Graph and download economic data for Employment Level - Part-Time for Economic Reasons, All Industries from May 1955 to Feb 2025 about part-time, 16 years +, household survey, employment, industry, and USA.

Graph and download economic data for Employed Persons in Cape May County, NJ (LAUCN340090000000005) from Jan 1990 to Jan 2025 about Cape May County, NJ; Ocean City; NJ; household survey; employment; persons; and USA.

United States Employment: NF: PW: TW: Used Household & Office Gds Moving data was reported at 83.200 Person th in May 2018. This records an increase from the previous number of 77.800 Person th for Apr 2018. United States Employment: NF: PW: TW: Used Household & Office Gds Moving data is updated monthly, averaging 78.000 Person th from Jan 1990 (Median) to May 2018, with 341 observations. The data reached an all-time high of 99.400 Person th in Jul 2001 and a record low of 63.200 Person th in Feb 1992. United States Employment: NF: PW: TW: Used Household & Office Gds Moving data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G030: Current Employment Statistics Survey: Employment: Production Worker: Non Farm.

Current Employment by Industry (CES) data reflect jobs by "place of work." It does not include the self-employed, unpaid family workers, and private household employees. Jobs located in the county or the metropolitan area that pay wages and salaries are counted although workers may live outside the area. Jobs are counted regardless of the number of hours worked. Individuals who hold more than one job (i.e. multiple job holders) may be counted more than once. The employment figure is an estimate of the number of jobs in the area (regardless of the place of residence of the workers) rather than a count of jobs held by the residents of the area.

United States AHE: sa: PW: RT: Household Appliance Stores data was reported at 20.600 USD in Nov 2021. This records an increase from the previous number of 20.140 USD for Oct 2021. United States AHE: sa: PW: RT: Household Appliance Stores data is updated monthly, averaging 14.340 USD from Jan 1990 (Median) to Nov 2021, with 383 observations. The data reached an all-time high of 22.330 USD in May 2021 and a record low of 8.370 USD in Feb 1990. United States AHE: sa: PW: RT: Household Appliance Stores data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G063: Current Employment Statistics Survey: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

Graph and download economic data for Employment Level - Persons At Work 1-34 Hours, Economic Reasons, All Industries (LNU02032194) from May 1955 to Feb 2025 about hours, 16 years +, household survey, employment, industry, and USA.

United States Employment: NF: Mfg: Household Appliances data was reported at 63.500 Person th in May 2018. This records a decrease from the previous number of 63.800 Person th for Apr 2018. United States Employment: NF: Mfg: Household Appliances data is updated monthly, averaging 108.900 Person th from Jan 1972 (Median) to May 2018, with 557 observations. The data reached an all-time high of 186.800 Person th in Nov 1973 and a record low of 54.400 Person th in Nov 2011. United States Employment: NF: Mfg: Household Appliances data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G024: Current Employment Statistics Survey: Employment: Non Farm.

Graph and download economic data for Employment Level - Part-Time for Economic Reasons, Could Only Find Part-Time Work, Nonagricultural Industries (LNS12032199) from May 1955 to Feb 2025 about nonagriculture, part-time, 16 years +, household survey, employment, industry, and USA.

Abstract copyright UK Data Service and data collection copyright owner.

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

Household datasets
Up to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. From January 2011, a pseudonymised household identifier variable (HSERIALP) is also included in the main quarterly LFS dataset instead.

Change to coding of missing values for household series
From 1996-2013, all missing values in the household datasets were set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. This was also in line with the Annual Population Survey household series of the time. The change was applied to the back series during 2010 to ensure continuity for analytical purposes. From 2013 onwards, the -8 and -9 categories have been reinstated.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each volume alongside the appropriate questionnaire for the year concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance page before commencing analysis.

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

End User Licence and Secure Access QLFS Household datasets
Users should note that there are two discrete versions of the QLFS household datasets. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. Secure Access household datasets for the QLFS are available from 2009 onwards, and include additional, detailed variables not included in the standard EUL versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurrence of learning difficulty or disability; and benefits. For full details of variables included, see data dictionary documentation. The Secure Access version (see SN 7674) has more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of...

