India Employment: Public Sector: Central Government data was reported at 2,520.000 Person th in 2012. This records an increase from the previous number of 2,463.000 Person th for 2011. India Employment: Public Sector: Central Government data is updated yearly, averaging 3,273.000 Person th from Mar 1982 (Median) to 2012, with 31 observations. The data reached an all-time high of 3,428.000 Person th in 1992 and a record low of 2,463.000 Person th in 2011. India Employment: Public Sector: Central Government data remains active status in CEIC and is reported by Central Statistics Office. The data is categorized under Global Database’s India – Table IN.GBA001: Employment.

Overview: Each quarter, the Temporary Foreign Worker Program (TFWP) publishes Labour Market Impact Assessment (LMIA) statistics on Open Government Data Portal, including quarterly and annual LMIA data related to, but not limited to, requested and approved TFW positions, employment location, employment occupations, sectors, TFWP stream and temporary foreign workers by country of origin. The TFWP does not collect data on the number of TFWs who are hired by an employer and have arrived in Canada. The decision to issue a work permit rests with Immigration, Refugees and Citizenship Canada (IRCC) and not all positions on a positive LMIA result in a work permit. For these reasons, data provided in the LMIA statistics cannot be used to calculate the number of TFWs that have entered or will enter Canada. IRCC publishes annual statistics on the number of foreign workers who are issued a work permit: https://open.canada.ca/data/en/dataset/360024f2-17e9-4558-bfc1-3616485d65b9. Please note that all quarterly tables have been updated to NOC 2021 (5 digit and training, education, experience and responsibilities (TEER) based). As such, Table 5, 8, 17, and 24 will no longer be updated but will remain as archived tables. Frequency of Publication: Quarterly LMIA statistics cover data for the four quarters of the previous calendar year and the quarter(s) of the current calendar year. Quarterly data is released within two to three months of the most recent quarter. The release dates for quarterly data are as follows: Q1 (January to March) will be published by early June of the current year; Q2 (April to June) will be published by early September of the current year; Q3 (July to September) will be published by early December of the current year; and Q4 (October to December) will be published by early March of the next year. Annual statistics cover eight consecutive years of LMIA data and are scheduled to be released in March of the next year. Published Data: As part of the quarterly release, the TFWP updates LMIA data for 28 tables broken down by: TFW positions: Tables 1 to 10, 12, 13, and 22 to 24; LMIA applications: Tables 14 to 18; Employers: Tables 11, and 19 to 21; and Seasonal Agricultural Worker Program (SAWP): Tables 25 to 28. In addition, the TFWP publishes 2 lists of employers who were issued a positive or negative LMIA: Employers who were issued a positive LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/90fed587-1364-4f33-a9ee-208181dc0b97/resource/b369ae20-0c7e-4d10-93ca-07c86c91e6fe); and Employers who were issued a negative LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/f82f66f2-a22b-4511-bccf-e1d74db39ae5/resource/94a0dbee-e9d9-4492-ab52-07f0f0fb255b). Things to Remember: 1. When data are presented on positive or negative LMIAs, the decision date is used to allocate which quarter the data falls into. However, when data are presented on when LMIAs are requested, it is based on the date when the LMIA is received by ESDC. 2. As of the publication of 2022Q1- 2023Q4 data (published in April 2024) and going forward, all LMIAs in support of 'Permanent Residence (PR) Only' are included in TFWP statistics, unless indicated otherwise. All quarterly data in this report includes PR Only LMIAs. Dual-intent LMIAs and corresponding positions are included under their respective TFWP stream (e.g., low-wage, high-wage, etc.) This may impact program reporting over time. 3. Attention should be given for data that are presented by ‘Unique Employers’ when it comes to manipulating the data within that specific table. One employer could be counted towards multiple groups if they have multiple positive LMIAs across categories such as program stream, province or territory, or economic region. For example, an employer could request TFWs for two different business locations, and this employer would be counted in the statistics of both economic regions. As such, the sum of the rows within these ‘Unique Employer’ tables will not add up to the aggregate total.

This dataset captures whether a minimum wage policy exists in a country and quantifies the monthly minimum wage level over time. The minimum wages recorded in this database apply only to private sector workers, unless it is stipulated in the sources that private sector minimum wages cannot be lower than public sector minimum wages. One exception is self-declared socialist economies where the government/public sector has traditionally been one of the largest employers; in this case, we recorded minimum wage levels for the public sector. In countries where the minimum wage is sector-specific or occupation-specific, we captured the minimum wage level applicable to either the manufacturing sector or unskilled workers. Scope: Longitudinal data is available for every year between 1995 and 2013 for the 121 countries that have been surveyed by either the Demographic and Health Surveys (DHS) or the Multiple Indicator Cluster Surveys (MICS) at least once between those dates.

