Occupation describes the kind of work a person does on the job. Occupation data were derived from answers to questions 45 and 46 in the 2015 American Community Survey (ACS). Question 45 asks: “What kind of work was this person doing?” Question 46 asks: “What were this person’s most important activities or duties?”

These questions were asked of all people 15 years old and over who had worked in the past 5 years. For employed people, the data refer to the person’s job during the previous week. For those who worked two or more jobs, the data refer to the job where the person worked the greatest number of hours. For unemployed people and people who are not currently employed but report having a job within the last five years, the data refer to their last job.

These questions describe the work activity and occupational experience of the American labor force. Data are used to formulate policy and programs for employment, career development, and training; to provide information on the occupational skills of the labor force in a given area to analyze career trends; and to measure compliance with antidiscrimination policies. Companies use these data to decide where to locate new plants, stores, or offices.

Occupation is a key variable in socio-economic research, used in a wide variety of studies, but its measurement is a major challenge. The national stocks of job titles are large with 10,000’s of job titles, they are unstructured with vague boundaries between job titles, and the stock has no fixed list but instead many entries and exits over time. Measuring occupations in a multi-country survey is even a larger challenge, because occupations with the same tasks have to be coded similarly across countries. Most surveys use an open-ended survey question to measure occupations. The challenge relates to time-consuming and expensive office-coding. Alternatively, web surveys and CAPI surveys allow using a look-up database with occupational titles. The Surveycodings team and WageIndicator Foundation provide a multilingual database of coded and translated occupational titles that allow for urvey respondents' self-identification of their occupational titles, thereby tackling the challenge for multi-country surveys to classify job titles into ISCO-08 classification of occupations and to do so consistently across countries. The database is gradually extended with more occupational titles and more languages. The current version, as of 20230202, holds 55 languages for at most 4,000 titles, though some languages have only half of the titles translated, among others because the occupations do not exist in the country at stake or because no translations were aavailable. Details about this and related databases as well as related publications can be found at https://www.surveycodings.org/articles/codings/occupation.

This collection provides data on labor force activity for the week prior to the survey. Comprehensive data are available on the employment status, occupation, and industry of persons aged 14 and over. Also shown are personal characteristics such as age, sex, race, marital status, veteran status, household relationship, educational background, and Spanish origin. The collection contains a supplement that includes data on skills and training that workers needed to obtain their current or last job, on-the-job training, skills used on the last job, and workers' perceptions of the adequacy of their skills. This supplement makes it possible to analyze changes in occupation and to assess the relative stability of employment in various industries and occupations. Questions were asked of all persons 15 years of age or older who were living in households and who were members of the experienced labor force, whether they were currently employed or not. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09716.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

We use data from the 2019 Occupational Information Network (O*NET) Work Context module, which reports summary measures of the tasks used in 968 occupations (National Center for O*NET Development 2020). These data are gathered through surveys asking workers how often they perform particular tasks and about the importance of different activities in their jobs. Some of the questions relate to the need for face-to-face interaction with clients, customers, and coworkers, and other questions assess how easily work could be done remotely. We use such questions to build two occupation indices: Face-to-Face (questions on face-to-face discussions and physical proximity) and Remote Work (questions on the use of electronic mail, written letters, and phone conversation). It is important to note that these occupational characteristics in the O*NET are measured prior to the epidemic. This means that they do not capture “work practice innovations” that may have been induced by the epidemic, such as the fact that many teachers and professors transitioned from face-to-face to online instruction during the epidemic.