This table contains quarterly and yearly figures on labour participation by position in the household in the Netherlands. The population of 15 to 74 years of age (excluding the institutionalized population) is divided into the employed labour force, the unemployed labour force and those not in the labour force. The employed labour force is subdivided on the basis of the professional status and the average working hours. A division by sex, age and whether they are in education is available.

Data available from: 2013

Status of the figures: The figures in this table are final.

Changes as of February 14, 2025: The figures for the fourth quarter and the year 2024 have been added.

Changes as of November 15, 2022: None, this is a new table. This table has been compiled on the basis of the Labor Force Survey (LFS). Due to changes in the research design and the questionnaire of the LFS, the figures for 2021 are not automatically comparable with the figures up to and including 2020. The key figures in this table have therefore been made consistent with the (non-seasonally adjusted) figures in the table Arbeidsdeelname, kerncijfers seizoengecorrigeerd (see section 4), in which the outcomes for the period 2013-2020 have been recalculated to align with the outcomes from 2021. When further detailing the outcomes according to job and personal characteristics, there may nevertheless be differences from 2020 to 2021 as a result of the new method.

When will new figures be released? New figures will be published in May 2025.

Abstract (en): This data collection is comprised of responses from two sets of survey questionnaires, the basic Current Population Survey (CPS) for May 2004 and a supplement survey on the topic of Work Schedules and Working at Home. The CPS, administered monthly, is a labor force survey providing current estimates of the economic status and activities of the population of the United States, for the week prior to the survey. Specifically, the CPS provides estimates of total employment (both farm and nonfarm), nonfarm self-employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment. The May 2004 supplemental survey queried respondents on their working hours and shift of work. Other questions asked about hours spent working at home and equipment used, temporary work done without expecting continuing employment from the employer, worker's expectation of continuing employment, satisfaction with their current employment arrangement, current job history, transition into the current employment arrangement, search for other employment, employee benefits, and earnings. Demographic variables include age, sex, race, Hispanic origin, marital status, veteran status, educational attainment, occupation, and income. The data contain eight weight variables: Household Weight -- HWHHWGT -- Used in tallying household characteristics.; Family Weight -- PWFMWGT -- Used only in tallying family characteristics.; Longitudinal Weight -- PWLGWGT -- Found only on adult records matched from month to month (used for gross flows analysis).; Outgoing Rotation Weight -- PWORWGT -- Used for tallying information collected only in outgoing rotations.; Final Weight -- PWSSWGT -- Used for most tabulations, controlled to independent estimates for (1) States; (2) Origin, Sex, and Age; and (3) Age, Race, and Sex.; Veteran's Weight -- PWVETWGT -- Used for tallying veteran's data only.; Composited Final Weight -- PWCMPWGT -- Used to create BLS's published labor force statistics.; Supplement Weight -- PWSUPWGT -- Used in tallying individuals on the file.; Users are strongly encouraged to refer to the User Guide for additional detailed information on how to use these weights, as well as how they were derived. The universe for the basic CPS consists of all persons aged 15 and older in the civilian noninstitutional population of the United States, living in households. The May 2004 supplement universe represented the full CPS sample comprising all households, for persons age 15 years or older who are currently employed. A multistage probability sample was selected to represent the universe of approximately 56,000 to 57,000 households. 2011-12-21 The ASCII data for this collection have been completely replaced. The data collection has been updated to include SAS, SPSS, and Stata setup files for use with the new data. Also included in the update are a corresponding SAS transport (CPORT) file, SPSS system file, Stata system file, and a tab-delimited version of the new ASCII data. computer-assisted personal interview (CAPI), computer-assisted telephone interview (CATI)Users are strongly encouraged to refer to the User Guide (produced by the Principal Investigators), which contains information about the basic CPS survey and detailed technical documentation specific to the Work Schedules and Work at Home Supplement. In particular, Attachment 8 of the User Guide contains the supplement questionnaire.Detailed and edited universe statements for various variables are defined in either the basic or supplement record layout, which are located in Attachments 6 and 7, respectively, of the User Guide.ICPSR removed all FILLER and PADDING variables from the data. As a result, the column locations in any ICPSR-released data product (e.g., codebook and setup files) will have column locations that are not consistent with locations described in the User Guide.

United States Employment: NF: sa: Mfg: Upholstered Household Furniture data was reported at 60.500 Person th in May 2018. This records a decrease from the previous number of 60.700 Person th for Apr 2018. United States Employment: NF: sa: Mfg: Upholstered Household Furniture data is updated monthly, averaging 87.600 Person th from Jan 1990 (Median) to May 2018, with 341 observations. The data reached an all-time high of 105.000 Person th in Mar 1990 and a record low of 50.700 Person th in Apr 2011. United States Employment: NF: sa: Mfg: Upholstered Household Furniture data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G026: Current Employment Statistics Survey: Employment: Non Farm: sa.

United States Employment: NF: PW: CO: New Single Family General Contractor data was reported at 233.300 Person th in May 2018. This records an increase from the previous number of 230.000 Person th for Apr 2018. United States Employment: NF: PW: CO: New Single Family General Contractor data is updated monthly, averaging 298.200 Person th from Jan 1990 (Median) to May 2018, with 341 observations. The data reached an all-time high of 419.600 Person th in Jun 2006 and a record low of 178.600 Person th in Feb 2011. United States Employment: NF: PW: CO: New Single Family General Contractor data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G030: Current Employment Statistics Survey: Employment: Production Worker: Non Farm.

United States Employment: NF: OS: Household Gds Repair & Maintenance data was reported at 81.200 Person th in May 2018. This records an increase from the previous number of 80.200 Person th for Apr 2018. United States Employment: NF: OS: Household Gds Repair & Maintenance data is updated monthly, averaging 79.100 Person th from Jan 1990 (Median) to May 2018, with 341 observations. The data reached an all-time high of 90.300 Person th in May 1990 and a record low of 64.300 Person th in Jan 2010. United States Employment: NF: OS: Household Gds Repair & Maintenance data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G024: Current Employment Statistics Survey: Employment: Non Farm.

This feature service contains employment and wage data for detailed farming, fishing, and forestry occupations by nation, state, and metropolitan and nonmetropolitan areas. Data from Bureau of Labor Statistics' (BLS) Occupation Employment Statistics (OES) series. Data vintage: May 2018.Boundary files came from U.S. Census Bureau's 2018 Cartographic Boundary Files. Nonmetropolitan areas were constructed based on BLS' May 2018 Area Definitions.A few Frequently Asked Questions from BLS' OES FAQ site:How are "employees" defined by the OES Survey? "Employees" are all part-time and full-time workers who are paid a wage or salary. The survey does not cover the self-employed, owners and partners in unincorporated firms, household workers, or unpaid family workers.Do OES wage estimates include benefits? No. OES wage estimates represent wages and salaries only, and do not include nonproduction bonuses or employer costs of nonwage benefits, such as health insurance or employer contributions to retirement plans. Information on cost of benefits, benefit incidence, and detailed plan provisions is available from the National Compensation Survey program.Why does the sum of the areas within a state not equal the statewide employment? The sum of the areas may differ from statewide employment for several reasons:RoundingThe totals include data items that are not released separately due to confidentiality and quality reasons.Many States include metropolitan areas that cross State lines. These cross-State metropolitan area estimates include data from each State, which should not be included in a total for a single State.A small number of establishments indicate the State in which their employees are located, but do not indicate the specific metropolitan or nonmetropolitan area in which they are located. Data for these establishments are used in the calculation of the statewide estimates, but are not included in the estimates of any individual area.Why don't the major group or "all occupations" employment totals equal the sum of the employment estimates for the detailed occupations? The major group and "all occupations" totals may include detailed occupations for which separate employment estimates could not be published. As a result, employment totals at the major group and "all occupations" levels may be greater than the sum of employment estimates for the detailed occupations. Because the major group employment totals include employment for the detailed occupations in that group, summing across both detailed occupations and major groups will result in double counting of occupational employment.

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

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Cape May County. The dataset can be utilized to gain insights into gender-based income distribution within the Cape May County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Cape May County, among individuals aged 15 years and older with income, there were 37,211 men and 37,688 women in the workforce. Among them, 17,135 men were engaged in full-time, year-round employment, while 12,088 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 7.13% fell within the income range of under $24,999, while 5.64% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 35.44% of men in full-time roles earned incomes exceeding $100,000, while 23.44% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cape May County median household income by race. You can refer the same here

In October 2024, about 161.5 million people were employed in the United States. Employed persons consist of: persons who did any work for pay or profit during the survey reference week; persons who did at least 15 hours of unpaid work in a family-operated enterprise; and persons who were temporarily absent from their regular jobs because of illness, vacation, bad weather, industrial dispute, or various personal reasons.

