This dataset provides values for LABOR MARKET CONDITIONS INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Labor Market Engagement Index provides a summary description of the relative intensity of labor market engagement and human capital in a neighborhood. This is based upon the level of employment, labor force participation, and educational attainment in a census tract. Formally, the labor market index is a linear combination of three standardized vectors: unemployment rate, labor-force participation rate, and percent with a bachelor’s degree or higher.

Labor Market Conditions Index in the United States decreased to 1.50 Index Points in June from 3.30 Index Points in May of 2017. This dataset provides the latest reported value for - United States Labor Market Conditions Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The LMCI is derived from a dynamic factor model that extracts the primary common variation from 19 labor market indicators. One essential feature of the authors' factor model is that its inference about labor market conditions places greater weight on indicators whose movements are highly correlated with each other. And, when indicators provide disparate signals, the model's assessment of overall labor market conditions reflects primarily those indicators that are in broad agreement.

LABOR MARKET ENGAGEMENT INDEXSummary

The labor market engagement index provides a summary description of the relative intensity of labor market engagement and human capital in a neighborhood. This is based upon the level of employment, labor force participation, and educational attainment in a census tract (i). Formally, the labor market index is a linear combination of three standardized vectors: unemployment rate (u), labor-force participation rate (l), and percent with a bachelor’s degree or higher (b), using the following formula:

Where means and standard errors are estimated over the national distribution. Also, the value for the standardized unemployment rate is multiplied by -1.

Interpretation

Values are percentile ranked nationally and range from 0 to 100. The higher the score, the higher the labor force participation and human capital in a neighborhood.

Data Source: American Community Survey, 2011-2015Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 9.

To learn more about the Labor Market Engagement Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

The Better Jobs Index is a tool for comparative analysis of labor markets in Latin America. This index evaluates the state of employment in the region through two dimensions: quantity and quality, each comprising two indicators. The quantity dimension measures how many people wish to work (labor force participation) and how many are actually employed (employment rate). The quality dimension assesses how much of the work generated is registered in social security systems (formality) and how many workers earn wages sufficient to lift them above the poverty line (sufficient wages). Through the Better Jobs Index, the Inter-American Development Bank aims to provide countries with a new instrument to more effectively monitor employment conditions, facilitate cross-country comparisons, and promote policies that lead to more favorable employment conditions.

The Labour Force Survey Ad Hoc Eurostat Datasets form the UK component of the European Union Labour Force Survey (EU LFS), and consist of a subset of core variables from the UK Quarterly Labour Force Survey (held at the UK Data Archive under GN 33246), alongside primary and secondary derived variables computed by Eurostat from the core variables supplied. The data comprise seasonal or calendar quarters, depending on the date, and are not directly comparable with the UK QLFS quarters. From 2002-2008 the ad hoc datasets only include data for quarter two of the survey year, as the questions were only asked in this quarter. From 2009, the questions were asked all year. Quarterly EU LFS datasets from 1999 onwards are also available (see under GN 33366), as are annual datasets (see under GN 33399).

Users should note that the LFS Eurostat datasets available from the UK Data Archive comprise UK data only, and no data from other EU countries are included here. Further information about the EU LFS can be found on the Eurostat EU Labour Force Survey webpage.

The UK Labour Force Survey (LFS) is a unique source of articulated 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. The first LFS was conducted in 1973 and continues to be one of the reasons for carrying out the survey. Eurostat co-ordinates information from labour force surveys in the European Union (EU) member states in order to assist the EU in such matters as the allocation of the Social Fund. Between 1984 and 1991 the survey was carried out annually, and moved to a quarterly cycle (the QLFS) from May 1992. Further information may be found in the main LFS documentation (see link below).

LFS Documentation (main LFS)
Besides the EU LFS documentation (see below), documentation is also available to accompany the main UK LFS datasets available from the Archive. This largely consists of the latest version of each document alongside the appropriate questionnaire for the year concerned. However, LFS documentation volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance pages before commencing analysis.

