VITAL SIGNS INDICATOR
Jobs by Industry (EC1)

FULL MEASURE NAME
Employment by place of work by industry sector

LAST UPDATED
December 2022

DESCRIPTION
Jobs by industry refers to both the change in employment levels by industry and the proportional mix of jobs by economic sector. This measure reflects the changing industry trends that affect our region’s workers.

DATA SOURCE
Bureau of Labor Statistics, Quarterly Census of Employment and Wages (QCEW) - https://www.bls.gov/cew/downloadable-data-files.htm
1990-2021

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Quarterly Census of Employment and Wages (QCEW) employment data is reported by the place of work and represent the number of covered workers who worked during, or received pay for, the pay period that included the 12th day of the month. Covered employees in the private-sector and in the state and local government include most corporate officials, all executives, all supervisory personnel, all professionals, all clerical workers, many farmworkers, all wage earners, all piece workers and all part-time workers. Workers on paid sick leave, paid holiday, paid vacation and the like are also covered.

Besides excluding the aforementioned national security agencies, QCEW excludes proprietors, the unincorporated self-employed, unpaid family members, certain farm and domestic workers exempted from having to report employment data and railroad workers covered by the railroad unemployment insurance system. Excluded as well are workers who earned no wages during the entire applicable pay period because of work stoppages, temporary layoffs, illness or unpaid vacations.

The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of California's employment in that same sector. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.

Data is mainly pulled from aggregation level 73, which is county-level summarized at the North American Industry Classification System (NAICS) supersector level (12 sectors). This aggregation level exhibits the least loss due to data suppression, in the magnitude of 1-2 percent for regional employment, and is therefore preferred. However, the supersectors group together NAICS 11 Agriculture, Forestry, Fishing and Hunting; NAICS 21 Mining and NAICS 23 Construction. To provide a separate tally of Agriculture, Forestry, Fishing and Hunting, the aggregation level 74 data was used for NAICS codes 11, 21 and 23.

QCEW reports on employment in Public Administration as NAICS 92. However, many government activities are reported with an industry specific code - such as transportation or utilities even if those may be public governmental entities. In 2021 for the Bay Area, the largest industry groupings under public ownership are Education and health services (58%); Public administration (29%) and Trade, transportation, and utilities (29%). With the exception of Education and health services, all other public activities were coded as government/public administration, regardless of industry group.

For the county data there were some industries that reported 0 jobs or did not report jobs at the desired aggregation/NAICS level for the following counties/years:

Farm:
(aggregation level: 74, NAICS code: 11) - Contra Costa: 2008-2010 - Marin: 1990-2006, 2008-2010, 2014-2020 - Napa: 1990-2004, 2013-2021 - San Francisco: 2019-2020 - San Mateo: 2013

Information:
(aggregation level: 73, NAICS code: 51) - Solano: 2001

Financial Activities:
(aggregation level: 73, NAICS codes: 52, 53) - Solano: 2001

Unclassified:
(aggregation level: 73, NAICS code: 99) - All nine Bay Area counties: 1990-2000 - Marin, Napa, San Mateo, and Solano: 2020 - Napa: 2019 - Solano: 2001

The Quarterly Census of Employment and Wages (QCEW) Program is a Federal-State cooperative program between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) and the California EDD’s Labor Market Information Division (LMID). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by California Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program.

The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit industry codes from the North American Industry Classification System (NAICS) at the national, state, and county levels. At the national level, the QCEW program publishes employment and wage data for nearly every NAICS industry. At the state and local area level, the QCEW program publishes employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. In accordance with the BLS policy, data provided to the Bureau in confidence are used only for specified statistical purposes and are not published. The BLS withholds publication of Unemployment Insurance law-covered employment and wage data for any industry level when necessary to protect the identity of cooperating employers.

Data from the QCEW program serve as an important input to many BLS programs. The Current Employment Statistics and the Occupational Employment Statistics programs use the QCEW data as the benchmark source for employment. The UI administrative records collected under the QCEW program serve as a sampling frame for the BLS establishment surveys.

