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.

Funded by the Robert Wood Johnson Foundation and The Atlantic Philanthropies, Better Jobs Better Care (BJBC) was a demonstration program that sought to bring about changes in public policy and management practice that would lead to improved recruitment and retention of high-quality paraprofessional direct care workers (DCW) in nursing homes as well as in home- and community-based settings. This was to be accomplished by implementing both policy and management practice goals. Policy goals included developing initiatives related to wages and benefits, incentives for job redesign, curriculum and credentialing, professional associations, and promotion of public awareness and policies. Practice goals involved interventions related to caregiving skill development, peer mentoring, team building, top management training, supervisor training, and provider-specific interventions. The program established demonstration projects in Iowa, North Carolina, Oregon, Pennsylvania, and Vermont which enrolled long-term care establishments across the spectrum of long-term care settings: skilled nursing facilities, assisted living facilities, home care agencies, and adult day service providers. Conducted as part of the BJBC evaluation, this survey interviewed DCWs at two points during the demonstration. The Time 1 interview was fielded as soon as establishments enrolled in the demonstration and provided a list of their DCWs (July 2004 to December 2006), and the Time 2 interviews were completed 12 to 28 months after the Time 1 interviews (April 2006 to June 2007). Both rounds of the survey used the same self-administered questionnaire which included questions about length of employment, job satisfaction, job rewards and problems, supervision, perceptions of quality of care, job confidence, training, intent to quit, and demographic characteristics. The survey also elicited recommendations for improving DCWs' jobs by asking the open-ended question "What is the single most important thing your employer could do to improve your job as a direct care worker?"

The Public Service Employee Survey was designed to solicit the views of Public Service employees on their work environment and overall job satisfaction. Employees expressed their opinions on their work units, their communications with their supervisors, skills and career aspirations, client services and labour management relations. General information such as age, gender, years of service and province of work were collected and questions were asked on specific themes such as staffing fairness, official languages, health and safety, harassment and discrimination and retention issues. The results were aggregated at the and Public Service-wide levels. After applied disclosure control methods, 9 demographic variables remains on the file. The survey ensures a measurement capacity between the 1999, 2002 and 2005 questionnaires.