United States Labour Market Conditions Index: Momentum data was reported at -0.199 Index in Apr 2025. This records an increase from the previous number of -0.769 Index for Mar 2025. United States Labour Market Conditions Index: Momentum data is updated monthly, averaging 0.070 Index from Jan 1992 (Median) to Apr 2025, with 400 observations. The data reached an all-time high of 2.634 Index in Jul 2020 and a record low of -10.986 Index in Apr 2020. United States Labour Market Conditions Index: Momentum data remains active status in CEIC and is reported by Federal Reserve Bank of Kansas City. The data is categorized under Global Database’s United States – Table US.G121: Labour Market Conditions Indicators.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (StatsSA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, StatsSA have produced an annual dataset based on the QLFS data, "Labour Market Dynamics in South Africa". The dataset is constructed using data from all all four QLFS datasets in the year. The dataset also includes a number of variables (including income) that are not available in any of the QLFS datasets from 2010.

Geographic coverage

The survey had national coverage.

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population but includes workers' hostels. However, persons living in private dwelling units within institutions are enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on a school compound would, however, be excluded.

Kind of data

Survey data

Sampling procedure

Each year the LMDSA is created by combining the QLFS waves for that year and then including some additional variables. The QLFS master frame for this LMDSA was based on the 2011 population census by Stas SA. The sampling is stratified by province, district, and geographic type (urban, traditional, farm). There are 3324 PSUs drawn each year, using probability proportional to size (PPS) sampling. In the second stage Dwelling Units (DUs) are systematically selected from PSUs. The 3324 PSU are split into four groups for the year, and at each quarter the DUs from the given group are replaced by substitute DUs from the same PSU or the next PSU on the list (in the same group). It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, two quarters and a new household moves in, the new household will be enumerated for two more quarters until the DU is rotated out. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (or unoccupied).

Mode of data collection

Computer Assisted Telephone Interview

Data appraisal

The statistical release notes that missing values were "generally imputed" for item non-response but provides no detail on how Statistics SA did so.

United States US: Employment Index data was reported at 112.136 2010=100 in Jun 2018. This records an increase from the previous number of 110.771 2010=100 for Mar 2018. United States US: Employment Index data is updated quarterly, averaging 72.841 2010=100 from Mar 1948 (Median) to Jun 2018, with 282 observations. The data reached an all-time high of 112.136 2010=100 in Jun 2018 and a record low of 40.537 2010=100 in Mar 1950. United States US: Employment Index data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s USA – Table US.IMF.IFS: Labour Force, Employment and Unemployment: Quarterly.

This dataset provides values for LABOR MARKET CONDITIONS INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Global Competitiveness Index (GCI), developed by the World Economic Forum, measures factors influencing productivity and long-term prosperity. The indicators are grouped into 12 pillars: Institutions, Infrastructure, Macroeconomic Environment, Health and Primary Education, Higher Education and Training, Goods Market Efficiency, Labor Market Efficiency, Financial Market Development, Technological Readiness, Market Size, Business Sophistication, and Innovation. The pillars are further organized into three subindexes: Basic Requirements, Efficiency Enhancers, and Innovation and Sophistication Factors. The weight assigned to each subindex in the overall index calculation depends on a country’s stage of development, determined by GDP per capita and the share of exports represented by raw materials.

Abstract

The Egypt Labor Market Panel Surveys (ELMPSs) of 1998, 2006, and 2012 and Jordan Labor Market Panel Survey (JLMPS) of 2010 have become well-recognized data sources for labor market studies in the Middle East and North Africa (MENA). These two surveys have been used in numerous research endeavors including peer reviewed academic publications, dissertations, and international organization reports. As part of the same series of surveys, the Tunisia Labor Market Panel Survey (TLMPS) of 2014 is the first wave of what will eventually become a longitudinal survey of the Tunisian labor market. Being far richer than any currently available data, the TLMPS 2014 is a much-needed addition in a landscape of otherwise scarce publicly-accessible data on the Tunisian labor market. The TLMPS 2014 was collected in partnership between the Economic Research Forum (ERF) and the Tunisian National Institute of Statistics (INS).