In addition, the data serve as an input to other federal and state programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses the QCEW data as the base for developing the wage and salary component of personal income.

The U.S. Department of Labor’s Employment and Training Administration (ETA) and California's EDD use the QCEW data to administer the Unemployment Insurance program. The QCEW data accurately reflect the extent of coverage of California’s UI laws and are used to measure UI revenues; national, state and local area employment; and total and UI taxable wage trends.

The U.S. Department of Labor’s Bureau of Labor Statistics publishes new QCEW data in its County Employment and Wages news release on a quarterly basis. The BLS also publishes a subset of its quarterly data through the Create Customized Tables system, and full quarterly industry detail data at all geographic levels.

Dataset is a list of national professional and industry associations and includes information such as name, website URL, and related occupation and industry codes to industries and occupations. Data include the name of the association and a URL link for each association.

CareerOneStop.org web service available upon request.

The Quarterly Census of Employment and Wages (QCEW) program is a cooperative program involving the Bureau of Labor Statistics (BLS) of the United States Department of Labor and the State Employment Security Agencies (SESAs). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by State unemployment insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. Publicly available data files include information on the number of establishments, monthly employment, and quarterly wages, by NAICS industry, by county, by ownership sector, for the entire United States. These data are aggregated to annual levels, to higher industry levels (NAICS industry groups, sectors, and supersectors), and to higher geographic levels (national, State, and Metropolitan Statistical Area (MSA)). To download and analyze QCEW data, users can begin on the QCEW Databases page. Downloadable data are available in formats such as text and CSV. Data for the QCEW program that are classified using the North American Industry Classification System (NAICS) are available from 1990 forward, and on a more limited basis from 1975 to 1989. These data provide employment and wage information for arts-related NAICS industries, such as: Arts, entertainment, and recreation (NAICS Code 71) Performing arts and spectator sports Museums, historical sites, zoos, and parks Amusements, gambling, and recreation Professional, scientific, and technical services (NAICS Code 54) Architectural services Graphic design services Photographic services Retail trade (NAICS Code 44-45) Sporting goods, hobby, book and music stores Book, periodical, and music stores Art dealers For years 1975-2000, data for the QCEW program provide employment and wage information for arts-related industries are based on the Standard Industrial Classification (SIC) system. These arts-related SIC industries include the following: Book stores (SIC 5942) Commercial photography (SIC Code 7335) Commercial art and graphic design (SIC Code 7336) Museums, Botanical, Zoological Gardens (SIC Code 84) Dance studios, schools, and halls (SIC Code 7911) Theatrical producers and services (SIC Code 7922) Sports clubs, managers, & promoters (SIC Code 7941) Motion Picture Services (SIC Code 78) The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit NAICS industry at the national, state, and county levels. At the national level, the QCEW program provides employment and wage data for almost every NAICS industry. At the State and area level, the QCEW program provides employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. Employment data under the QCEW program represent the number of covered workers who worked during, or received pay for, the pay period including the 12th of the month. Excluded are members of the armed forces, the self-employed, proprietors, domestic workers, unpaid family workers, and railroad workers covered by the railroad unemployment insurance system. Wages represent total compensation paid during the calendar quarter, regardless of when services were performed. Included in wages are pay for vacation and other paid leave, bonuses, stock options, tips, the cash value of meals and lodging, and in some States, contributions to deferred compensation plans (such as 401(k) plans). The QCEW program does provide partial information on agricultural industries and employees in private households. Data from the QCEW program serve as an important source for many BLS programs. The QCEW data are used as the benchmark source for employment by the Current Employment Statistics program and the Occupational Employment Statistics program. The UI administrative records collected under the QCEW program serve as a sampling frame for BLS establishment surveys. In addition, data from the QCEW program serve as a source to other Federal and State programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses QCEW data as the base for developing the wage and salary component of personal income. The Employment and Training Administration (ETA) of the Department of Labor and the SESAs use QCEW data to administer the employment security program. The QCEW data accurately reflect the ex

Number of employees by National Occupational Classification (NOC), last 5 months. Data are also available for the standard error of the estimate, the standard error of the month-to-month change and the standard error of the year-over-year change.