The Household Market and Nonmarket Activities (HUS) project started as a joint research project between the Industrial Institute for Economic and Social Research (IUI) and Göteborg University in 1980. The ambition was to build a consistent longitudinal micro data base on the use of time, money and public services of households. The first main survey was carried out in 1984. In addition to a contact interview with the selected individuals, all designated individuals participated in a personal interview and two telephone interviews. All respondents were asked about their family background, education, marital status, labor market experience, and employment. In addition, questions about the household were asked of the head of household, concerning family composition, child care, health status, housing, possession of vacation homes, cars, boats and other consumption durables. At the end of the personal interview the household head had to fill out a questionnaire including questions about financing of current home, construction costs for building a house, house value and loans, imputation of property values and loans, additions/renovations 1983, maintenance and repairs, leasing, sale of previous home, assets and liabilities, and non-taxable benefits. All the respondents had to fill out a questionnaire including questions about tax-return information 1983, employment income, and taxes and support payments. Two telephone interviews were used primarily to collect data on the household´s time use and consumption expenditures. The 1986 HUS-survey included both a follow-up of the 1984 sample (panel study) and a supplementary sample. The 1986 sample included 1) all respondents participating in the 1984 survey, 2) the household heads, partners and third persons who should have participated in 1984 but did not (1984 nonresponse), 3) those individuals who started living together after the 1984 interview with an selected individual who participated or was supposed to participate in 1984, 4) members of the 1984 household born in 1966 or 1967. If entering a new household, for example because of leaving their parental home, the household head and his/her partner were also interviewed. Respondents participating in the 1984 survey were interviewed by telephone in 1986. Questions dealt with changes in family composition, housing, employment, wages and child care, and it was not only recorded whether a change had occurred, and what sort of change, but also when it occurred. The respondents also received a questionnaire by mail with questions mainly concerning income and assets. Respondents not participating in the earlier survey were interviewed in person and were asked approximately the same questions as in the 1984 personal interview. The 1988 HUS-survey was considerably smaller than the previous ones. It was addressed exclusively to participants in the 1986 survey, and consisted of a self-enumerated questionnaire with a nonrespondent follow-up by telephone. The questions dealt with changes in housing conditions, employment and household composition. The questionnaire also contained some questions on household income. In many respect the 1991 HUS-survey replicated the 1988 survey. The questions were basically the same in content and range, and the survey was conducted as a self-enamurated questionnaire sent out by mail. This time, however, in contrast to the 1988 survey, an attempt was made to include in the survey the new household members who had moved into sample households since 1986, as well as young people who turned 18 after the 1986 survey. Earlier respondents received a questionnaire by mail containing questions about their home, their primary occupation and weekly work hours since May 1988 (event-history data), earnings in 1989, 1990 and 1991, household composition and any changes in it that might have occurred since 1988, child care and some questions on income. New respondents were also asked about their education and labor-market experience. With respect to its design and question wording, the 1993 survey is a new version of the 1986 survey. The survey is made up of four parts: 1) the panel survey, which was addressed mainly to respondents in the 1991 survey, with certain additions; 2) the so-called supplementary survey, which focused on a new random sample of individuals; 3) the so-called nonresponse survey, which encompassed respondents who had participated in at least one of the earlier surveys but had since dropped out; 4) the time-use survey, which included the same sample of respondents as those in the panel and supplementary surveys. Individuals in the nonresponse group were not included in the time-use survey. Most of the questions in the first three surveys were the same, but certain questions sequences were targeted to the respondents in a specific survey. Thus certain retrospective questions were asked of the nonresponse group, while specific questions on social background, labor market experience etc. were addressed to new respondents. In the case of respondents who had already participated in the panel, a combined contact and main interview was conducted by telephone, after which a self-enumerated questionnaire was sent out to each respondent by mail. The panel sample also included young people in panel households who were born in 1973 or 1974 as well as certain new household members who had not previously been interviewed. These individuals, like new respondents, were not interviewed by telephone until they had been interviewed personally. Thus technically they were treated in the same manner as individuals in the supplementary sample. The new supplementary sample was first contacted by telephone and then given a fairly lengthy personal interview, at the conclusion of which each respondent was asked to fill out a written questionnaire. In this respect the survey design for the nonresponse sample was the same as for the supplementary sample. The nonresponse sample also included young people born in 1973 or 1974 as well as certain new household members. The time-use interviews were conducted by telephone. For each respondent two days were chosen at random from the period from February 15, 1993 to February 14, 1994 and the respondents were interviewed about their time use during those two days. If possible, the time-use interviews were preceded by the other parts of the survey, but this was not always feasible. In each household the household head and spouse/partner were interviewed, as well as an additional person in certain households. Questions regarding the household as a whole were asked of only one person in the household, preferably the household head. As in earlier surveys, data from the interviews was subsequently supplemented by registry data, but only for those respondents who had given their express consent. There is registry information for 75-80 percent of the sample. The telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; and cars and boats. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1992. The 1996 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and environment. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1995. The 1998 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and municipal service. The questionnaire was divided into nine sections: sale of previous home; house value and loans; insurance; home-related expenses; assets; household income; inheritances and gifts; black-market work; and respondent income 1997.

Abstract

Uzbekistan Jobs, Skills, and Migration Survey 2013 is one of three identical household surveys conducted in Central Asia in 2013 by the World Bank in collaboration with German Federal Enterprise for International Cooperation (GIZ). Kyrgyz Republic and Tajikistan were the other countries.

The purpose of the survey was to collect data on employment, migration, cognitive and non-cognitive skills as well as consumption. Conducted from July to September 2013, the survey collected comprehensive information not typically captured by traditional household surveys. It included two distinct instruments: a core questionnaire and a skills questionnaire.