Analysis of choice of occupation and transfers into training and employment of young people in the East of Germany. Topics: Change of living conditions since the political turning point in the GDR; future expectations (scale); goals in life (scale); right-left classification; Sunday question; residence sought; expectations of occupation work (scale); activities in protecting one's place of work (scale); problems in applying for work (scale); reasons for selection and obtaining training occupation or university/college opening (scale); grades at high school graduation or vocational certificate; readiness to work in the old or new states; willingness to compromise to obtain a job (scale); problems in transfer into current occupation (scale); feelings from threat of unemployment; salience of work in life; valuation of one's own training achievements; training demands; motives for learning in training; valuation of primary activities of training occupation; valuation of future occupation; training disadvantages in the new states; reasons for possibly terminating training and consequences; branch of economy and size of company doing the training; reputation of different occupations; characteristics which determine the reputation of an occupation; desired study facility and subject; aids in transfer into employment; housing situation and migration intents; reasons for change of city; evaluation of significance of various organizations; membership in trade union; expectations of and evaluation of representation of interests by trade union (scale); employment situation of parents; evaluation of family situation; members of household; national identity (scale); school degree and vocational certificate as well as activity of parents. Analyse der Berufswahl und der Übergänge in Ausbildung und Erwerbstätigkeit Jugendlicher im Osten Deutschlands. Themen: Veränderung von Lebensbedingungen seit der politischen Wende in der DDR; Zukunftserwartungen (Skala); Lebensziele (Skala); Rechts-Links-Einstufung; Sonntagsfrage; angestrebter Wohnsitz; Erwartungen an die Berufsarbeit (Skala); Aktivitäten bei der Arbeitsplatzsicherung (Skala); Probleme bei der Bewerbung (Skala); Gründe für die Wahl und das Erhalten des Ausbildungsberufs oder Studienplatzes (Skala); Noten bei Abitur oder Berufsabschluß ; Bereitschaft zur Arbeit in den alten bzw. neuen Bundesländern; Kompromißbereitschaft für den Erhalt eines Arbeitsplatzes (Skala); Probleme beim Übergang zum jetzigen Beruf (Skala); Gefühle bei drohender Arbeitslosigkeit; Stellenwert der Arbeit im Leben; Wertung der eigenen Ausbildungsleistungen; Ausbildungsanforderungen; Motive für Lernen in der Ausbildung; Wertung der Haupttätigkeiten des Ausbildungsberufes; Wertung des zukünftigen Berufes; Ausbildungsnachteile in den neuen Ländern; Gründe für einen möglichen Ausbildungsabbruch und Auswirkungen; Wirtschaftszweig und Größe des Ausbildungsbetriebes; Ansehen unterschiedlicher Berufe; Merkmale, die das Ansehen eines Berufes bestimmen; gewünschte Studieneinrichtung und Studienfach; Hilfen beim Übergang in die Erwerbstätigkeit; Wohnsituation und Migrationsabsichten; Gründe für Ortswechsel; Bewertung der Bedeutung verschiedener Organisationen; Mitgliedschaft in Gewerkschaften; Erwartungen an und Bewertung der Interessenvertretung durch die Gewerkschaft (Skala); Erwerbssituation der Eltern; Bewertung der familiären Situation; Mitglieder des Haushalts; nationale Identität (Skala); Schul- und Berufsabschluß sowie Tätigkeit der Eltern.

The national Survey of Information Technology Occupations, conducted in 2002 on behalf of the Software Human Resource Council (SHRC), is the first to shed light on the IT labour market in both the public and private sectors. IT employers and employees were surveyed separately, but simultaneously. The employer survey consisted of questions on occupation profile, hiring and recruitment, employee retention, and training and development. The employee survey had questions on the occupational history of IT employees, salary, education, training, and skills. The target population consisted of private sector locations with at least six employees, and with at least one employee working in IT, as well as public-sector divisions with at least one IT employee. The NSITO is a three-stage survey. First, a sample of employers in both private and public sectors is selected; this is stage 1. The questions asked in stage 1 are essentially about the IT workforce. Stage 2 involves selecting a maximum of two occupations (out of 25) per employer. The questions asked in this stage deal with hiring, training and retaining employees in the selected occupations. In stage 3, a maximum of 10 employees are sampled for each occupation selected in stage 2. Among the subjects that employees are asked about are training, previous employment and demographic characteristics. For National Survey of Information Technology Occupations data, refer to Statistics Canada.

These three datasets contain data and R code to assign a proxy variable for office worker, based on responses to an open-ended question (OEQ) about occupation in Swedish surveys. The R code and proxy variable can be applied to any dataset with Swedish OEQ about occupation; the R code is also adaptable for OEQ in any language, provided there is a standard classification of occupations in that language.

The R code can be found in the dataset Assigning_office_worker_proxy.R, and the proxy variable in the dataset SSYK12_modified.xlsx.

The dataset Occupation_response.xlsx gives an example of what can be extracted from a Swedish questionnaire with an OEQ about occupation. The dataset can be replaced with optional data as long as it includes two variables named “ID” and “Occupation_swe” (i.e., occupation title given by respondent).

The Employment data from the 2021 Federal Census covers labour force status, employment status, labour force participation rate, industry, and occupation. For questions, please contact socialresearch@calgary.ca. Please visit Data about Calgary's population for more information.

Labour force status refers to whether a person was employed, unemployed or not in the labour force during the reference period. Not in the labour force refers to persons who were neither employed nor unemployed during the reference period. This includes persons who, during the reference period were either unable to work or unavailable for work. It also includes persons who were without work and who had neither actively looked for work in the past four weeks nor had a job to start within four weeks of the reference period.

Employment status refers to the employment status of a person during the period of Sunday, May 2 to Saturday, May 8, 2021. An employed person is one who did any work at all at a job or business, that is, paid work in the context of an employer-employee relationship, or self-employment. This category excludes persons not at work because they were on layoff or between casual jobs, and those who did not then have a job (even if they had a job to start at a future date). While an unemployed person is one who was without paid work or without self-employment work and was available for work. An unemployed person either: had actively looked for paid work in the past four weeks; was on temporary lay-off and expected to return to his or her job; or had definite arrangements to start a new job in four weeks or less.

Labour force participation rate refers to the total labour force in that group, expressed as a percentage of the total population in that group.

Industry refers to the general nature of the business carried out in the establishment where the person worked. The industry data are produced according to the North American Industry Classification System (NAICS).

Occupation refers to the kind of work performed in a job, a job being all the tasks carried out by a particular worker to complete their duties. An occupation is a set of jobs that are sufficiently similar in work performed. The occupation data are produced according to the National Occupational Classification (NOC) 2021.

This is a one-time load of Statistics Canada federal census data from 2021 applied to the Communities, Wards, and City geographical boundaries current as of 2022 (so they will likely not match the current year's boundaries). Update frequency is every 5 years. Data Steward: Business Unit Community Strategies (Demographics and Evaluation). This dataset is for general public and internal City business groups.

Census Key Statistics Table KS12: Occupation groups.

Please note that Armed Forces figures comprise those people who responded "Armed Forces" to the industry question and "Commissioned Officer" or "Other rank" to the occupation question. As some people who were in the Armed Forces may have given other occupations or industries, the figures in this table for 'managers and senior officials' and 'associate professional and technical occupations' are likely to underestimate the true figure.

This is, however, unlikely significantly to affect the figures presented here in the vast majority of areas. ONS will release more detailed analysis in due course to help with the interpretation and use of this information.

Cells in this table have been randomly adjusted to avoid the release of confidential data. All data is © Crown Copyright 2003. Census day was 29 April 2001.

This dataset consists of a curated and anonymized collection of real job application confirmation emails from a Gmail inbox. It includes confirmation emails, rejection notices, and other relevant correspondences. The dataset was originally curated to address the challenge of eliminating manual job application tracking, allowing for automatic tracking directly from the inbox, capturing application confirmations and rejection notifications.

The dataset has been carefully pre-processed, cleaned, and enriched with derived features such as:

1. 📅 Parsed Date and Time
2. 🕒 Week, Month, and Year of Email
3. ⏱️ Days Since Email Received
4. 📩 Email Subject and Body
5. 🏢 Company Name (Parsed from Subject/Body)
6. 📊 Application Status Insights

The dataset was originally curated to build a job application tracking agent that can automatically extract and track application updates—such as confirmations, rejections, interview invites, and assessment notifications—directly from the inbox. The goal was to enable users to easily interact with an AI assistant to analyze and manage their job search process more efficiently.

⚠️ Disclaimer: All personal identifiable information (PII) such as names and email addresses have been fully anonymized or redacted. This dataset is intended strictly for educational and research purposes. All personally identifiable information (PII) has been carefully anonymized. Any personal names found in the dataset have been replaced with the fictional name "Michael Gary Scott" as a placeholder. This character reference is used purely for fun and does not correspond to any real individual. Please ensure any further use of this dataset respects privacy and ethical data handling practices.

In 2021, more than *** of job candidates in Poland heard a recruiter ask them to say a few words about themselves.

Survey about educational and occupational career detailed questions about occupation of father when respondent was born and when respondent was twelve years old / dates respondent started and finished with primary education and other following types of daytime education / highest grade attained / other education, e.g. part time courses, finished with a diploma / employment after finishing education / how did respondent find first job after education / dates of starting and finishing of jobs and time spending of periods within the jobs / characteristics of those jobs / have been married or cohabitated before present marriage or cohabitation / detailed questions about education and occupation of ( previous ) spouse, brothers and sisters Background variables: basic characteristics/ place of birth/ household characteristics/ characteristics of parental family/household/ occupation/employment/ education

Abstract copyright UK Data Service and data collection copyright owner. The purpose of this study was to provide information on the prestige ratings of occupations open to Africans in Southern Rhodesia in 1961. The respondents were the secondary level scholars at eight educational institutions. Main Topics: Attitudinal/Behavioural Questions The respondents were required to rate each of 56 occupations on a five point scale ranging from 'Very high respect' to 'Very low respect'. Open-ended questions asked for occupations which in the opinion of the respondent commanded more or less respect than any on the list. These data were not coded. Additional open-ended questions asked for the occupational aspirations of the respondent and the features lending respect to occupations. These questions were coded. Background Variables Age, gender, educational standard, ethnic group, religious denomination, father's occupation, father's education, mother's education, and years lived in town.

This 1947 NORC study of occupational prestige uses the Alphabetical Index of Occupations and Industries, published by the US Bureau of the Census, 140 Edition. The study was carried out to measure patterns of occupational status inheritance in the United States. Data were collected on three generations of occupational history for men and women in a nationally representative study. Questions include occupational goals and decisions, work history, occupation, industry, perceptions of occupations, educational goals and history, veteran status, father's work history, occupation and industry, home ownership, presidential preferences and vote in 1944, age, sex and race (white or black).

Do negative attitudes toward older adults vary according to the occupation of the older adult? Addressing this question is crucial to foster continued employment opportunities for older individuals. To explore this, we conducted an online experiment with Japanese participants, examining how negative attitudes fluctuate when comparing non-older and older adults within specific occupations. This study applied the stereotype content model across 16 occupations and unveiled a three-cluster solution, indicating variations in stereotype mappings between non-older and older workers. Moreover, it was observed that the propensity for these differences varied across clusters. Notably, in occupations where the workers were perceived as more competent and warmer than the general older adults, stereotypes shifted more positively when participants were informed that the workers were older. Conversely, in occupations where workers were perceived as more competent and less warm, both competence and warmth shifted in a negative direction with the introduction of age information. In addition, respect—the degree to which the target person is esteemed and/or admired by others—was strongly associated with both competence and warmth. It is anticipated that the insights gleaned from this study can inform practical interventions aimed at mitigating negative attitudes toward older adults concerning employment.