This paper uses job seekers’ elicited beliefs about job finding to disentangle the sources of the decline in job-finding rates by duration of unemployment. We document that beliefs have strong predictive power for job finding, but are not revised downward when remaining unemployed and are subject to optimistic bias, especially for the long-term unemployed. Leveraging the predictive power of beliefs, we find substantial heterogeneity in job finding with the resulting dynamic selection explaining most of the observed negative duration dependence in job finding. Moreover, job seekers’ beliefs under-react to heterogeneity in job finding, distorting search behavior and increasing long-term unemployment.

VITAL SIGNS INDICATOR
Jobs by Industry (EC1)

FULL MEASURE NAME
Employment by place of work by industry sector

LAST UPDATED
December 2022

DESCRIPTION
Jobs by industry refers to both the change in employment levels by industry and the proportional mix of jobs by economic sector. This measure reflects the changing industry trends that affect our region’s workers.

DATA SOURCE
Bureau of Labor Statistics, Quarterly Census of Employment and Wages (QCEW) - https://www.bls.gov/cew/downloadable-data-files.htm
1990-2021

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Quarterly Census of Employment and Wages (QCEW) employment data is reported by the place of work and represent the number of covered workers who worked during, or received pay for, the pay period that included the 12th day of the month. Covered employees in the private-sector and in the state and local government include most corporate officials, all executives, all supervisory personnel, all professionals, all clerical workers, many farmworkers, all wage earners, all piece workers and all part-time workers. Workers on paid sick leave, paid holiday, paid vacation and the like are also covered.

Besides excluding the aforementioned national security agencies, QCEW excludes proprietors, the unincorporated self-employed, unpaid family members, certain farm and domestic workers exempted from having to report employment data and railroad workers covered by the railroad unemployment insurance system. Excluded as well are workers who earned no wages during the entire applicable pay period because of work stoppages, temporary layoffs, illness or unpaid vacations.

The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of California's employment in that same sector. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.

Data is mainly pulled from aggregation level 73, which is county-level summarized at the North American Industry Classification System (NAICS) supersector level (12 sectors). This aggregation level exhibits the least loss due to data suppression, in the magnitude of 1-2 percent for regional employment, and is therefore preferred. However, the supersectors group together NAICS 11 Agriculture, Forestry, Fishing and Hunting; NAICS 21 Mining and NAICS 23 Construction. To provide a separate tally of Agriculture, Forestry, Fishing and Hunting, the aggregation level 74 data was used for NAICS codes 11, 21 and 23.

QCEW reports on employment in Public Administration as NAICS 92. However, many government activities are reported with an industry specific code - such as transportation or utilities even if those may be public governmental entities. In 2021 for the Bay Area, the largest industry groupings under public ownership are Education and health services (58%); Public administration (29%) and Trade, transportation, and utilities (29%). With the exception of Education and health services, all other public activities were coded as government/public administration, regardless of industry group.

For the county data there were some industries that reported 0 jobs or did not report jobs at the desired aggregation/NAICS level for the following counties/years:

Farm:
(aggregation level: 74, NAICS code: 11) - Contra Costa: 2008-2010 - Marin: 1990-2006, 2008-2010, 2014-2020 - Napa: 1990-2004, 2013-2021 - San Francisco: 2019-2020 - San Mateo: 2013

Information:
(aggregation level: 73, NAICS code: 51) - Solano: 2001

Financial Activities:
(aggregation level: 73, NAICS codes: 52, 53) - Solano: 2001

Unclassified:
(aggregation level: 73, NAICS code: 99) - All nine Bay Area counties: 1990-2000 - Marin, Napa, San Mateo, and Solano: 2020 - Napa: 2019 - Solano: 2001