The core questionnaire covered such topics as education, employment, migration, health expenditure, remittances, government transfers, financial services, subjective poverty, housing conditions, and household expenditures. The skills questionnaire contained detailed modules on labor and work expectations, migration and preparation for migration, language skills, and technical skill training. The non-cognitive test modules of the skills questionnaire were based on World Bank Skills Toward Employment and Productivity (STEP) surveys.

The Uzbekistan sample consisted of 1,500 households with 8,622 individuals, representative at the national, regional (Oblast), and urban/rural levels.

Analysis unit

Households and individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample consists of 1,500 households with 8,622 individuals, representative at the Oblast (region) and Urban/Rural level. The sampling strategy is an adaptation of the sampling methodology commonly used in Uzbekistan.

The sample is grouped into PSUs, which are geographical areas of a walkable size. The 75 PSUs sampled (among the 15,000+ in the whole country) are divided according to the population size of each of the 27 urban and rural regions. Then within each urban and rural region, each PSU is randomly selected with a probability proportional to its size. Exceptionally in the sample for Uzbekistan, the sizes of the PSUs have been adjusted ex post for each region to better represent its population importance; PSUs nevertheless contain on average 20 households.

Within each PSU, the households are selected using a geographical sampling procedure. This procedure consists of generating a random point using a numbered grid over a map. From this starting point within the PSU, one out of every 5 households is interviewed, following a systematic route designed for each PSU.

The total number of either refusals or absences noted after 3 attempts amounts to 1,067 households. Each missing and refusal was replaced with another household by extending the geographical sampling procedure within the PSU.

Mode of data collection

Face-to-face [f2f]

Research instrument

Within each household, two sections of the questionnaire - a core questionnaire and a skills questionnaire - were directed at two different categories of individuals within the household. Sometimes, the same person responded to both sections. First, the most knowledgeable person of the household was asked the core part of the questionnaire, which includes questions regarding each household member for their education, health spending and labour and migration. This main part also included a complete household expenditure module, questions about remittances, government transfers, financial services, subjective poverty and questions about the housing conditions.

The second part of the questionnaire was asked to a randomly chosen adult between the age of 15 to 64 who is not currently a migrant, using a random number table (Kish grid) to ensure the randomness of the selection. Provided it was not possible to reach the person selected after 3 attempts, another person was selected using the same random procedure.

The skills part of the questionnaire included detailed modules about labour and work expectations, migration and preparation for migration, language skills, and technical skill training. It also included a self-assessment of technical skills and knowledge, a non-cognitive, and a cognitive test. Unfortunately, 7 language questions of the cognitive skills test are unusable because of translation.

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 Professional Worker Career Experience Survey (PWCES) contains responses from 752 working professionals who were surveyed between December 2003 and September 2004. The survey contains a combination of data on personal education and work histories, family structure, employment and demographic characteristics, and variety of personality scales. The data were collected originally as part of an investigation of the reasons for the under representation of women and minorities in the information technology (IT) workforce. The survey instrument was made up of two separate sets of questions. The first part, developed by the University of Kansas (KU) research team, gathered information on the following topics: work history and job characteristics, education history and experiences, family history and experiences, career choice influences, family and other non-work obligations, attitudes and perceptions of work experiences, life/family/work conflicts, job and career satisfaction, personal attitudes and beliefs, and demographic and salary information. The second part of the survey consisted of the Strong Interest Inventory (SII), a widely used vocational counseling instrument that was developed and is maintained by Consulting Psychologists Press (CPP). After completing the first part of the survey users were transferred to a site maintained by CPP and filled out responses to the SII online. CPP then transferred these responses to the KU team and responses from the two parts were matched based on individual identifiers. After the data collection phase was completed the KU research team cleaned the responses by examining consistency of responses. A number of additional variables were also constructed based on survey responses. Respondents were classified as either IT or non-IT employees based on self-reported current career field (one of 13 categories or "Other"), and specific job title (open ended). Based on this information a total of 749 respondents could be placed in one career field or the other, with 200 being coded as IT and 549 coded as non-IT. Data collected in the first part of the survey allowed the KU research team to construct a number of instruments that have been used by previous researchers. These include measures of: Work-family conflict, job satisfaction, life satisfaction, and work stress. Based on responses to the Strong Interest Inventory it was possible to construct measures of the Big Five Personality Constructs, and Holland's General Occupational Themes. Each of these instruments is described more fully in the glossary included as Appendix A in the user guide. Because not all respondents completed the entire survey sample sizes will depend on the specific questions being analyzed. Demographic variables include education, parent's education, family occupation, occupants in household, spouse/partner occupation, number of children in household, age, race, citizenship, and income.