Background

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

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

Secure Access QLFS household data
Up to 2015, the LFS household datasets were produced twice a year (April-June and October-December) from the corresponding quarter's individual-level data. From January 2015 onwards, they are now produced each quarter alongside the main QLFS. The household datasets include all the usual variables found in the individual-level datasets, with the exception of those relating to income, and are intended to facilitate the analysis of the economic activity patterns of whole households. It is recommended that the existing individual-level LFS datasets continue to be used for any analysis at individual level, and that the LFS household datasets be used for analysis involving household or family-level data. For some quarters, users should note that all missing values in the data are set to one '-10' category instead of the separate '-8' and '-9' categories. For that period, the ONS introduced a new imputation process for the LFS household datasets and it was necessary to code the missing values into one new combined category ('-10'), to avoid over-complication. From the 2013 household datasets, the standard -8 and -9 missing categories have been reinstated.

Secure Access household datasets for the QLFS are available from 2002 onwards, and include additional, detailed variables not included in the standard 'End User Licence' (EUL) versions. Extra variables that typically can be found in the Secure Access versions but not in the EUL versions relate to: geography; date of birth, including day; education and training; household and family characteristics; employment; unemployment and job hunting; accidents at work and work-related health problems; nationality, national identity and country of birth; occurence of learning difficulty or disability; and benefits.

Prospective users of a Secure Access version of the QLFS will need to fulfil additional requirements, commencing with the completion of an extra application form to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access users must also complete face-to-face training and agree to Secure Access' User Agreement (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access version.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of each volume of the User Guide including the appropriate questionnaires for the years concerned. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance pages before commencing analysis.

The study documentation presented in the Documentation section includes the most recent documentation for the LFS only, due to available space. Documentation for previous years is provided alongside the data for access and is also available upon request.

Review of imputation methods for LFS Household data - changes to missing values
A review of the imputation methods used in LFS Household and Family analysis resulted in a change from the January-March 2015 quarter onwards. It was no longer considered appropriate to impute any personal characteristic variables (e.g. religion, ethnicity, country of birth, nationality, national identity, etc.) using the LFS donor imputation method. This method is primarily focused to ensure the 'economic status' of all individuals within a household is known, allowing analysis of the combined economic status of households. This means that from 2015 larger amounts of missing values ('-8'/-9') will be present in the data for these personal characteristic variables than before. Therefore if users need to carry out any time series analysis of households/families which also includes personal characteristic variables covering this time period, then it is advised to filter off 'ioutcome=3' cases from all periods to remove this inconsistent treatment of non-responders.

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

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

Latest Edition Information
For the sixteenth edition (November 2023), one quarterly data file covering the time period April-June, 2023, along with a new Excel variable catalogue for 2023 and a documentation form, have been added to the study.

Employment and unemployment data by city for places in San Mateo County. CDP is "Census Designated Place" - a recognized community that was unincorporated at the time of the 2000 Census.

1) Data may not add due to rounding. All unemployment rates shown are calculated on unrounded data. 2) These data are not seasonally adjusted.

Methodology: Monthly city and CDP labor force data are derived by multiplying current estimates of county employment and unemployment by the employment and unemployment shares (ratios) of each city and CDP at the time of the 2000 Census. Ratios for cities of 25,000 or more persons were developed from special tabulations based on household population only from the Bureau of Labor Statistics. For smaller cities and CDP, ratios were calculated from published census data.

City and CDP unrounded employment and unemployment are summed to get the labor force. The unemployment rate is calculated by dividing unemployment by the labor force. Then the labor force, employment, and unemployment are rounded.

This method assumes that the rates of change in employment and unemployment, since 2000, are exactly the same in each city and CDP as at the county level (i.e., that the shares are still accurate). If this assumption is not true for a specific city or CDP, then the estimates for that area may not represent the current economic conditions. Since this assumption is untested, caution should be employed when using these data.