Similarly to its Egyptian and Jordanian counterparts, the TLMPS 2014 is a nationally representative survey that features detailed information on households and individuals, especially in regards to labor market characteristics. As in other countries in the MENA region, Tunisia suffers from high unemployment, particularly for university graduates, youth, and women, and from low female labor force participation.

The survey allows for an in-depth investigation of current employment characteristics as well as analyses of broader labor market dynamics. For instance, analyses have already revealed the particularly long unemployment durations Tunisian youth experience, long even in comparison to other countries in the region.

For more information, see the paper(s) cited in the "Citations" section: (Assaad, Ragui, Samir Ghazouani, Caroline Krafft, and Dominique J. Rolando, 2016).

Geographic coverage

The sample covered urban/rural areas of each of Tunisia's governorates

Analysis unit

1- Households. 2- Individuals.

Universe

The survey covered a national sample of households and all households members.

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample frame included around 5,160 households drawn from a larger sample that is regularly used to conduct the quarterly survey on population and employment in Tunisia. This larger sample contained 18,000 households as of the last quarter of 2012. The drawing of the sample was done in two stages. In the first stage, 258 enumeration areas were randomly drawn according to the principle of probability proportional to size from the list of enumeration areas drawn up in the 2004 Census. This first sampling stage was carried out using 46 strata comprised of the urban/rural components of each of Tunisia's governorates. The final sample was made up of 253 clusters (out of a possible 40,377 nationally). In the second stage, 20 households were supposed to be drawn at random from each cluster. This procedure was, however, not strictly followed in the field.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey incorporates questionnaires to be administered at both the household and individual levels. At the household level, there was a general household questionnaire, as well as a questionnaire specifically about current migration, transfers, and agricultural and non-agricultural enterprises. At the individual level, there was a detailed questionnaire for working age individuals (15+) and an abbreviated version of the questionnaire for those 6-14 years old.

The main household questionnaire and the migration/enterprise questionnaire were designed to be answered by the most knowledgeable individual in the household, usually the head or the spouse of the head. Along with information on the characteristics of the dwelling, access to public services, and ownership of durables, the household questionnaire includes a full household roster with information on basic demographic characteristics, such as age, sex, and relationship to the head of household. The migration/enterprise questionnaire includes information on any family members currently abroad, remittances, and other transfers, such as child support and pensions. Data were gathered on both non-agricultural and agricultural enterprises, including assets used and net revenues.

The ELMPS and JLMPS had a single questionnaire for all individuals regardless of age. However, in Tunisia, a distinct questionnaire for individuals 6-14 was designed in order to more carefully incorporate measures of child labor. As very little child labor was detected even with this special design, in future LMPSs we plan to revert to a single questionnaire with a few additional questions targeted to children 6-14.

The questionnaire includes a variety of modules on labor market experience and outcomes and related issues. On the labor market side, it elicits information on the current labor market status of the individual, detailed job characteristics (for the employed), wage earnings and non-wage benefits (for wage workers) and participation in domestic and subsistence work. Those who work were asked about both primary and secondary jobs (if any). The questionnaire also includes a detailed labor market history starting from the first labor market status after leaving school and moving forward towards the present for those who ever worked. Further, there is a detailed section on return migration for those who ever worked abroad.

The labor market intersects with a number of other important life experiences, such as education, fertility, and marriage, which are also captured in the TLMPS individual questionnaire. For instance, there are modules on family background (parents and siblings), educational experiences, health, and residential mobility. For women, a section is devoted to fertility issues, the status of women in the household, and work-family issues such as child care and maternity leave. Data were also collected from both men and women on marriage and decisions around marriage, such as the incidence of kin marriage and living arrangements at marriage. Finally, there are modules on financial decision-making, with specific questions about savings and borrowing, as well as on the use of information technology.