Open Science in (Higher) Education – data of the February 2017 survey

This data set contains:

• Full raw (anonymised) data set (completed responses) of Open Science in (Higher) Education February 2017 survey. Data are in xlsx and sav format.
• Survey questionnaires with variables and settings (German original and English translation) in pdf. The English questionnaire was not used in the February 2017 survey, but only serves as translation.
• Readme file (txt)

Survey structure

The survey includes 24 questions and its structure can be separated in five major themes: material used in courses (5), OER awareness, usage and development (6), collaborative tools used in courses (2), assessment and participation options (5), demographics (4). The last two questions include an open text questions about general issues on the topics and singular open education experiences, and a request on forwarding the respondent’s e-mail address for further questionings. The online survey was created with Limesurvey[1]. Several questions include filters, i.e. these questions were only shown if a participants did choose a specific answer beforehand ([n/a] in Excel file, [.] In SPSS).

Demographic questions

Demographic questions asked about the current position, the discipline, birth year and gender. The classification of research disciplines was adapted to general disciplines at German higher education institutions. As we wanted to have a broad classification, we summarised several disciplines and came up with the following list, including the option “other” for respondents who do not feel confident with the proposed classification:

• Natural Sciences
• Arts and Humanities or Social Sciences
• Economics
• Law
• Medicine
• Computer Sciences, Engineering, Technics
• Other

The current job position classification was also chosen according to common positions in Germany, including positions with a teaching responsibility at higher education institutions. Here, we also included the option “other” for respondents who do not feel confident with the proposed classification:

• Professor
• Special education teacher
• Academic/scientific assistant or research fellow (research and teaching)
• Academic staff (teaching)
• Student assistant
• Other

We chose to have a free text (numerical) for asking about a respondent’s year of birth because we did not want to pre-classify respondents’ age intervals. It leaves us options to have different analysis on answers and possible correlations to the respondents’ age. Asking about the country was left out as the survey was designed for academics in Germany.

Remark on OER question

Data from earlier surveys revealed that academics suffer confusion about the proper definition of OER^[2]. Some seem to understand OER as free resources, or only refer to open source software (Allen & Seaman, 2016, p. 11). Allen and Seaman (2016) decided to give a broad explanation of OER, avoiding details to not tempt the participant to claim “aware”. Thus, there is a danger of having a bias when giving an explanation. We decided not to give an explanation, but keep this question simple. We assume that either someone knows about OER or not. If they had not heard of the term before, they do not probably use OER (at least not consciously) or create them.

Data collection

The target group of the survey was academics at German institutions of higher education, mainly universities and universities of applied sciences. To reach them we sent the survey to diverse institutional-intern and extern mailing lists and via personal contacts. Included lists were discipline-based lists, lists deriving from higher education and higher education didactic communities as well as lists from open science and OER communities. Additionally, personal e-mails were sent to presidents and contact persons from those communities, and Twitter was used to spread the survey.

The survey was online from Feb 6^th to March 3^rd 2017, e-mails were mainly sent at the beginning and around mid-term.

Data clearance

We got 360 responses, whereof Limesurvey counted 208 completes and 152 incompletes. Two responses were marked as incomplete, but after checking them turned out to be complete, and we added them to the complete responses dataset. Thus, this data set includes 210 complete responses. From those 150 incomplete responses, 58 respondents did not answer 1st question, 40 respondents discontinued after 1st question. Data shows a constant decline in response answers, we did not detect any striking survey question with a high dropout rate. We deleted incomplete responses and they are not in this data set.

Due to data privacy reasons, we deleted seven variables automatically assigned by Limesurvey: submitdate, lastpage, startlanguage, startdate, datestamp, ipaddr, refurl. We also deleted answers to question N^o 24 (email address).