To ensure respondent confidentiality, estimates below a certain threshold are suppressed. For Canada, Quebec, Ontario, Alberta and British Columbia suppression is applied to all data below 1,500. The threshold level for Newfoundland and Labrador, Nova Scotia, New Brunswick, Manitoba and Saskatchewan is 500, while in Prince Edward Island, estimates under 200 are suppressed. For census metropolitan areas (CMAs) and economic regions (ERs), use their respective provincial suppression levels mentioned above. Estimates are based on smaller sample sizes the more detailed the table becomes, which could result in lower data quality. Fluctuations in economic time series are caused by seasonal, cyclical and irregular movements. A seasonally adjusted series is one from which seasonal movements have been eliminated. Seasonal movements are defined as those which are caused by regular annual events such as climate, holidays, vacation periods and cycles related to crops, production and retail sales associated with Christmas and Easter. It should be noted that the seasonally adjusted series contain irregular as well as longer-term cyclical fluctuations. The seasonal adjustment program is a complicated computer program which differentiates between these seasonal, cyclical and irregular movements in a series over a number of years and, on the basis of past movements, estimates appropriate seasonal factors for current data. On an annual basis, the historic series of seasonally adjusted data are revised in light of the most recent information on changes in seasonality. Number of civilian, non-institutionalized persons 15 years of age and over who, during the reference week, were employed or unemployed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, worked for pay or profit, or performed unpaid family work or had a job but were not at work due to own illness or disability, personal or family responsibilities, labour dispute, vacation, or other reason. Those persons on layoff and persons without work but who had a job to start at a definite date in the future are not considered employed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, were without work, had looked for work in the past four weeks, and were available for work. Those persons on layoff or who had a new job to start in four weeks or less are considered unemployed. Estimates in thousands, rounded to the nearest hundred. The unemployment rate is the number of unemployed persons expressed as a percentage of the labour force. The unemployment rate for a particular group (age, gender, marital status, etc.) is the number unemployed in that group expressed as a percentage of the labour force for that group. Estimates are percentages, rounded to the nearest tenth. Industry refers to the general nature of the business carried out by the employer for whom the respondent works (main job only). Industry estimates in this table are based on the 2022 North American Industry Classification System (NAICS). Formerly Management of companies and administrative and other support services"." This combines the North American Industry Classification System (NAICS) codes 11 to 91. This combines the North American Industry Classification System (NAICS) codes 11 to 33. This combines the North American Industry Classification System (NAICS) codes 41 to 91. Unemployed persons who have never worked before, and those unemployed persons who last worked more than 1 year ago. For more information on seasonal adjustment see Seasonally adjusted data - Frequently asked questions." Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 1100 - Farming - not elsewhere classified (nec). When the type of farm activity cannot be distinguished between crop and livestock, (for example: mixed farming). Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 2100 - Mining - not elsewhere classified (nec). Whenever the type of mining activity cannot be distinguished. Also referred to as Natural resources. The standard error (SE) of an estimate is an indicator of the variability associated with this estimate, as the estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals and calculate coefficients of variation (CVs). The confidence interval can be built by adding the SE to an estimate in order to determine the upper limit of this interval, and by subtracting the same amount from the estimate to determine the lower limit. The CV can be calculated by dividing the SE by the estimate. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of the standard errors for 12 previous months The standard error (SE) for the month-to-month change is an indicator of the variability associated with the estimate of the change between two consecutive months, because each monthly estimate is based on a sample rather than the entire population. To construct confidence intervals, the SE is added to an estimate in order to determine the upper limit of this interval, and then subtracted from the estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate approximately 68% of the time, and within two standard errors approximately 95% of the time. For example, if the estimated employment level increases by 20,000 from one month to another and the associated SE is 29,000, the true value of the employment change has a 68% chance of falling between -9,000 and +49,000. Because such a confidence interval includes zero, the 20,000 change would not be considered statistically significant. However, if the increase is 30,000, the confidence interval would be +1,000 to +59,000, and the 30,000 increase would be considered statistically significant. (Note that 30,000 is above the SE of 29,000, and that the confidence interval does not include zero.) Similarly, if the estimated employment declines by 30,000, then the true value of the decline would fall between -59,000 and -1,000. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months. They are updated twice a year The standard error (SE) for the year-over-year change is an indicator of the variability associated with the estimate of the change between a given month in a given year and the same month of the previous year, because each month's estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals: it can be added to an estimate in order to determine the upper limit of this interval, and then subtracted from the same estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate, approximately 68% of the time, and within two standard errors, approximately 95% of the time. For example, if the estimated employment level increases by 160,000 over 12 months and the associated SE is 55,000, the true value of the change in employment has approximately a 68% chance of falling between +105,000 and +215,000. This change would be considered statistically significant at the 68% level as the confidence interval excludes zero. However, if the increase is 40,000, the interval would be -15,000 to +95,000, and this increase would not be considered statistically significant since the interval includes zero. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months and are updated twice a year Excluding the territories. Starting in 2006, enhancements to the Labour Force Survey data processing system may have introduced a level shift in some estimates, particularly for less common labour force characteristics. Use caution when comparing estimates before and after 2006. For more information, contact statcan.labour-travail.statcan@statcan.gc.ca

Number of persons in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate, and employment rate, by National Occupational Classification for Statistics (NOC-S) and sex, last 5 years.

This paper documents that the earnings cost of job loss is concentrated among workers who find reemployment in lower-skill occupations, and that the cost and incidence of such occupation dis- placement is higher for workers who lose their job during a recession. I propose a model where hiring is endogenously more selective during recessions, leading some unemployed workers to optimally search for reemployment in lower-skill jobs. The model accounts for existing estimates of the size and cyclicality of the present value cost of job loss, and the cost of entering the labor market during a recession. (JEL E24, E32, J23, J24, J31, J63, J64)

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in employment the week before the census in England and Wales by occupation and by economic activity status. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Occupation (current)

Classifies what people aged 16 years and over do as their main job. Their job title or details of activities they do in their job and any supervisory or management responsibilities form this classification. This information is used to code responses to an occupation using the Standard Occupational Classification (SOC) 2020.

It classifies people who were in employment between 15 March and 21 March 2021, by the SOC code that represents their current occupation.

The lowest level of detail available is the four-digit SOC code which includes all codes in three, two and one digit SOC code levels.

Economic activity status

People aged 16 years and over are economically active if, between 15 March and 21 March 2021, they were:

• in employment (an employee or self-employed)
• unemployed, but looking for work and could start within two weeks
• unemployed, but waiting to start a job that had been offered and accepted

It is a measure of whether or not a person was an active participant in the labour market during this period. Economically inactive are those aged 16 years and over who did not have a job between 15 March to 21 March 2021 and had not looked for work between 22 February to 21 March 2021 or could not start work within two weeks.

The census definition differs from International Labour Organization definition used on the Labour Force Survey, so estimates are not directly comparable.

This classification splits out full-time students from those who are not full-time students when they are employed or unemployed. It is recommended to sum these together to look at all of those in employment or unemployed, or to use the four category labour market classification, if you want to look at all those with a particular labour market status.

Context A data driven look into answering the common question while travelling overseas: "how easy is it to get a job in your country?"

Content This dataset contains youth unemployment rates (% of total labor force ages 15-24) (modeled ILO estimate) Latest data available from 2010 to 2014.

Acknowledgements International Labour Organization.

http://data.worldbank.org/indicator/SL.UEM.TOTL.ZS

Released under Open license.

Abstract

The Urban Employment and Unemployment Survey program was designed to provide statistical data on the size and characteristics of the economically active and the inactive population of the country on continuous basis. The variables collected in the survey: socio-demographic characteristics of household members; economic activity during the last seven days and six months; including characteristics of employed persons such as hours of work, occupation, industry, employment status, and earnings from paid employment; unemployment and characteristics of unemployed persons.