Quellen: Amtliche Statistik der jeweiligen Länder (insbesondere publizierte Daten der Volks- und Berufzählungen). Ferner wurden länderspezifische Daten aus wissenschaftlichen Einzelpublikationen verwendet “Figures for general government personnel are generally available for the period 1880-1970, but those for central governement are more limited for some countries. Lacking official payroll or staff statistics, the occupational enumerations contained in decennial or quinquennial population censuses have been used as the next best source of employment data. Most European governments began relatively sophisticated occupational censuses between 1860 and 1880. ´Government employment´ or at least the traditional ´administrative´ sectors of government has historically been among the five or six major economic sectors in the censuses. However, it is only quite recently, that census results for public employment are reported by level of government. Since central governments began publishing their employment data relatively late, the coverage at this level is more limited“ (Flora, P. u.a., 1983: State, Economy, and Society in Western Europe 1815 – 1975. Volume I: The Growth of Mass Democracies and Welfare States. Frankfurt/M. u.a.: Campus, S. 193).

Context

This dataset contains information about individuals' demographic and employment attributes to predict whether their income exceeds $50,000 per year. It originates from the 1994 U.S. Census database and has been widely used in classification problems, making it an excellent resource for machine learning, data analysis, and statistical modeling.

Content

The dataset includes various features related to personal and work-related attributes. The target variable is whether an individual's income exceeds $50,000 annually.

Key features include:

Age: Age of the individual.

Workclass: Employment type (e.g., private, government, self-employed).

Education: Highest level of education achieved.

Education-Num: Number corresponding to the level of education.

Marital Status: Marital status of the individual.

Occupation: Profession or job role.

Relationship: Family role (e.g., husband, wife, not in family).

Race: Race of the individual.

Sex: Gender of the individual.

Capital Gain: Income from investment sources other than salary.

Capital Loss: Losses from investment sources.

Hours Per Week: Average number of hours worked per week.

Native Country: Country of origin of the individual

Variables

Age: Continuous variable representing the age of the individual.

Workclass: Categorical variable indicating the type of employment (e.g., Private, Self-Employed, Government).

Education: Categorical variable showing the highest level of education achieved (e.g., Bachelors, Masters).

Education-Num: Numerical representation of the education level.

Marital Status: Categorical variable representing marital status (e.g., Married, Never-Married).

Occupation: Categorical variable indicating the job role or occupation

Relationship: Categorical variable describing the family relationship (e.g., Husband, Wife).

Race: Categorical variable showing the race of the individual.

Sex: Categorical variable indicating the gender of the individual.

Capital Gain: Continuous variable representing income from capital gains.

Capital Loss: Continuous variable representing losses from investments.

Hours Per Week: Continuous variable showing the average working hours per week.

Native Country: Categorical variable indicating the country of origin.

Income: Target variable (binary), indicating whether the individual earns more than $50,000 (>50K) or not (<=50K).

Acknowledgements

This dataset was derived from the 1994 U.S. Census database and has been made publicly available for research and educational purposes. It is not affiliated with any specific organization. Users are encouraged to comply with ethical data usage guidelines while working with this dataset.

The study on the future of work was conducted by Kantar Public on behalf of the Press and Information Office of the Federal Government. During the survey period from 13 to 22 June 2023, German-speaking people aged 16 to 67 in Germany, excluding pensioners, were surveyed in online interviews (CAWI) on the following topics: current life and work situation, future expectations, the use of AI and the digitalization of the world of work as well as attitudes towards demographic change and the shortage of skilled workers. The respondents were selected using a quota sample from an online access panel. Future: general life satisfaction; satisfaction with selected aspects of life (working conditions, education, qualifications, health situation, professional remuneration, family situation, financial situation); expectations for the future: rather confident vs. rather worried about the private and professional future; rather confident vs. rather worried about the professional future of younger people or the next generation; rather confident vs. rather worried about the future of Germany; confidence vs. concern regarding the competitiveness of the German economy in various areas (digitalization and automation of the working world, climate protection goals of industry, effects of the Ukraine war on the German economy, access to important raw materials such as rare earths or metals, reliable supply of energy, number of qualified specialists, general price development, development of wages and salaries, development of pensions); probability of various future scenarios for Germany in 2030 (Germany is once again the world export champion, unemployment is at an all-time low - full employment prevails in Germany, the energy transition has already created hundreds of thousands of new jobs in German industry, Germany has emerged the strongest in the EU from the crises of the last 15 years, the price crisis has led to the fact The price crisis has meant that politics and business have successfully set the course for the future, citizens can deal with all official matters digitally from home, German industry is much faster than expected in terms of climate targets and is already almost climate-neutral, Germany is the most popular country of immigration for foreign university graduates, the nursing shortage in Germany has been overcome thanks to the immigration of skilled workers). 2. Importance of work: importance of different areas of life (ranking); work to earn money vs. as a vocation; importance of different work characteristics (e.g. job security, adequate income, development prospects and career opportunities, etc.). 3. Professional situation: satisfaction with various aspects of work (job security, pay/income, development/career opportunities, interesting work, sufficient contact with other people, compatibility of family/private life and work. Work climate/ working atmosphere, further training opportunities, social recognition, meaningful and useful work); job satisfaction; expected development of working conditions in own professional field; recognition for own work from the company/ employer, from colleagues, from other people from the work context, from the personal private environment, from society in general and from politics; unemployed people were asked: currently looking for a new job; assessment of chances of finding a new job; pupils, students and trainees were asked: assessment of future career opportunities; reasons for assessing career opportunities as poor (open). 4. AI: use of artificial intelligence (AI) in the world of work rather as an opportunity or rather as a danger; expected effects of AI on working conditions in their own professional field (improvement, deterioration, no effects); opportunities and dangers of digitization, AI and automation based on comparisons (all in all, digitization leads to a greater burden on the environment, as computers, tablets, smartphones and data centers are major power guzzlers vs. All in all, digitalization protects the environment through less mobility and more efficient management, artificial intelligence and digitalization help to reduce the workload and relieve employees of repetitive and monotonous tasks vs. artificial intelligence and digitalization overburden many employees through further work intensification. Stress and burnouts will increasingly be the result, artificial intelligence and digitalization will primarily lead to job losses vs. artificial intelligence and digitalization will create more new, future-proof jobs than old ones will be lost, our economy will benefit greatly from global networking through speed and efficiency gains vs. our economy is threatened by global networking by becoming more susceptible to cyberattacks and hacker attacks, digitalization will lead to new, more flexible working time models and a better work-life balance vs. digitalization will lead to a blurring of boundaries between work and leisure time and thus, above all, to more self-exploitation by employees). 5. Home office: local focus of own work currently, before the corona pandemic and during the corona pandemic (exclusively/ predominantly in the company or from home, at changing work locations (company, at home, mobile from on the road); Agreement with various statements on the topic of working from home (wherever possible, employers should give their employees the opportunity to work from home, working from home leads to a loss of cohesion in the company, working from home enables a better work-life balance, digital communication makes coordination processes more complicated, home office makes an important contribution to climate protection due to fewer journeys to work, home office leads to a mixture of work and leisure time and thus to a greater workload, home office leads to greater job satisfaction and thus to higher productivity, since many professions cannot be carried out in the home office, it would be fairer if everyone had to work outside the home); attitude towards a general 4-day working week (A four-day week for everyone would increase the shortage of skilled workers vs. a four-day week for everyone would increase motivation and therefore productivity). 6. Demographic change: knowledge of the meaning of the term demographic change; expected impact of demographic change on the future of Germany; opinion on the future in Germany based on alternative future scenarios (in the future, poverty in old age will increase noticeably vs. the future generation of pensioners will be wealthier than ever before, in the future, politics and elections will be increasingly determined by older people vs. the influence of the younger generation on politics will become much more important, our social security systems will continue to ensure intergenerational fairness and equalization in the future vs. the distribution conflicts between the younger and older generations will increase noticeably, future generations will have to work longer due to the shortage of skilled workers vs. people will have to work less in the future due to digitalization and automation and will be able to retire earlier). 7. Shortage of skilled workers: shortage of skilled workers in own company; additional personal burden due to shortage of skilled workers; company is doing enough to counteract the shortage of skilled workers; use of artificial intelligence (AI) in the company could compensate for the shortage of skilled workers; evaluation of various measures taken by the federal government to combat the shortage of skilled workers (improvement of training and further education opportunities, increasing the participation of women in the labor market (e.g. by expanding childcare services, more flexible working hours, offers for older skilled workers to stay in work longer, facilitating the immigration of foreign skilled workers); evaluation of the work of the federal government to combat the shortage of skilled workers; attractiveness (reputation in society) of various professions with a shortage of skilled workers (e.g. social pedagogues/educators); evaluation of the work of the federal government to combat the shortage of skilled workers. B. social pedagogue, nursery school teacher, etc.); job recommendation for younger people; own activity in one of the professions mentioned with a shortage of skilled workers. Demography: sex; age; age in age groups; employment; federal state; region west/east; school education; vocational training; self-placement social class; employment status; occupation differentiated workers, employees, civil servants; industry; household size; number of children under 18 in the household; net household income (grouped); location size; party sympathy; migration background (respondent, one parent or both parents). Additionally coded were: consecutive interview number; school education head group (low, medium, high); weighting factor.

Botswana Employment: Central Government data was reported at 130,424.000 Person in Mar 2024. This records an increase from the previous number of 129,344.000 Person for Sep 2023. Botswana Employment: Central Government data is updated quarterly, averaging 104,062.000 Person from Sep 2007 (Median) to Mar 2024, with 37 observations. The data reached an all-time high of 135,575.000 Person in Mar 2020 and a record low of 90,186.000 Person in Sep 2007. Botswana Employment: Central Government data remains active status in CEIC and is reported by Statistics Botswana. The data is categorized under Global Database’s Botswana – Table BW.G001: Employment.

This dataset provides values for RETIREMENT AGE MEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This table provides a breakdown of government expenditure according to the Classification of the Functions of Government (COFOG), which shows how much governments spend in areas such as health, education, environmental protection, defence and servicing public debt.

The presentation is on a country-by-country basis. Users are recommended to select one country (or area) at a time in the ‘Reference area’ filter. Data is presented for each country in national currency as well as in euros for the European Union and the euro area. Data are also available, for most countries, for the sub-sectors of general government.

Specific categories of expenditure (such as health and education) can be selected using the ‘Expenditure’ filter, while details of the type of expenditure such as compensation of employees (payment of wages and salaries and employers’ social contributions) can be selected using the ‘Transaction’ filter. For government final consumption expenditure a breakdown is also provided, in the ‘Transaction’ filter, between individual and collective consumption expenditure.

Data is for General Government. For most countries, results are also available for the General Government sub-sectors: central, local and state (regional) government and social security.

These sub-sectors can be selected using the ‘Institutional sector’ filter. These indicators were presented in the previous dissemination system in the SNA_TABLE11 dataset.
See ANA Changes for information on changes in methodology: ANA Changes
Explore also the Government Finances and Public Sector Debt webpage: Government Finances and Public Sector Debt webpage
OECD statistics contact: STAT.Contact@oecd.org

This data collection consists of transcripts from 12 focus group discussions on themes related to social equality in Russia. The focus group discussions were conducted by the Institute of Applied Politics in Moscow, directed by Dr Kryshtanovskaya; using a discussion guide written by the Investigators. They were held in 12 cities chosen to represent different regions of the country, with an emphasis on provincial cities: Ufa, Kaliningrad, Ekaterinburg, Tiumen, Saratov, Ulyanovsk, Volgograd, Ivanovo, Irkutsk, Obolensk, Vladivostok and Protvino. The respondents included a mix of ages, genders, blue and white collar workers. The focus groups in Protvino and Ulyanovsk were held only for respondents age 18-29. The focus group discussions dealt with household and national economic change, perceptions of social fairness, and welfare values. Specifically, respondents were asked about the state of the national and local economies, their household economy, how they define rich and poor people and how they position themselves in relation to these categories. They were asked about whether they perceived differences in wealth between individuals, regions and between urban and rural areas as fair, and whether such differences are increasing or decreasing. Finally they were asked about whether the rich should take more responsibility for the welfare of the poor, about their own personal responsibility and that of the state and businesses, as well as about progressive income taxes and the degree to which the state should control the economy. The discussion guide is provided in Russian and English. Basic information about the respondents, including gender, age, and occupation are provided at the top of each focus group transcript. Each participant is identified by their given name only. The transcripts are provided in Russian. The Russian text was transcribed by the Institute of Applied Politics from audio files. A parallel set of focus groups was conducted in China and are available as the collection Social equality forum China: Focus group transcripts (see Related Resources). Taken together, Russia and China account for 41 per cent of the total territory of the BRICs and 63 per cent of their GDP/PPP. On Goldman Sachs projections China will be the world’s largest economy by 2050, and Russia its sixth largest. The project will seek to examine the following propositions: (1) that these two BRIC countries are becoming increasingly unequal; (2) that within them, political power and economic advantage are increasingly closely associated; (3) that their political systems have increasingly been employed to ensure that no effective challenge can be mounted to that combination of government position and economic advantage; (4) that set against a broader comparative perspective, an increasingly unequal society in which government is effectively immune from conventional challenge is likely to become increasingly regressive, or unstable, or both. Evidence will be drawn from official statistics, interviews with policy specialists and government officials, two dozen focus groups, and an analysis of the composition of the management boards of the largest companies in both countries. A final part of the analysis will employ crossnational evidence to test a series of hypotheses relating to the association between inequality and political instability on a more broadly comparative basis. Focus group discussions held in 12 Russian cities with 6 participants each drawn from a range of ages, both genders and different professions. Two focus groups were held for respondents age 18-29 only.

General Services Administration Owned PropertiesThis National Geospatial Data Asset (NGDA) dataset, shared as a General Services Administration (GSA) feature layer, displays federal government owned properties in the United States, Puerto Rico, Northern Mariana Islands, U.S. Virgin Islands, Guam and American Samoa. Per GSA, it is "the nation’s largest public real estate organization, provides workspace for over one million federal workers. These employees, along with government property, are housed in space owned by the federal government and in leased properties including buildings, land, antenna sites, etc. across the country."Federally owned buildings in downtown DCData currency: Current federal service (FC_IOLP_BLDG))NGDAID: 133 (Inventory of Owned and Leased Properties (IOLP))OGC API Features Link: Not AvailableFor more information: Real EstateFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes."For other NGDA Content: Esri Federal Datasets

Abstract copyright UK Data Service and data collection copyright owner.BackgroundThe 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 Annual Population Survey, also held at the UK Data Archive, is derived from the LFS.The LFS was first conducted biennially from 1973-1983, then annually between 1984 and 1991, comprising a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter. From 1992 it moved to a quarterly cycle with a sample size approximately equivalent to that of the previous annual data. Northern Ireland was also included in the survey from December 1994. Further information on the background to the QLFS may be found in the documentation.The UK Data Service also holds a Secure Access version of the QLFS (see below); household datasets; two-quarter and five-quarter longitudinal datasets; LFS datasets compiled for Eurostat; and some additional annual Northern Ireland datasets.LFS DocumentationThe 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 (the latest questionnaire available covers July-September 2022). Volumes are updated periodically, 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.LFS response to COVID-19From April 2020 to May 2022, additional non-calendar quarter LFS microdata were made available to cover the pandemic period. The first additional microdata to be released covered February to April 2020 and the final non-calendar dataset covered March-May 2022. Publication then returned to calendar quarters only. Within the additional non-calendar COVID-19 quarters, pseudonymised variables Casenop and Hserialp may contain a significant number of missing cases (set as -9). These variables may not be available in full for the additional COVID-19 datasets until the next standard calendar quarter is produced. The income weight variable, PIWT, is not available in the non-calendar quarters, although the person weight (PWT) is included. Please consult the documentation for full details.Occupation data for 2021 and 2022 data filesThe 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: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.2024 ReweightingIn February 2024, reweighted person-level data from July-September 2022 onwards were released. Up to July-September 2023, only the person weight was updated (PWT23); the income weight remains at 2022 (PIWT22). The 2023 income weight (PIWT23) was included from the October-December 2023 quarter. Users are encouraged to read the ONS methodological note of 5 February, Impact of reweighting on Labour Force Survey key indicators: 2024, which includes important information on the 2024 reweighting exercise.End User Licence and Secure Access QLFS dataTwo versions of the QLFS are available from UKDS. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes country and Government Office Region geography, 3-digit Standard Occupational Classification (SOC) and 3-digit industry group for main, second and last job (from July-September 2015, 4-digit industry class is available for main job only).The Secure Access version contains more detailed variables relating to:age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent childfamily unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of familynationality and country of originfiner detail geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district, and other categories;health: including main health problem, and current and past health problemseducation and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeshipsindustry: including industry, industry class and industry group for main, second and last job, and industry made redundant fromoccupation: including 5-digit industry subclass and 4-digit SOC for main, second and last job and job made redundant fromsystem variables: including week number when interview took place and number of households at addressother additional detailed variables may also be included.The Secure Access datasets (SNs 6727 and 7674) have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Latest edition informationFor the third edition (April 2024), the variable OMCONT was added to the data. 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 covered mainly problems which affected the respondent's work. From that quarter onwards, the questions cover 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.

This collection provides users with data about R&D expenditure and R&D personnel broken down by the following institutional sectors: business enterprise (BES); government (GOV); higher education (HES); private non-profit (PNP), total of all sectors.

The R&D expenditure is broken down by source of funds; sector of performance; type of costs; type of R&D; fields of research and development (FORD); https://circabc.europa.eu/ui/group/c1b49c83-24a7-4ff2-951c-621ac0a89fd8/library/b4b841e5-d200-41bc-8f23-d0b1e034f689?p=1&n=10&sort=modified_DESC">socio-economic objectives (NABS 2007) and by regions (https://showvoc.op.europa.eu/#/datasets/ESTAT_Nomenclature_of_Territorial_Units_for_Statistics/data">NUTS 2 level). The business enterprise sector is further broken down by economic activity (https://showvoc.op.europa.eu/#/datasets/ESTAT_Statistical_Classification_of_Economic_Activities_in_the_European_Community_Rev._2/data">NACE Rev.2); size class; industry orientation.