References

Allen, E., & Seaman, J. (2016). Opening the Textbook: Educational Resources in U.S. Higher Education, 2015-16.

First results of the survey are presented in the poster:

Heck, Tamara, Blümel, Ina, Heller, Lambert, Mazarakis, Athanasios, Peters, Isabella, Scherp, Ansgar, & Weisel, Luzian. (2017). Survey: Open Science in Higher Education. Zenodo. http://doi.org/10.5281/zenodo.400561

Contact:

Open Science in (Higher) Education working group, see http://www.leibniz-science20.de/forschung/projekte/laufende-projekte/open-science-in-higher-education/.

[1] https://www.limesurvey.org

[2] The survey question about the awareness of OER gave a broad explanation, avoiding details to not tempt the participant to claim “aware”.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE NATIONAL INSTITUTE OF STATISTICS (INS) - TUNISIA

The survey aims at estimating the demographic and educational characteristics of the population. It also calculates the economic indicators of the population such as the number of active individuals, the additional demand for jobs, the number of employed and their characteristics, the number of jobs created, the characteristics of the unemployed and the unemployment rate. Furthermore, this survey estimates these indicators on the household level and their living conditions.

The results of this survey were compared with the results of the second quarter of the national survey on population and employment 2011. It should also be noted that the National Institute of Statistics -Tunisia uses the unemployment definition and concepts adopted by the International Labour Organization. This definition implies that, the individual did not work during the week preceding the day of the interview, was looking for a job in the month preceding the date of the interview, is available to work within two weeks after the day of the interview.

In 2010, the National Institute of Statistics has adopted a strict ILO definition for unemployment, by conditioning that the person must perform effective approaches to search for a job in the month preceding the day of the interview.

Geographic coverage

Covering a representative sample at the national and regional level (governorates).

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE NATIONAL INSTITUTE OF STATISTICS - TUNISIA (INS)

The sample is drawn from the frame of the 2004 General Census of Population and Housing.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three modules were designed for data collection:

• Household Questionnaire (Module 1): Includes questions regarding household characteristics, living conditions, individuals and their demographic, educational and economic characteristics. This module also provides information on internal and external migration.

• Active Employed Questionnaire (Module 2): Includes questions regarding the characteristics of the employed individuals as occupation, industry and wages for employees.

• Active Unemployed Questionnaire (Module 3): Includes questions regarding the characteristics of the unemployed as unemployment duration, the last occupation, activity, and the number of days worked during the last year...etc.

Cleaning operations

Harmonized Data

• SPSS software is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Agency.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

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 ...

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.

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.