The general objective of the 2015 Urban Employment and Unemployment Survey is to provide statistical data on the characteristics and size of the economic activity status i.e. employed, unemployed population of the country at urban levels on annual basis. The specific objectives of the survey are to: • collect statistical data on the potential manpower and those who are available to take part in various socio-economic activities; • update the data and determine the size and distribution of the labour force participation and the status of economic activity for different sub-groups of the population at different levels of the country; and also to study the socioeconomic and demographic characteristics of these groups; • identify the size, distribution and characteristics of employed population i.e. working in the formal or informal employment sector of the economy and earnings from paid employees and its distribution by occupation and Industry...etc; • provide data on the size, characteristics and distribution of unemployed population and rate of unemployment; • provide data that can be used to assess the situation of women’s employment or the participation of women in the labour force; and • generated time series data to trace changes over time;

Geographic coverage

The survey covered all urban parts of the country except three zones of Afar and six zones of Somali, where the residents are pastoralists.

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2007 Population and Housing Census was used as frame to select 30 households from the sample enumeration areas.

The country was divided into two broad categories. 1) Major urban centers: All regional capitals and five other major urban centers were included in this category. This category had a total of 16 reporting levels. A stratified two-stage cluster sample design was implemented to select the samples. The primary sampling units were EAs, from each EA 30 households were selected as a second stage unit.

2) Other urban centers: In this category, all other urban centers were included. This category had a total of 8 reporting levels. A stratified three stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. From each EA 30 households were selected at the third stage.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire that was used to collect the data had five sections:

Section - 1: Area identification of the selected household: this section dealt with area identification of the respondents such as region, zone, wereda, etc.

Section - 2: Socio- demographic characteristics of households: it consisted of the general socio-demographic characteristics of the population such as age, sex, education, status and type of migration, disability, literacy status, educational Attainment, types of training and marital status.

Section – 3: Economic activities during the last seven days: this section dealt with a range of questions which helps to see the status and characteristics of employed persons in a current status approach such as hours of work in productive activities, occupation, industry, status in employment, earnings from employment, job mobility, service year for paid employees employment in the formal and informal sector and time related under employment.

Section – 4: Unemployment and characteristics of unemployed persons: this section focused on the size, rate and characteristics of the unemployed population.

Section – 5: Economic activities during the last twelve months: this section consists of the usual economic activity status refereeing to the long reference period i.e. engaged in productive activities during most of the last twelve months, reason for not being active, status in employment, main occupation and industry with two digit codes.

Cleaning operations

The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork, field supervisors and statisticians of the head and branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry by the subject matter experts.

Using the computer edit specifications prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing or data cleaning was made by referring back to the filled-in questionnaire. This is an important part of data processing operation to maintain the quality of the data. Consistency checks and rechecks were also made based on frequency and tabulation results. This was done by senior programmers using CSPro software in collaboration with the senior subject matter experts from Manpower Statistics Team of the CSA.

Response rate

Response rate of the survey was 99.8%

Sampling error estimates

Estimation procedures, estimates, and CV's for selected tables are provided in the Annex II and III of the survey final report.

The Quarterly Census of Employment and Wages (QCEW) program (also known as ES-202) collects employment and wage data from employers covered by New York State's Unemployment Insurance (UI) Law. This program is a cooperative program with the U.S. Bureau of Labor Statistics. QCEW data encompass approximately 97 percent of New York's nonfarm employment, providing a virtual census of employees and their wages as well as the most complete universe of employment and wage data, by industry, at the State, regional and county levels. "Covered" employment refers broadly to both private-sector employees as well as state, county, and municipal government employees insured under the New York State Unemployment Insurance (UI) Act. Federal employees are insured under separate laws, but are considered covered for the purposes of the program. Employee categories not covered by UI include some agricultural workers, railroad workers, private household workers, student workers, the self-employed, and unpaid family workers. QCEW data are similar to monthly Current Employment Statistics (CES) data in that they reflect jobs by place of work; therefore, if a person holds two jobs, he or she is counted twice. However, since the QCEW program, by definition, only measures employment covered by unemployment insurance laws, its totals will not be the same as CES employment totals due to the employee categories excluded by UI. Industry level data from 1975 to 2000 is reflective of the Standard Industrial Classification (SIC) codes.

Replication files for Journal of Economic PerspectivesAbstract:We discuss how the relative importance of factors that contribute to movements of the US Beveridge curve has changed from 1959 to 2023. We review these factors in the context of a simple flow analogy used to capture the main insights of search and matching theories of the labor market. Changes in inflow rates, related to demographics, accounted for Beveridge curve shifts between 1959 and 2000. A reduction in matching efficiency, that depressed unemployment outflows, shifted the curve outwards in the wake of the Great Recession. In contrast, the most recent shifts in the Beveridge curve appear driven by changes in the eagerness of workers to switch jobs. Finally, we argue that, while the Beveridge curve is a useful tool for relating unemployment and job openings to inflation, the link between these labor market indicators and inflation depends on whether and why the Beveridge curve shifted. Therefore, a careful examination of the factors underlying movements in the Beveridge curve is essential for drawing policy conclusions from the joint behavior of unemployment and job openings.

The study is the most recent subproject of the German Life History Study. It follows directly on from the two previous life history surveys. In a repeat survey, the 1971-born who had participated in the 1996-1998 study ´Ostdeutsche Lebensverläufe im Transformationsprozess (LV-Ost 71)´ or in the 1998-1999 study ´Ausbildungs- und Berufsverläufen der Geburtskohorten 1964 und 1971 in Westdeutschland (LV-West 64/71)´ were interviewed again. The repeat survey was initiated because the observations of the 1971 birth cohort in these two surveys reached a maximum of 28 years of age. The focus of interest was the question of how young women and men succeed in reconciling career and family/partnership against the background of growing mobility requirements. The questionnaire programme of the repeat survey is based on that of previous life course studies. Since an important focus of the repeat survey is on examining the compatibility of partnership and family, special attention was also paid to the retrospective survey of childcare.

Topics: Residence history: information on all residence episodes, e.g. place of residence in Germany or abroad; federal state; month and year of beginning and end; current place of residence; reasons for moving away. Children, parental leave, desire to have children: children; existing pregnancy; expected date of birth (month, year); desire for (further) children; number of desired (further) children; period up to the next child; reason for childlessness; infant suffers when the mother is gainfully employed; best care alternative when the mother is gainfully employed; ID child; sex of the child; month and year of birth of the child; relationship with child; child deceased; month and year of death of the child; willingness to answer further questions about the deceased child; always lived together with child; beginning and end of the episode lived together (month and year); child in the household; child was desired child; time planning of the child´s birth; parental leave (respondent, other person); start and end of the parental leave (month and year); further children; type of school attended; time regulation of school attendance and attendance at the day care centre; care arrangement and number of hours; amount of the monthly care costs and, if applicable, currency.

School education/further school attendance: Time regulation of school attendance; start and end of school episode (month, year); current school attendance; school in Germany or abroad; federal state or country of school; school leaving certificate or desired school leaving certificate; further school episodes.

Training and further education: type of training or further education; training institution in Germany or abroad; federal state or country of training location; type of training company; sector; time regulation or sequence of training visit or further education visit; start and end of training or further education episode; current training; total number of hours of training or further education; reasons for training; cost unit; type of completion of training; completed training or intended completion of training; further training or further education; vocational code of training.

Gainful and part-time employment: start and end of episode of gainful employment (month and year); current gainful employment; type of job search; reason for taking up the job; occupational status; training required for the activity; training corresponds to the activity; business trip at least once a month; fixed term or ABM; duration of fixed term; reason for fixed term; desire for permanent employment; type of business; business location in Germany or abroad; federal state or country in which the company is located; industry sector; company offers facilities to facilitate the reconciliation of family and work; restructuring within the company; staff reductions; agreed and actual working hours (weekly hours); minimum working hours of 12 weekly hours; reasons for termination of employment relationship; immediately thereafter permanent or temporary position with the same employer; further gainful employment; other secondary occupations; occupational code of occupational activity.

Unemployment: start and end of unemployment episode (month and year); currently unemployed; continuously registered as unemployed; continuously receiving benefits; further unemployment.

Employment history, examination and supplementary modules, professional control convictions: information on type and time (start and end) of gap activities; current gap activity; occupational perspective at last interview time; in each case end of 2000 and currently: level of monthly net earnings (if applicable, currency), type of income (if not monthly net income) and occupational perspective; professional control convictions (scale).

Childcare: start and end of the childcare episode; caregivers; changes in the care situation.

Partnerships, partner acquisition history, family...

Dataset population: Persons aged 16 and over in employment the week before the census

Age

Age is derived from the date of birth question and is a person's age at their last birthday, at 27 March 2011. Dates of birth that imply an age over 115 are treated as invalid and the person's age is imputed. Infants less than one year old are classified as 0 years of age.

Economic activity

Economic activity relates to whether or not a person who was aged 16 and over was working or looking for work in the week before census. Rather than a simple indicator of whether or not someone was currently in employment, it provides a measure of whether or not a person was an active participant in the labour market.

A person's economic activity is derived from their 'activity last week'. This is an indicator of their status or availability for employment - whether employed, unemployed, or their status if not employed and not seeking employment. Additional information included in the economic activity classification is also derived from information about the number of hours a person works and their type of employment - whether employed or self-employed.

The census concept of economic activity is compatible with the standard for economic status defined by the International Labour Organisation (ILO). It is one of a number of definitions used internationally to produce accurate and comparable statistics on employment, unemployment and economic status.

Occupation

A person's occupation relates to their main job and is derived from either their job title or details of the activities involved in their job. This is used to assign responses to an occupation code based on the Standard Occupational Classification 2010 (SOC2010).

Sex

The classification of a person as either male or female.

This service offers Esri's Updated Demographics, Census Data, Tapestry Segmentation, and Business Summary data for the United States. Updates are based on the decennial census, Infogroup business data, other public and proprietary data sources, and proprietary models.

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There are over 2,100 attributes in categories such as: population, households, race and ethnicity, educational attainment, marital status, employment by industry and occupation, income, net worth, housing and home value, number of businesses and employees, sales, and many others. Key attributes from the 2010 Census such as population, are presented for reference. Some attributes such as population, income, and home value, are also projected five years to 2021.

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The Jobs dataset by LaLonde [36] is a widely used benchmark in the causal inference community, where the treatment is job training and the outcomes are income and employment status after training. The dataset includes 8 covariates such as age, education, and previous earnings. Our goal is to predict unemployment, using the feature set of Dehejia and Wahba [37]. Following Shalit et al. [8], we combined the LaLonde experimental sample (297 treated, 425 control) with the PSID comparison group (2490 control).

Dataset population: Persons aged 16 to 74 in employment the week before the census

Economic activity

Economic activity relates to whether or not a person who was aged 16 and over was working or looking for work in the week before census. Rather than a simple indicator of whether or not someone was currently in employment, it provides a measure of whether or not a person was an active participant in the labour market.

A person's economic activity is derived from their 'activity last week'. This is an indicator of their status or availability for employment - whether employed, unemployed, or their status if not employed and not seeking employment. Additional information included in the economic activity classification is also derived from information about the number of hours a person works and their type of employment - whether employed or self-employed.

The census concept of economic activity is compatible with the standard for economic status defined by the International Labour Organisation (ILO). It is one of a number of definitions used internationally to produce accurate and comparable statistics on employment, unemployment and economic status.

Occupation (detailed)

A person's occupation relates to their main job and is derived from either their job title or details of the activities involved in their job. This is used to assign responses to an occupation code based on the Standard Occupational Classification 2010 (SOC2010).