Response rate

There were several different problems with non-response during the fielding. First, households often refused to respond entirely. Second, in completing the household survey, some individuals were not captured and some households refused or failed to answer the migration/enterprise questionnaire. In this section we discuss the patterns of non-response, which are incorporated into the weights, discussed below:

1. Non-response of the entire household While the initial goal was to collect data from 5,160 households, time pressures reduced the intended sample to 4,986 households. Of the 4,986 households initially selected, interviews were completed with only 4,521, generating an overall household non-response rate of 9.3%. Additionally, because several clusters were found not to have the requisite twenty households at the end of the data collection stage, additional households were added to some clusters to improve the response rate, leading to wide variation in the number of the households per cluster. The minimum number of households interviewed in a cluster was 8 and the maximum was 34. The mean was 19.7, and the median was 20, with the interquartile range going from 17 to 22 households.

After this additional work to add households to the sample, non-response rates at a cluster level ranged from 0% (complete response), which occurred for 29% of clusters, to a maximum of 62.5%. The mean non-response at the cluster level was 10.2%, the median was 6.7%, the 75th percentile was 13.3%, and the 90th percentile was 24.8%. This household non-response is incorporated in the weights at a cluster level, with the households that did respond within a cluster representing those that did not.

1. Non-response to child, adult, and migration/enterprise questionnaires As well as problems with non-response on the household level, there were problems with completing the child, adult, and migration/enterprise questionnaires. We developed weights to account for non-response to each of these questionnaires in their entirety. However, individuals often stopped answering partway through a questionnaire, suffered from incorrect skips, or other data problems, such that data is sometimes missing for a particular question within a questionnaire that contains some data. Additional data imputation techniques, implemented on a question-by-question basis, are required for these problems.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population of South Africa with one exception. The only institutional subpopulation included in the QLFS sample are individuals in worker's hostels. Persons living in private dwelling units within institutions are also enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The QLFS uses a master sampling frame that is used by several household surveys conducted by Statistics South Africa. This wave of the QLFS is based on the 2013 master frame, which was created based on the 2011 census. There are 3324 PSUs in the master frame and roughly 33000 dwelling units.

The sample for the QLFS is based on a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

For each quarter of the QLFS, a quarter of the sampled dwellings are rotated out of the sample. These dwellings are replaced by new dwellings from the same PSU or the next PSU on the list. For more information see the statistical release.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaire consists of the following sections: - Biographical information (marital status, education, etc.) - Economic activities in the last week for persons aged 15 years and older - Unemployment and economic inactivity for persons aged 15 years and above - Main work activity in the last week for persons aged 15 years and above - Earnings in the main job for employees, employers and own-account workers aged 15 years and above

From 2010 the income data collected by South Africa's Quarterly Labour Force Survey is no longer provided in the QLFS dataset (except for a brief return in QLFS 2010 Q3 which may be an error). Possibly because the data is unreliable at the level of the quarter, Statistics South Africa now provides the income data from the QLFS in an annualised dataset called Labour Market Dynamics in South Africa (LMDSA). The datasets for LMDSA are available from DataFirst's website.

This data has been created by the Regional Australia Institute for the [In]Sight competitive index released in 2012. Modelled on the World Economic Forums Global Competitiveness Report [In]Sight was developed in collaboration with Deloitte Access Economics and combines data from sources including the Australian Bureau of Statistics and the Social Health Atlas of Australia. Both employment rates and the levels of labour force participation are key inputs into the creation of an efficient labour market. Generally long-term unemployment indicates the presence of inherent structural problems which may adversely impact competitiveness. Low labour force participation may reflect low education levels in the region a lack of economic opportunities or an atypical age structure (such as a skew towards retirement age persons).