The Household Market and Nonmarket Activities (HUS) project started as a joint research project between the Industrial Institute for Economic and Social Research (IUI) and Göteborg University in 1980. The ambition was to build a consistent longitudinal micro data base on the use of time, money and public services of households. The first main survey was carried out in 1984. In addition to a contact interview with the selected individuals, all designated individuals participated in a personal interview and two telephone interviews. All respondents were asked about their family background, education, marital status, labor market experience, and employment. In addition, questions about the household were asked of the head of household, concerning family composition, child care, health status, housing, possession of vacation homes, cars, boats and other consumption durables. At the end of the personal interview the household head had to fill out a questionnaire including questions about financing of current home, construction costs for building a house, house value and loans, imputation of property values and loans, additions/renovations 1983, maintenance and repairs, leasing, sale of previous home, assets and liabilities, and non-taxable benefits. All the respondents had to fill out a questionnaire including questions about tax-return information 1983, employment income, and taxes and support payments. Two telephone interviews were used primarily to collect data on the household´s time use and consumption expenditures. The 1986 HUS-survey included both a follow-up of the 1984 sample (panel study) and a supplementary sample. The 1986 sample included 1) all respondents participating in the 1984 survey, 2) the household heads, partners and third persons who should have participated in 1984 but did not (1984 nonresponse), 3) those individuals who started living together after the 1984 interview with an selected individual who participated or was supposed to participate in 1984, 4) members of the 1984 household born in 1966 or 1967. If entering a new household, for example because of leaving their parental home, the household head and his/her partner were also interviewed. Respondents participating in the 1984 survey were interviewed by telephone in 1986. Questions dealt with changes in family composition, housing, employment, wages and child care, and it was not only recorded whether a change had occurred, and what sort of change, but also when it occurred. The respondents also received a questionnaire by mail with questions mainly concerning income and assets. Respondents not participating in the earlier survey were interviewed in person and were asked approximately the same questions as in the 1984 personal interview. The 1988 HUS-survey was considerably smaller than the previous ones. It was addressed exclusively to participants in the 1986 survey, and consisted of a self-enumerated questionnaire with a nonrespondent follow-up by telephone. The questions dealt with changes in housing conditions, employment and household composition. The questionnaire also contained some questions on household income. In many respect the 1991 HUS-survey replicated the 1988 survey. The questions were basically the same in content and range, and the survey was conducted as a self-enamurated questionnaire sent out by mail. This time, however, in contrast to the 1988 survey, an attempt was made to include in the survey the new household members who had moved into sample households since 1986, as well as young people who turned 18 after the 1986 survey. Earlier respondents received a questionnaire by mail containing questions about their home, their primary occupation and weekly work hours since May 1988 (event-history data), earnings in 1989, 1990 and 1991, household composition and any changes in it that might have occurred since 1988, child care and some questions on income. New respondents were also asked about their education and labor-market experience. With respect to its design and question wording, the 1993 survey is a new version of the 1986 survey. The survey is made up of four parts: 1) the panel survey, which was addressed mainly to respondents in the 1991 survey, with certain additions; 2) the so-called supplementary survey, which focused on a new random sample of individuals; 3) the so-called nonresponse survey, which encompassed respondents who had participated in at least one of the earlier surveys but had since dropped out; 4) the time-use survey, which included the same sample of respondents as those in the panel and supplementary surveys. Individuals in the nonresponse group were not included in the time-use survey. Most of the questions in the first three surveys were the same, but certain questions sequences were targeted to the respondents in a specific survey. Thus certain retrospective questions were asked of the nonresponse group, while specific questions on social background, labor market experience etc. were addressed to new respondents. In the case of respondents who had already participated in the panel, a combined contact and main interview was conducted by telephone, after which a self-enumerated questionnaire was sent out to each respondent by mail. The panel sample also included young people in panel households who were born in 1973 or 1974 as well as certain new household members who had not previously been interviewed. These individuals, like new respondents, were not interviewed by telephone until they had been interviewed personally. Thus technically they were treated in the same manner as individuals in the supplementary sample. The new supplementary sample was first contacted by telephone and then given a fairly lengthy personal interview, at the conclusion of which each respondent was asked to fill out a written questionnaire. In this respect the survey design for the nonresponse sample was the same as for the supplementary sample. The nonresponse sample also included young people born in 1973 or 1974 as well as certain new household members. The time-use interviews were conducted by telephone. For each respondent two days were chosen at random from the period from February 15, 1993 to February 14, 1994 and the respondents were interviewed about their time use during those two days. If possible, the time-use interviews were preceded by the other parts of the survey, but this was not always feasible. In each household the household head and spouse/partner were interviewed, as well as an additional person in certain households. Questions regarding the household as a whole were asked of only one person in the household, preferably the household head. As in earlier surveys, data from the interviews was subsequently supplemented by registry data, but only for those respondents who had given their express consent. There is registry information for 75-80 percent of the sample. The telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; and cars and boats. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1992. The 1996 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and environment. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1995. The 1998 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and municipal service. The questionnaire was divided into nine sections: sale of previous home; house value and loans; insurance; home-related expenses; assets; household income; inheritances and gifts; black-market work; and respondent income 1997.