R&D personnel data are broken down by professional position; sector of performance; educational attainment level; sex; field of research and development (https://www.oecd.org/innovation/frascati-manual-2015-9789264239012-en.htm">FORD); regions (https://showvoc.op.europa.eu/#/datasets/ESTAT_Nomenclature_of_Territorial_Units_for_Statistics/data">NUTS 2 level); for the business enterprise sector is further broken down in size class and economic activity (NACE Rev.2). Researchers are further broken down by age class and citizenship.

The periodicity of R&D data are every two years, except for the key R&D indicators (R&D expenditure, R&D personnel (in Full Time Equivalent - FTE) and Researchers (in FTE) by sectors of performance) which are transmitted annually by the EU Member States (from 2003 onwards based on a legal obligation). Some other breakdowns of the data may appear on an annual basis based on voluntary data provisions.

The data are collected through sample or census surveys, from administrative registers or through a combination of sources.

R&D data are available for following countries and country groups:

• All EU Member States; Candidate Countries; EFTA Countries; The Organisation for Economic Cooperation and Development (OECD) is data provider for the United States of America, Japan, South Korea and China.
• Country groups: EU Member States, Euro Area States.

R&D data are compiled in accordance to the guidelines laid down in OECD (2015), https://www.oecd.org/publications/frascati-manual-2015-9789264239012-en.htm">Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities and the European business statistics methodological manual for R&D statistics – 2023 edition - Manuals and guidelines - Eurostat

This dataset provides values for GOVERNMENT SPENDING TO GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This paper deals with the problem of corruption, with a focus on both individual and country-specific institutional factors that may affect this problem. We analyse the determinants of the incidence of corruption as well as the tolerance of corruption. We used logit regressions that utilised data derived from Eurobarometer. The results strongly suggest gender, age, and education are important factors. We may say that anti-corruption policy ought to be targeted towards younger, less-educated, self-employed people with no children. On the other hand, a better-educated man in his early 30s seems to be a typical victim of corruption. The same is true for those having problems paying their expenses. Furthermore, contact with public officials appears to be one of the key issues, with Internet-based interactions with the government perhaps serving as the most effective solution to this problem. The rule of law, government effectiveness, and public accountability seem to be other factors that negatively correlate with the level of corruption within a country.

This table provides the number of temporary foreign workers in Canada and in provinces by their country of citizenship.

The International Social Survey Programme (ISSP) is a continuous programme of cross-national collaboration running annual surveys on topics important for the social sciences. The programme started in 1984 with four founding members - Australia, Germany, Great Britain, and the United States – and has now grown to almost 50 member countries from all over the world. As the surveys are designed for replication, they can be used for both, cross-national and cross-time comparisons. Each ISSP module focuses on a specific topic, which is repeated in regular time intervals. Please, consult the documentation for details on how the national ISSP surveys are fielded. The present study focuses on questions about political attitudes and the role of government. The release of the cumulated ISSP ´Role of Government´ modules for the years 1985, 1990, 1996, 2006 and 2016 consists of two separate datasets: ZA4747 Role of Government I-V and ZA4748 Role of Government I-V Add On. ZA4747 contains all the cumulated variables, while the supplementary data file ZA4748 contains all those variables that could not be cumulated for various reasons. A comprehensive overview on the contents, the structure and basic coding rules of both data files are provided in the Variable Reports. Role of government I-V Add On: Country-specific variables (for countries included in the cumulated dataset): education (highest degree earned), party affiliation, party voted for in last general election, size of community, country-specific occupational codes (respondent and spouse), personal income, household income, country of origin or ethnic group, religious affili-ation or denomination; Slovakia 2006: political interest; New Zealand 2016: political interest. Module-specific variables: respondent´s religious affiliation or denomination 2006, employment status 2016 (respondent and spouse), living in steady partnership 2016, spouse: working hours 2016; spouse: supervise other employees 2016; parents´ country if birth 2016; Italy: type of housing 1985; type of community (urban/ rural) 1985, 1990, 1996. Additionally coded: Unique cumulation respondent ID Number; Country/ Sample Prefix ISO 3166 Code – alphanumeric; region (country-specific). Das International Social Survey Programme (ISSP) ist ein länderübergreifendes, fortlaufendes Umfrageprogramm, das jährlich Erhebungen zu Themen durchführt, die für die Sozialwissenschaften wichtig sind. Das Programm begann 1984 mit vier Gründungsmitgliedern - Australien, Deutschland, Großbritannien und den Vereinigten Staaten - und ist inzwischen auf fast 50 Mitgliedsländer aus aller Welt angewachsen. Da die Umfragen auf Replikationen ausgelegt sind, können die Daten sowohl für länder- als auch für zeitübergreifende Vergleiche genutzt werden. Jedes ISSP-Modul konzentriert sich auf ein bestimmtes Thema, das in regelmäßigen Zeitabständen wiederholt wird. Details zur Durchführung der nationalen ISSP-Umfragen entnehmen Sie bitte der Dokumentation. Die vorliegende Studie konzentriert sich auf Fragen zu politischen Einstellungen und der Rolle der Regierung. Die Veröffentlichung der kumulierten ISSP-Module "Role of Government" für die Jahre 1985, 1990, 1996, 2006 und 2016 besteht aus zwei separaten Datensätzen: ZA4747 Role of Government I-V und ZA4748 Role of Government I-V Add On. ZA4747 enthält alle kumulierten Variablen, während der Zusatzdatensatz ZA4748 all jene Variablen enthält, die aus verschiedenen Gründen nicht kumuliert werden konnten. Ein umfassender Überblick über den Inhalt, die Struktur und die grundlegenden Kodierungsregeln beider Datenfiles wird in den Variablenreports gegeben. Rolle des Staates I-V Add On: Länderspezifische Variablen (für die im kumulierten Datensatz enthaltenen Länder): Bildung (höchster erworbener Abschluss), Parteizugehörigkeit, bei den letzten allgemeinen Wahlen gewählte Partei, Größe der Gemeinde, länderspezifische Berufscodes (Befragter und Ehepartner), persönliches Einkommen, Haushaltseinkommen, Herkunftsland oder ethnische Gruppe, Religionszugehörigkeit oder Konfession; Slowakei 2006: politisches Interesse; Neuseeland 2016: politisches Interesse. Modulspezifische Variablen: Religionszugehörigkeit oder Konfession des Befragten 2006, Beschäftigungsstatus 2016 (Befragter und Ehegatte), Leben in fester Partnerschaft 2016, Ehegatte: Arbeitszeit 2016; Ehegatte: Aufsicht über andere Mitarbeiter 2016; Geburtsland der Eltern 2016; Italien: Art der Wohnung 1985; Art der Gemeinde (Stadt/Land) 1985, 1990, 1996. Zusätzlich kodiert: Eindeutige Kumulierungs-ID-Nummer des Befragten; Land/Stichprobenpräfix ISO 3166 Code - alphanumerisch; Region (länderspezifisch).

The International Social Survey Programme (ISSP) is a continuous programme of cross-national collaboration running annual surveys on topics important for the social sciences. The programme started in 1984 with four founding members - Australia, Germany, Great Britain, and the United States – and has now grown to almost 50 member countries from all over the world. As the surveys are designed for replication, they can be used for both, cross-national and cross-time comparisons. Each ISSP module focuses on a specific topic, which is repeated in regular time intervals. Please, consult the documentation for details on how the national ISSP surveys are fielded. The present study focuses on questions about political attitudes and the role of government. Attitude to compliance with law; attitudes to various forms of protest against the government; views regarding freedom of speech for extremists; attitude to justice error; attitudes towards state intervention in the economy; attitude to increased government spending for environmental protection, public health system, the police, education system, defense, pensions, unemployment benefits, culture and arts; attitude to welfare state and responsibility for jobs, price control, health care, decent standard of living, economic growth, reduction of income differences, support for students, housing supply and protection of environment; political interest; rating the government performance in providing health care and living standards as well as dealing with country`s security threats, controlling crime, fighting unemployment and protecting environment; attitude towards surveillance measures of the authorities in case of security challenges; political efficacy; trust in politicians and civil servants; assessment of tax equity with various income groups; trust in people; being treated fairly by public officials; treatment by public officials depends on personal contact; perceived amount of politicians and public officials involved in corruption; how often asked for bribe by public officials; number of persons respondent is in contact with per week. Demography: sex; age; marital status; steady life partner; years of schooling; highest education level; country specific education and degree; current employment status (respondent and partner); hours worked weekly; occupation (ISCO 1988) (respondent and partner); supervising function at work; working for private or public sector or self-employed (respondent and partner); if self-employed: number of employees; trade union membership; earnings of respondent (country specific); family income (country specific); size of household; household composition; party affiliation (left-right); country specific party affiliation; participation in last election; religious denomination; religious main groups; attendance of religious services; self-placement on a top-bottom scale; region (country specific); size of community (country specific); type of community: urban-rural area; country of origin or ethnic group affiliation. Additionally coded: administrative mode of data-collection; weight. Das International Social Survey Programme (ISSP) ist ein länderübergreifendes, fortlaufendes Umfrageprogramm, das jährlich Erhebungen zu Themen durchführt, die für die Sozialwissenschaften wichtig sind. Das Programm begann 1984 mit vier Gründungsmitgliedern - Australien, Deutschland, Großbritannien und den Vereinigten Staaten - und ist inzwischen auf fast 50 Mitgliedsländer aus aller Welt angewachsen. Da die Umfragen auf Replikationen ausgelegt sind, können die Daten sowohl für länder- als auch für zeitübergreifende Vergleiche genutzt werden. Jedes ISSP-Modul konzentriert sich auf ein bestimmtes Thema, das in regelmäßigen Zeitabständen wiederholt wird. Details zur Durchführung der nationalen ISSP-Umfragen entnehmen Sie bitte der Dokumentation. Die vorliegende Studie konzentriert sich auf Fragen zu politischen Einstellungen und der Rolle der Regierung. Einstellung zur Befolgung von Gesetzen (Gesetzestreue); Einstellung zu verschiedenen Protestformen gegen die Regierung; Ansichten bezüglich der Meinungsfreiheit für Extremisten; Einstellung zu einem Justizirrtum; Einstellung zu wirtschaftssteuernden Interventionen der Regierung durch Subventionen oder Änderung von Rahmenbedingungen (Skala); Einstellung zur Erhöhung von Regierungsausgaben für Umweltschutz, Gesundheitswesen, Polizei und Strafverfolgung, Bildungswesen, Verteidigung, Renten, Arbeitslosenunterstützung, Kultur und Kunst; Einstellung zum Wohlfahrtsstaat: Einschätzung der staatlichen Verantwortlichkeit für sozialpolitische Aufgaben (Absicherung von Arbeitsplätzen, Sicherung des Lebensstandards für alte und kranke Menschen sowie Studenten, Preisstabilität, Wirtschaftswachstum durch Hilfen an die Industrie, Wohnungsversorgung, Einkommensangleichung etc.); Issue-Kompetenz der Regierung in den Bereichen Gesundheitswesen, Altersversorgung, Sicherheit, Verbrechensbekämpfung, Bekämpfung der Arbeitslosigkeit und Umweltschutz; Einstellung zu ausgewählten staatlichen Überwachungsmaßnahmen bei Verdacht auf einen Terroranschlag; Politikinteresse; generelle Einstellung zur Politik; Einschätzung der politischen Wirksamkeit (efficacy), der eigenen politischen nformiertheit und Vertrauen zu Politikern und zu Staatsbediensteten (Skala); Einschätzung der Steuergerechtigkeit bezogen auf verschiedene Einkommensgruppen; Vertrauen zu den Mitmenschen; Befragter kann wichtige Entscheidungen zugunsten anderer beeinflussen bzw. Befragter ist jemand, der um Einflussnahme bittet; Einschätzung einer fairen Behandlung durch Beamte; Abhängigkeit der Behandlung durch Beamte von persönlichen Beziehungen; Einschätzung des Anteils korrupter Politiker und Beamter im eigenen Land; Häufigkeit von persönlich erlebter Anfrage von Bestechungsgeldern durch öffentliche Bedienstete; Anzahl der Kontaktpersonen pro Woche. Demographie: Geschlecht; Alter; Familienstand; Zusammenleben mit einem Partner; Anzahl Schuljahre; Schulbildung; Beschäftigungsstatus des Befragten sowie des Partners; Wochenarbeitsstunden; Beruf (ILO/ISCO 1988) des Befragten und des Partners sowie Beschäftigung im öffentlichen Sektor; selbständige Beschäftigung; Anzahl der Beschäftigten; Vorgesetztenstatus; Gewerkschaftsmitgliedschaft; Einkommen; Haushaltseinkommen; Haushaltsgröße; Haushaltszusammensetzung; Parteipräferenz ; Wahlbeteiligung bei der letzten Wahl; Konfession; Kirchgangshäufigkeit; Selbsteinstufung der Schichtzugehörigkeit (Oben-Unten-Skala); Region; Ortsgröße; Urbanisierungsgrad; ethnische oder nationale Zugehörigkeit bzw. Herkunft. Zusätzlich verkodet wurde: Erhebungsmethode; Gewichtungsfaktor.