The study surveyed the characteristics of Finnish employees' work and work-related use of information technology. Several questions dealt with the respondents' occupation, occupational status, size of workplace, working time and line of work. The characteristics of work were examined by asking whether the respondents' work demanded presenting ideas, whether they could influence their working pace, did the job involve management or producing information and could they influence the decisions at workplace on the basis of their status. Questions covering occupational health and safety looked into physical and mental stressfulness of work, changes at workplace during the last few years and recent emotions experienced by the respondents. Respondents were also asked whether they were on a tight schedule at work, whether they found it difficult to forget work at leisure time and would they be able to cope with their present job till retirement. Regarding the use of information technology, respondents were asked what kind of telecommunications or computer technology equipment they used at work, did they use e-mail or Internet and which activities the use of computers was connected to. There were also questions about how they had acquired their computer skills, the extent of computer training on the job, their assessment of the equipment provided and whether they worked at home (home-based work) with the help of computers. Several questions examined the respondents' education and employment history: educational background and field of education, correspondence of work to education, the need for further education. Additional questions surveyed the respondents' working experience in management, security of their job, experiences of temporary dismissals or unemployment and their methods in seeking work. Also examined was the process by which they got their present job, whether they were interested in self-employment (becoming entrepreneurs), possible stays abroad on account of study or work and which aspects of work were most important to them. Opinions on trade union membership, various social problems, and income disparity were also canvassed. Respondents were also asked whether they believed that there is work for those who want it, how would they react to reorganisation in their workplace, which social class did they belong to, and what was important for doing well in the Finnish society. The survey carried a set of statements relating to poverty and activities of the state. Respondents were asked who should take care of various services. Questions related to family life and leisure included marital status, number of children, spouse's occupation, leisure time activities, income of the respondent, family income and family's economic status. The study also surveyed what the respondents talked about with their friends and how important employment, family and leisure were to them. Final questions examined who supported the family in respondents' parental home, occupation of the mother and father and the occupational status of the family breadwinner.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..Occupation titles and their 4-digit codes are based on the Standard Occupational Classification (SOC). The Census occupation codes for 2018 and later years are based on the 2018 revision of the SOC. To allow for the creation of the multiyear tables, occupation data in the multiyear files (prior to data year 2018) were recoded to the 2018 Census occupation codes. We recommend using caution when comparing data coded using 2018 Census occupation codes with data coded using Census occupation codes prior to data year 2018. For more information on the Census occupation code changes, please visit our website at https://www.census.gov/topics/employment /industry-occupation/guidance/code-lists.html..In 2019, methodological changes were made to the class of worker question. These changes involved modifications to the question wording, the category wording, and the visual format of the categories on the questionnaire. The format for the class of worker categories are now listed under the headings "Private Sector Employee," "Government Employee," and "Self-Employed or Other." Additionally, the category of Active Duty was added as one of the response categories under the "Government Employee" section for the mail questionnaire. For more detailed information about the 2019 changes, see the 2016 American Community Survey Content Test Report for Class of Worker located at http://www.census.gov/library/working-papers/2017/acs/2017_Martinez_01.html..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient ...

In 2021, for more than half of the respondents, the most challenging question during a job interview in Poland was about a former boss's opinion of a candidate.

How are employee’s risk of being temporary employed linked to occupational closure? Stefan Stuth (2017) derived various measures for occupational closure to answer this question. The data set includes these measures for different Dictionaries of occupational titles in Germany for the years 2000, 2004, and 2007 (KldB 1992 with 4 digits, KldB 1992 with 3 digits, KldB 2010 with 5 digits). The closure measures provided by the data are: 1) Credentialism. Stuth calculates a "credential inflation index (CIX)" which relates the number of all newly awarded occupation-specific credentials to the number of employees in the respective occupation. 2) Standardization. This measure indicates whether credentials are standardized on the school/university level, the level of states or on the federal level. 3) Licensure. The indicator measures the licensure of tasks. 4) Title protection. The indicator measures the legal protection of occupational titles. 5) Occupational specificity. The measure indicates whether occupational incumbents performing highly specialized tasks or whether they are generalist and do a wide range of tasks. 6) Task-niches. The measure indicates whether occupational incumbents performing tasks that are only rarely performed by members from other occupations or whether they are doing tasks that are common for most occupations. 7) Occupational associations. This indicator states whether occupations are represented by associations that lobby on the behalf of their members. 8) Occupation-specific trade unions. The measure indicates whether occupational unions like the train-driver association (GDL) have the right to collective bargain on behalf of the occupational incumbents within firms. Some control variables are also included.

This graph shows recent college graduates responses to a survey question about their experience in finding a career-related job after graduating. The survey was conducted in the United States in 2012. 41 percent of graduates surveyed said that they were yet to find a career-related job.