Abstract

We study the impact of a light-touch job facilitation intervention that supported young female jobseekers during the application process for factory work in a newly constructed industrial park in Ethiopia. Using data from a panel of 687 jobseekers and randomized access to the support intervention, we find that treated applicants are more likely to be employed and have higher earnings and savings 8 months after baseline, although these impacts are short-lived. Four years later, the effects on employment and income largely dissipated. Our results suggest that young women face significant barriers to engaging in factory work in the short run that a simple job facilitation intervention can help overcome. In the long term, however, these jobs do not offer a better alternative than other income-generating opportunities.

Geographic coverage

The project targeted geographically the outskirts of Addis Ababa, Bole Lemi Industrial Parks. More details under Sampling.

Analysis unit

Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The impact evaluation estimates the impact of supporting and facilitating the job application process for young women seeking a production line position at three factories in the Bole Lemi Industrial Park in Addis Ababa (Ethiopia). These firms were all foreign-owned and produced finished garments for export. They also had large-scale hiring plans for the study duration. Each firm agreed to interview the applicants the research team randomized into the study sample. Given that all firms were only considering female applicants, the study sample comprises only women.

The research team advertised for the factory positions and directed interested applicants to a local sub-district (woreda) administration office for registration. The factory positions were advertised using various methods, including posting advertisements in public places, passing out flyers in high-traffic areas of the city, coordinating with youth associations and utilizing other forms of community mobilization. Unemployed individuals who have registered with their local woreda were also contacted directly by a professional HR consultant.

During the recruitment process, those individuals identified as potential candidates were told to bring their identification and qualification documents to the nearest screening center which was set up in several woreda offices across three sub-cities of Addis Ababa. These screening centers were staffed by trained enumerators every day of the working week from 9am-3pm.

During the scheduled opening hours, enumerators reviewed the documentation of the interested applicants who visited the screening centers and determined their eligibility for the advertised positions. Applicants with incomplete documentation, for example, those who did not have personal identification cards or those who did not meet any of the firms’ eligibility criteria (i.e. applicants fell outside the targeted age range or were unable to provide proof of the required education) were screened out from the study.

Eligible individuals received an invitation to interview with an Industrial Park firm and were provided transportation to the factory for the interview. All applicants who met the eligibility criteria and had proper documentation to prove their eligibility were selected into the sample and asked to stay for the baseline survey. Study participants were then randomized into treatment and control, with two-thirds of applicants in the treatment group and one-third in the control group using a public lottery method. Once randomized, the treatment applicants were assigned a specific firm to interview with. Following the interview, the firms decided whether to make a job offer to the applicants and initiate any hiring procedures for the individuals who they wanted to hire.

Mode of data collection

Face-to-face [f2f]

Research instrument

The baseline, midline and endline survey questionnaires are provided for download in English. The questionnaire comprises the following modules:

Baseline A – Female job seeker Module - Baseline S1 - Identification and Consent S2 – Demographics and Health S3 – Human Capital S4 – Household and Networks S5 – Cash, Savings and Remittances S6 – Women’s Status
S7 – Conscientiousness
S8 – Job Search and Perceptions S9 – Work History S10 – Wealth S11 – Cognitive S12 – Time and Risk S13 – Domestic Violence S14 – Income Risk S15 – Conclusions

Midline B – Female job seeker Module S1 - Identification and Consent S2 – Demographics and Health S11 – Cognitive (Position 1) S3 – Human Capital S4 – Household and Networks S5 – Cash, Savings and Remittances S6 – Women’s Status S8 – Job Search and Perceptions S9 – Work History S10 – Wealth S12 – Time and Risk S13 – Domestic Violence S14 – Income Risk S11 – Cognitive (Position 2) S15 – Conclusions

Endline C – Female job seeker Module S1 - Identification and Consent S2 – Demographics and Health S11 – Cognitive (Position 1) S3 – Human Capital S4 – Household and Networks S5 – Cash, Savings and Remittances S6 – Women’s Status S8 – Job Search and Perceptions S9 – Work History S10 – Wealth S12 – Time and Risk S13 – Domestic Violence S14 – Income Risk S11 – Cognitive (Position 2) S15 – Conclusions

Notes on survey modules:

Sections numbering - Some baseline sections have been removed in midline and endline questionnaires. Thus, baseline and endline section numbering is not continuous. We have chosen to keep them in this order and not to number them so that the prefixes of the variable names (s1, s2, s3, s4, etc) correspond to the sections of the questionnaires.

