up until 1988. Originally, AKU was intended to be an analytical supplement to the monthly occupational statistics that was based on the social security membership index file. However, the social security-based statistics disappeared when the sickness benefit was included in the National Insurance as of 1st of January 1971, and AKU has after gradually developed into the most significant source of knowledge of the state of the labour market and its development.

In 1975, Statistics Norway changed the sampling frame of survey research, see article 37: “Om bruk av stikkprøver ved kontoret for intervjuundersøkelser”, SSB (About the Use of Random Samples at the Office for Survey Research, Statistics Norway) by Steinar Tamsfoss, and SØS 33: “Prinsipper og metoder for Statistisk sentralbyrås utvalgsundersøkelse (Principles and Methods for Statistics Norway's sample research) by Ib Thomsen. Simultaneously, the method for estimation of inflation to national numbers was changed, so that reasonable numbers for regions do exist from 1975 and onwards. The change in 1975 led to a different way of interviewing in groups. This caused amongst other things a break with the AKU panel systematics.

In the AKU survey of 1976, a slightly changed questionnaire was introduced. Also, there was a return to the original 6-quarter rotation scheme. The new questionnaire implied a better identification of family workers and persons that are temporarily without paid work. Thus, 30-35 000 more people were defined as employed. The group of "job-seekers without income" were also extended to include persons that were on an involuntary leave of absence. The questions concerning underemployment and “over employment” in the original questionnaire were abandoned.

From the 1st quarter of 1987, the estimation method (inflation to national numbers) was slightly changed. There was also a minor adjustment in the definition of employment. In order to ensure that the numbers were to be comparable to earlier surveys, new versions of the 1980-1986 AKU-files were drawn up. Consequently two versions of the 1980-1987 files - respectively with the old and new methods of estimation - exist. The “old” means that the data are comparable to the original numbers published in the period of 1972 - 1987, whilst the “new” implies that the data are comparable to numbers published after 1987.

Between the 1st and 2nd quarter of 1988, the AKU file description was changed. The variable “Labour-market status” was given a different coding. In addition, adjustments in the data collections were made - from interviewing a specific week every quarter to carry out continuous weekly interviews. SSB also started up an escalation scheme to increase the sample size. This affected the weights, and from the 2nd quarter of 1988, these were recalculated monthly. To balance out the quarterly or yearly files to total national numbers, the monthly weights therefore had to be divided in three or twelve to give the correct total number.

In 1996, AKU was significantly revised: The questionnaire, the file description and the standard for coding of industry and occupation. The data collection also changed to CATI - Computer Assisted Telephone Interviewing. A new classification of industry was put into use (NOS C 182, based on the EU standard NACE, Rev.1). This standard was updated in 2002 and 2007. Also, the new Norwegian...

Household furnishings, consumer habits, evaluation of country image and general attitude to the EEC.

Topics: 1. consumption and furnishing questions: assessment of personal as well as national economic situation in the last 5 years; relative assessment of the standard of living in one´s country, compared with the other countries; detailed recording of type, age and repeated acquisition of durable economic goods; country image regarding product, price and fashion; judgement on the quantitative product selection from abroad; residential furnishings; having a yard; type of film used for camera and film use in the last year; flash pictures.

1. questions on car: possession of delivery vehicle and car, organized according to number, brand, model, form of vehicle, displacement and year of manufacture; new purchase or used car; car radio possession and kilometers driven annually; getting gas self-service; personally conducting vehicle maintenance and use of car cleanser as well as car wax; possession of bicycle.

2. detailed recording of drinking habits with softdrinks, beer, wine and schnapps.

3. attitude to the EEC: knowledge about the member countries of the EEC; countries that should join the EEC; countries that have drawn the greatest or the least benefit from the EEC; EEC membership for the benefit of the country and to raise the standard of living; most important political goals of the EEC.

4. socio-cultural attitudes: attitude to law-breakers; social justice; social and ethnic tolerance; general attitude to young people and older people.

5. attitude to advertising: purchase of a watch during the last five years and price paid for it; activities and jobs conducted oneself in the household; attitude to fashion (scale); social prestige of selected occupations; church attendance on Christmas Day; desire for a life 50 years from now; number of rooms with carpeting.

6. leisure time and further education: knowledge of a foreign language; television habits and reading habits with magazines; total reading times and whereabouts of the magazines; number of books read and bought in the last year; book price; manner of book purchase (mail-order or bookstore); pet possession and manner as well as extent of obtaining feed; participation in further education courses and motives for this; vacation behavior; vacation destinations abroad; package tours; relatives and friends traveling along; trip duration; trip costs; means of transport used; trips by airplane; scheduled or charter flight; frequency of trips to the hairdresser; (among women: use of toiletries and cosmetics); (among men: use of washing and shaving utensils; custom-made or off-the-shelf suit; type of store and price of suit last purchased); use or provisions of nutrition and semi-luxury foods, tobacco and alcohol; use of dish-washing liquids and household cleansers or cleaning products; use of communal washing machines or use of a laundry; age of one´s own washing machine; forms of assets and bank account possession; second home; Readers´ Digest subscriber.

Demography: age; sex; marital status; religious denomination; occupational position; employment; company size; household income; possession of durable economic goods; composition of household; respondent is head of household; characteristics of head of household; housing situation; residential status; degree of urbanization.

Interviewer rating: social class of respondent; weekday of interview.

According to a survey on consumption trend during Spring Festival in China conducted in December 2019, approximately 57 percent of the Chinese respondents preferred well-known brands when making purchases during Chinese New Year. The survey also revealed that respondents born after 1990 were more likely to consider famous online brands in online shopping than older generations.

This statistic shows the expected highest Spring festival spending in Hong Kong in 2020, by type. During the survey period, around 31 percent of respondents in Hong Kong stated that they expected most of their Chinese New Year expenses to be giving away hongbaos.