The Labour Force Survey (LFS) has been carried out in the UK since 1973. From 1973 until 1983 the survey was carried out biennially, and from 1984 until 1991 it was conducted annually. In 1992 the quarterly LFS was introduced. For full background and methodological information users should refer to the main Quarterly Labour Force Survey (QLFS) series (held at the UK Data Archive (UKDA) under GN 33246).

The Local Area Data series was produced quarterly alongside the main QLFS from 1992-2006, and included aggregated data on employment, economic activity and related subjects, covering Local Authority Districts (LADs), Training and Enterprise Councils (TECs), and their Scottish and Welsh equivalents. From 1992 until August 1997, the data covered Great Britain, and from September 1997 data from Northern Ireland were added. The Local Area Data were also available as an annual database between 1994-1995 and 1999-2000, for LADs at individual level, but these are no longer produced.

LFS move from seasonal to calendar quarters
In accordance with EU regulations, the LFS moved from seasonal (spring, summer, autumn, winter) quarters to calendar quarters (January-March, April-June, July-September, October-December) in 2006. The last seasonal Local Area Data dataset issued was the Quarterly Labour Force Survey, December 2005 - February, 2006: Local Area Data (SN 5392), and the first calendar quarter dataset was the Quarterly Labour Force Survey, January - March, 2006: Local Area Data (SN 5393). Users should note that there is some overlap between these two datasets. Further information on the seasonal to calendar quarter change and its impact on LFS data may be found in the following online article:
Madouros, V. (2006) Impact of the switch from seasonal to calendar quarters in the Labour Force Survey, London: ONS.

LFS Documentation
The User Guides available with the UK Data Archive's LFS studies are those available at the time of deposit. Users can access the updated guides online via the ONS LFS User Guide pages.

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.

Secure Access QLFS data
Secure Access datasets for the QLFS are available from the April-June 1992 quarter, and include additional, detailed variables not included in the standard 'End User Licence' (EUL) versions (see under GN 33246). Extra variables that typically can be found in the Secure Access versions but not in the EUL relate to:

• geography (see 'Spatial Units' below)
• date of birth, including day
• education and training: including type of 'other qualifications', more detail regarding the number of O'levels/GCSE passes, type of qualification gained in last 12 months, class of first degree, type of degree held, UK country of highest degree, type of current educational institution, level of Welsh baccalaureate, activities to improve knowledge or skills in last 12 months, attendance at adult learning taught courses, attendance at leisure or educational classes, self-teaching, payment of job-related training fees
• household and family characteristics: including number of family units (and extended family units) with dependent children only, and with non-dependent children only, total number of family units with more than one person, total number of eligible people, type of household, type of family unit, number of bedrooms
• employment: including industry code of main job, whether working full-time or part-time, reason job is temporary, payment of own National Insurance and tax, when started working at previous job, whether paid or self-employed in previous job, contracts with employment agency
• unemployment and job hunting: including main reason for not being employed prior to current job, reasons for leaving job (provision of care or other personal/family reasons), use of internet for job hunting, if and when will work in the future
• temporary leave from work: including proportion of salary received and duration of leave
• accidents at work and work-related health problems
• nationality, national identity and country of birth: including whether lived continuously in UK, month of most recent arrival to UK, frequency of Welsh speaking
• occurrence of learning difficulty or disability
• benefits, including additional variables on type of benefits claimed and tax credit payments

Secure Access versions of QLFS household datasets are available from 2009 under SN 7674.

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.

Well-Being variables are not included in the LFS
Users should note that subjective well-being variables (Satis, Worth, Happy, Anxious and Sad) are not available on the LFS, despite being referenced in the questionnaire. Users who wish to analyse well-being variables should apply for the Annual Population Survey instead (see SNs 6721 and 7961).

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the relevant versions of each volume of the user guide. However, LFS volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

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.

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.

Latest Edition Information

For the thirty-eighth edition (October 2023), a new data file for April-June 2023 and a new 2023 variable catalogue have been added to the study.