Cognitive section – The baseline questionnaire includes one cognitive section while midline and endline questionnaires include two. The goal was to assess whether randomizing the position (or timing) of the cognitive skills questions would alter the quality of survey questions. Some people were asked these questions early in the survey and some others later on. The authors did not find significant variations between the two approaches.

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..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 number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, 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, 2016-2020 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 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 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.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 w...

Abstract (en): This data collection supplies standard monthly labor force data for the week prior to the survey. Comprehensive information is given on the employment status, occupation, and industry of persons 14 years old and older. Additional data are available concerning weeks worked and hours per week worked, reason not working full-time, total income and income components, and residence. Besides the CPS core questions, this survey gathered additional data on respondents' premium pay, number of days and hours per week usually worked, whether they worked a shift or flextime schedule, time of day that workers started and ended work, and union membership status. Supplemental questions on multiple job holding were asked of one-fourth of sample households. Questions asked of dual job-holders include the reason for working at a second job, the number of hours worked at this job, and whether they were on layoff from their primary job. Statistics on adult education participation by persons aged 16 years and older are also provided. For each course taken, data are included on subject area, reason for taking the course, amount paid for the course, and source of payment. Information on demographic characteristics, such as age, sex, race, marital status, veteran status, household relationship, educational level, and Hispanic origin, is available for each person in the household enumerated. All persons in the civilian noninstitutionalized population of the United States living in housing units. A national probability sample was used in selecting housing units. Approximately 77,000 households were sampled.

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, 2015-2019 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 2015-2019 American Community Survey (ACS) data generally reflect the September 2018 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:An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***...

This study contains data on the working conditions of 1,455 workers aged 16 and older who were working for pay for 20 or more hours per week in the United States in the period 1973-1977. This survey is a panel study version of the cross-section study, QUALITY OF EMPLOYMENT SURVEY, 1977: CROSS-SECTION (ICPSR 7689). The surveys were undertaken by the investigators to provide an overview of working conditions in the American labor force. The aims of these surveys and many of the questions that were asked were comparable to those of the related collections, SURVEY OF WORKING CONDITIONS, 1969-1970 (ICPSR 3507), and QUALITY OF EMPLOYMENT SURVEY, 1972-1973 (ICPSR 3510). The major measures used in each of the four surveys were the frequency and severity of labor standards problems, the quality of employment indicators that were shown to be predictors of job satisfaction, the job satisfaction indices themselves, and the ratings of important job facets. Respondents were asked questions about many facets of their job situations and other areas of their lives that might be affected by their jobs in order to assess the impact of work on them. Questions included job tension, security, physical health, job satisfaction, and financial well-being. A series of questions regarding job expectations was also asked. Additional questions probed respondents' feelings about their overall contentment with their jobs and with life in general. Other variables probed respondents' feelings about their work culture, physical work environment, discrimination at work, job fringe benefits, and labor unions, as well as child care provisions, nature of time spent with children and spouse, use of leisure time, and electoral participation. Demographic variables provide information on age, sex, marital status, race, place of birth, education, and income.

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, 2015-2019 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..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..Industry titles and their 4-digit codes are based on the North American Industry Classification System (NAICS). The Census industry codes for 2018 and later years are based on the 2017 revision of the NAICS. To allow for the creation of multiyear tables, industry data in the multiyear files (prior to data year 2018) were recoded to the 2017 Census industry codes. We recommend using caution when comparing data coded using 2017 Census industry codes with data coded using Census industry codes prior to data year 2018. For more information on the Census industry code changes, please visit our website at https://www.census.gov/topics/employment/industry-occupation/guidance/code-lists.html..2019 ACS data products include updates to several categories of the existing means of transportation question. For more information, see: Change to Means of Transportation..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 2015-2019 American Community Survey (ACS) data generally reflect the September 2018 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:An "**" entry in the margin of error column indicates that either no sample observations ...