Introducing the Indeed Job Postings Index

Hiring Lab's Job Postings Tracker is being re-released as the Indeed Job Postings Index. By Chris Glynn

Indeed Hiring Lab is re-releasing our Job Postings Tracker as the Indeed Job Postings Index, a daily measure of labor market activity that is updated and will continue to be released weekly. Covering seven national markets in the US, Canada, United Kingdom, Ireland, France, Germany, and Australia, the Indeed Job Postings Index meets one of Hiring Lab’s primary goals: produce high quality and high frequency labor market metrics using Indeed’s proprietary data.

The primary difference between the Indeed Job Postings Index and the legacy Job Postings Tracker is the level. The Indeed Job Postings Index is set to 100 on February 1, 2020, and this effectively provides a uniform level shift of 100 to the existing Job Postings Tracker across all time points.The Job Postings Tracker measured the percent change in postings from February 1st, 2020. For example, if the Job Postings Tracker were 40%, the corresponding Indeed Job Postings Index on the same date would be 140. Additionally, we are now including year-over-year and month-over-month percent changes in the Indeed Job Postings Index as part of our data portal on hiringlab.org/data and on our GitHub page. Month-over-month changes are calculated as 28 day (4 week) differences to control for day of week.

As Covid-19 fades from the global labor market discussion, moving to an index better reflects current economic conditions. The Indeed Job Postings Index allows us to compare job postings more naturally across flexible date ranges as opposed to comparing to the pre-pandemic baseline. It also places Indeed’s job postings metric in a broader class of macroeconomic indexes such as the Case Shiller Index that measures house price appreciation and the Consumer Price Index that measures inflation.

Data Schema Each market covered by a Hiring Lab economist has a folder in this repo. Each folder contains the following files:

aggregate_job_postings_{country_code}.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings and new jobs postings (on Indeed for 7 days or fewer) for that market, as well as non-seasonally adjusted postings since February 1, 2020 for total job postings.

job_postings_by_sector_{country_code}.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for occupational sectors for that market. We do not share sectoral data for Ireland.

For certain markets, we also share subnational job postings trends. In the United States, we provide:

metro_job_postings_us.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in US metropolitan areas with a population of at least 500,000 people.

state_job_postings_us.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in the US states and the District of Columbia.

In Canada, we provide:

provincial_postings_ca.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in each Canadian provinces. In the United Kingdom, we provide:

regional_postings_gb.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in each region in the UK.

city_postings_gb.csv This file contains the % change in seasonally-adjusted postings since February 1, 2020 for total job postings in each city in the UK.

Github link: https://github.com/hiring-lab/job_postings_tracker#data-schema Hiring Lab Link: https://www.hiringlab.org/2022/12/15/introducing-the-indeed-job-postings-index/

The JLMPS 2010 offers significant advantages over the regular Employment and Unemployment (EUS) survey conducted quarterly by the Department of Statistics (DOS). Although it is only the first wave of what is to be a longitudinal survey, it contains a number of retrospective questions that allow us to reconstruct entire employment trajectories rather than simply get a snapshot of a single point in time. The main advantage of this approach is that it allows for the examination of flows into various segments of the labor market and not simply stocks over time. The JLMPS 2010 data also offers significant advantages over the EUS in its ability to identify informal employment in its various guises, including wage and salary employment without contracts or social insurance and self-employment and unpaid family employment. It also offers a more detailed view of employment conditions including paid and unpaid leaves, the presence of health insurance, hours of work, and the type and size of economic unit in which the worker is employed. Since JLMPS 2010 has the same sampling strategy as the EUS it focuses exclusively on the population residing in regular households rather than in collective residential units. This makes it equally likely as the EUS to under-sample the foreign worker population in Jordan. The data may be accessed through the ERF Data Portal: http://www.erfdataportal.com/index.php/catalog/138

Historical resident Labor Force and Employment, not seasonally adjusted Index of Washington state and labor market areas, 1990-2022 Source: Employment Security Department/DATA; U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics