In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

Abstract

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

Geographic coverage

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

Analysis unit

Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

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

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

Mode of data collection

Face-to-face [f2f]

Research instrument

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

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

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

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

Notes on survey modules:

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

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

As of July 2020, a total share of 82 percent of Poles declared that they considered changing their current employment. This was an increase of 8.9 percent compared to July 2016, as well as the record share of respondents who expressed the desire to change their jobs within the observed timeframe.

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.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

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.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - 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.

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

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...

Unemployment Rate in the United States remained unchanged at 4.20 percent in May. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States Expected Job Reallocation data was reported at 3.514 % in Sep 2020. This records an increase from the previous number of 3.119 % for Aug 2020. United States Expected Job Reallocation data is updated monthly, averaging 2.297 % from Sep 2016 (Median) to Sep 2020, with 49 observations. The data reached an all-time high of 5.999 % in May 2020 and a record low of 0.867 % in Oct 2018. United States Expected Job Reallocation data remains active status in CEIC and is reported by Federal Reserve Bank of Atlanta. The data is categorized under Global Database’s United States – Table US.S018: Business Uncertainty Index.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The Class of Worker status "unpaid family workers" may have earnings. Earnings reflect any earnings from all jobs held during the 12 months prior to the ACS interview. The Class of Worker status reflects the job or business held the week prior to the ACS interview, or the last job held by the respondent..In 2019, methodological changes were made to the class of worker question. These changes involved modifications to the question wording, the category wording, and the visual format of the categories on the questionnaire. The format for the class of worker categories are now listed under the headings "Private Sector Employee," "Government Employee," and "Self-Employed or Other." Additionally, the category of Active Duty was added as one of the response categories under the "Government Employee" section for the mail questionnaire. For more detailed information about the 2019 changes, see the 2016 American Community Survey Content Test Report for Class of Worker located at http://www.census.gov/library/working-papers/2017/acs/2017_Martinez_01.html..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Abstract copyright UK Data Service and data collection copyright owner.

The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.

For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.

Occupation data for 2021 and 2022
The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022

APS Well-Being Datasets
From 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.

APS disability variables
Over time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage.

End User Licence and Secure Access APS data
Users should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to:

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address

The Secure Access data have 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...

In 1990, the unemployment rate of the United States stood at 5.6 percent. Since then there have been many significant fluctuations to this number - the 2008 financial crisis left millions of people without work, as did the COVID-19 pandemic. By the end of 2022 and throughout 2023, the unemployment rate came to 3.6 percent, the lowest rate seen for decades. However, 2024 saw an increase up to four percent. For monthly updates on unemployment in the United States visit either the monthly national unemployment rate here, or the monthly state unemployment rate here. Both are seasonally adjusted. UnemploymentUnemployment is defined as a situation when an employed person is laid off, fired or quits his work and is still actively looking for a job. Unemployment can be found even in the healthiest economies, and many economists consider an unemployment rate at or below five percent to mean there is 'full employment' within an economy. If former employed persons go back to school or leave the job to take care of children they are no longer part of the active labor force and therefore not counted among the unemployed. Unemployment can also be the effect of events that are not part of the normal dynamics of an economy. Layoffs can be the result of technological progress, for example when robots replace workers in automobile production. Sometimes unemployment is caused by job outsourcing, due to the fact that employers often search for cheap labor around the globe and not only domestically. In 2022, the tech sector in the U.S. experienced significant lay-offs amid growing economic uncertainty. In the fourth quarter of 2022, more than 70,000 workers were laid off, despite low unemployment nationwide. The unemployment rate in the United States varies from state to state. In 2021, California had the highest number of unemployed persons with 1.38 million out of work.

During the COVID-19 pandemic, certain occupations and industries were deemed "essential", and typically included individuals who worked in healthcare, food service, public transportation, etc. However, early on in the pandemic, while these workers faced disproportionately higher risks, they often did not receive adequate personal protective equipment (PPE), were unable to work from home, and were limited in their ability to take other precautions to safeguard their health (Chen et al., 2021). As a result, previous studies have documented higher rates of infection, hospitalization, and death among essential workers compared to their non-essential worker counterparts (Selden & Berdahl, 2021; Wei et al., 2022). This dataset provides users with information on the number and proportion of essential workers in census tracts or ZIP Code tabulation areas (ZCTAs) in the United States over the 2016-2020 period.

Data Source: The primary data source used for this analysis are point-level business establishment data from InfoUSA. This commercial database produced by InfoGroup provides a comprehensive list of businesses in the SCAG region, including their industrial classification, number of employees, and several additional fields. Data have been post-processed for accuracy by SCAG staff and have an effective date of 2016. Locally-weighted regression: First, the SCAG region is overlaid with a grid, or fishnet, of 1km, 2km, and ½-km per cell. At the 1km cell size, there are 16,959 cells covering the SCAG region. Using the Spatial Join feature in ArcGIS, a sum total of business establishments and total employees (i.e., not separated by industrial classification) were joined to each grid cell. Note that since cells are of a standard size, the employment total in a cell is the equivalent of the employment density. A locally-weighted regression (LWR) procedure was developed using the R Statistical Software package in order to identify subcenters.The below procedure is described for 1km grid cells, but was repeated for 2km and 1/2km cells. Identify local maxima candidates.Using R’s lwr package, each cell’s 120 nearest neighbors, corresponding to roughly 5.5 km in each direction, was explored to identify high outliers or local maxima based on the total employment field. Cells with a z-score of above 2.58 were considered local maxima candidates.Identify local maxima. LWR can result in local maxima existing within close proximity. This step used a .dbf-format spatial weights matrix (knn=120 nearest neighbors) to identify only cells which are higher than all of their 120 nearest neighbors. At the 1km scale, 84 local maxima were found, which will form the “peak” of each individual subcenter. Search adjacent cells to include as part of each subcenter. In order to find which cells also are part of each local maximum’s subcenter, we use a queen (adjacency) contiguity matrix to search adjacent cells up to 120 nearest neighbors, adding cells if they are also greater than the average density in their neighborhood. A total of 695 cells comprise subcenters at the 1km scale. A video from Kane et al. (2018) demonstrates the above aspects of the methodology (please refer to 0:35 through 2:35 of https://youtu.be/ylTWnvCCO54), with several minor differences which result in a different final map of subcenters: different years and slightly different post-processing steps for InfoUSAdata, video study covers 5-county region (Imperial county not included), and limited to 1km scale subcenters.A challenge arises in that using 1km grid cells may fail to identify the correct local maximum for a particularly large employment center whose experience of high density occurs over a larger area. The process was repeated at a 2km scale, resulting in 54 “coarse scaled” subcenters. Similarly, some centers may exist with a particularly tightly-packed area of dense employment which is not detectable at the medium, 1km scale. The process was repeated again with ½-km grid cells, resulting in 95 “fine scaled” subcenters. In many instances, boundaries of fine, medium, and coarse scaled subcenters were similar, but differences existed. The next step was to qualitatively comparing results at each scale to create the final map of 72 job centers across the region. Most centers are medium scale, but some known areas of especially employment density were better captured at the 2km scale while . Giuliano and Small’s (1991) “ten jobs per acre” threshold was used as a rough guide to test for reasonableness when choosing a larger or smaller scale. For example, in some instances, a 1km scale included much additional land which reduced job density well below 10 jobs per acre. In this instance, an overlapping or nearby 1/2km scaled center provided a better reflection of the local employment peak. Ultimately, the goal was to identify areas where job density is distinct from nearby areas. Finally, in order to serve land use and travel demand modeling purposes for Connect SoCal, job centers were joined to their nearest TAZ boundaries. While the identification mechanism described above uses a combination of point and grid cell boundaries, the job centers boundaries expressed in this layer, and used for Connect SoCal purposes, are built from TAZ geographies. In Connect SoCal, job centers are associated with one of three strategies: focused growth, coworking space, or parking/AVR.Data Field/Value description:name: Name of job center based on name of local jurisdiction(s) or other discernable feature.Focused_Gr: Indicates whether job center was used for the 2020 RTP/SCS Focused Growth strategy, 1: center was used, 0: center was not used.Cowork: Indicates whether job center was used for the 2020 RTP/SCS Co-working space strategy, 1: center was used, 0: center was not used.Park_AVR: Indicates whether job center was used for the 2020 RTP/SCS parking and average vehicle ridership (AVR) strategies, 1: center was used, 0: center was not used. nTAZ: number of Transportation Analysis Zones (TAZs) which comprise this center.emp16: Estimated number of workers within job center boundaries based on 2016 InfoUSA point-based business establishment data. Values are rounded to the nearest 1000. acres: Land area within job center boundaries based on grid-based identification mechanism (i.e., not based on TAZ boundaries shown). Values are rounded to the nearest 100.

In May 2024, about 4.5 percent of recent college graduates were unemployed in the United States. This was a significant decrease from September 2020, when the unemployment rate among recent college graduates was at nine percent.

These data are based on data collected as part of the national data collection operation on the professional integration of graduates of vocational licenses.

2 new indicators were added for the 2020 session:

Employment rate: share of employed graduates among all active (employment or research) or inactive graduates

Rate of paid employment in France: share of graduates in paid employment in France among all active (employment or research) or inactive graduates

This investigation was conducted

• in December 2022, 18 and 30 months after graduation, among the graduates of the Professional License of the 2020 session;
• in December 2021, 18 and 30 months after graduation, among the graduates of the 2019 Professional License session;
• in December 2020, 18 and 30 months after graduation, among the 2018 Professional License graduates;
• in December 2019, 18 and 30 months after graduation, among the graduates of the Professional License of the 2017 session;
• in December 2018, 18 and 30 months after graduation, among the graduates of the Professional License of the 2016 session;

• in December 2017, 18 and 30 months after graduation, among the graduates of the Professional License of the 2015 session;

• in December 2016, 18 and 30 months after graduation, among the graduates of the Professional License of the 2014 session;

• in December 2015, 18 and 30 months after graduation, among the graduates of the Professional License of the 2013 session.

The insertion rate is defined as the percentage of graduates in any job out of all graduates in the labour market. It is calculated on graduates of French nationality, from initial training, who entered the labour market immediately and permanently after graduation in 2013, 2014, 2015, 2016, 2017, 2018, 2019 or 2020.

The information collected on the salary relates to the net salary, including bonuses. The wages displayed correspond to the median values on full-time jobs. On the basis of these values, an annual gross salary is estimated, on the basis of a flat rate of change from net to gross of 1.3 (average data on private sector salaries).

The survey was carried out by universities under a charter whose provisions aim to ensure comparability of results between institutions. The overall coordination and operation of the survey is the responsibility of the Ministry of Higher Education and Research.

Sources of additional data:

% of graduate scholarship holders: observed data on the population of the labour market integration survey.

Regional unemployment rate: INSEE - 4th quarter 2015 for the 2013 session, INSEE - 1st quarter 2017 for the 2014, 4th quarter 2017 for the 2015, 4th quarter 2018 for the 2016, 4th quarter 2019 for the 2017, 4th quarter 2020 for the 2018, 4th quarter 2021 for the 2019, 4th quarter 2022 for the 2020 session.

Regional median monthly net salary: INSEE DADS 2013 for the session 2013, INSEE DADS 2014 for the session 2014, INSEE DADS 2015 for the session 2015, INSEE DADS 2016 for the session 2016, INSEE DADS 2017 for the session 2017, INSEE DADS 2018 for the session 2018, INSEE DADS 2019 for the session 2019, INSEE DADS 2020 for the session 2020 for 25-29 year olds employed full-time in the socio-professional categories "Frames and higher intellectual professions" and "Intermediate professions.

Legend: nd = not available (no respondents) ns = not significant (number of respondents less than 30).

Source: 18- and 30-month job placement survey of university graduates 2013, 2014, 2015, 2016, 2017, 2018, 2019 and 2020.

Collection: survey carried out by universities, treatments and synthesis carried out by MESR-SIES

Field: graduates of professional bachelor’s degrees 2013, 2014, 2015, 2016, 2017, 2018, 2019 and 2020 from universities in metropolitan France and the French overseas departments (excluding Paris-Dauphine and Gustave Eiffel University (for 2020 graduates)), of French nationality, from initial training, who entered the labour market immediately and permanently after graduation.

Over the last years, the highest share of job postings by tech companies in the U.S. was in Texas. At the same time, from 2020 to 2021, there were over four percent of job postings in Virginia and New York.

This study is the second in a series of reviews of effective employment and training ET program components and practices. The study included a review of research focusing on SNAP ET and other public workforce programs published from 2016 to 2020. Particular attention was given to recent changes to the SNAP ET program, new referral and retention strategies, and promising work-based learning interventions, like apprenticeships.

family resource (mplus).csv: data used in the current study (Early career gender differences in job burnout trajectories in Finland: Roles of work, family, and financial resources).

Variables: gender, income2013, income2016, income2020, parenthood2013, parenthood2016, parenthood2020, job burnout2013, job burnout2016, job burnout2020, belongingness to workplace2013, belongingness to workplace2016, belongingness to workplace2020, partner support2013, partner support2016, partner support2020.

Please contact Yirou Fang (yirou.fang@helsinki.fi) for details of the data set.

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No. 953326. This study is a part of the ongoing FinEdu project, funded by grant from the Academy of Finland (210319) and the Jacobs Foundation.

Please contact Katariina Salmela-Aro (katariina.salmela-aro@helsinki.fi) for full access of the data.

In 2024, about 133 million people were employed in the United States on a full-time basis, returning to pre-pandemic levels. Employment in the U.S. The number of full-time employees in the United States has increased by more than 30 million people since 1991. In 1990, there were 98.67 million full-time employees which had increased to 134.06 million employees in 2023. After the recession, the number of full-time employees dropped and part-time employment increased significantly. There were about 28.1 million part-time employees in the country as of January 2024. Full-time employment usually is associated with certain benefits that are otherwise not offered to part-time employees, such as health insurance and annual leave. In the United States, a full time worker us usually regard as an individual who works more than 30 hours a week. Salaries nationwide Salary in the United States can vary greatly between ethnicities and gender. The median weekly earnings of a full-time salary worker, was 1,310 U.S. dollars for the Asian population, but only 794 U.S. dollars for the Black or African American population in 2020. In the same year, the median hourly wage for female salary workers 15.22 U.S. dollars, while males earned an hourly wage of 17.75 U.S. dollars. Based on a constant value, the wage among salary workers has fluctuated since 1979, where employees earned a median of 14.80 U.S. dollars, compared to 16.36 U.S. dollars in 2020. The minimum wage in the United States was established at 7.25 U.S. dollars per hour as of 2009.

As of October 2024, there were 133.89 million full-time employees in the United States. This is a slight decrease from the previous month, when there were 134.15 million full-time employees. The impact COVID-19 on employment In December 2019, the COVID-19 virus began its spread across the globe. Since being classified as a pandemic, the virus caused a global health crisis that has taken the lives of millions of people worldwide. The COVID-19 pandemic changed many facets of society, most significantly, the economy. In the first years, many businesses across all industries were forced to shut down, with large numbers of employees being laid off. The economy continued its recovery in 2022 with the nationwide unemployment rate returning to a more normal 3.4 percent as of April 2023. Unemployment benefits Because so many people in the United States lost their jobs, record numbers of individuals applied for unemployment insurance for the first time. As an early response to this nation-wide upheaval, the government issued relief checks and extended the benefits paid by unemployment insurance. In May 2020, the amount of unemployment insurance benefits paid rose to 23.73 billion U.S. dollars. As of December 2022, this value had declined to 2.24 billion U.S. dollars.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Tables for Workplace Geography are only available for States; Counties; Places; County Subdivisions in selected states (CT, ME, MA, MI, MN, NH, NJ, NY, PA, RI, VT, WI); Combined Statistical Areas; Metropolitan and Micropolitan Statistical Areas, and their associated Metropolitan Divisions and Principal Cities; Combined New England City and Town Areas; New England City and Town Areas, and their associated Divisions and Principal Cities. Tables B08601, B08602, B08603, and B08604 are also available for Place parts and County Subdivision parts for the 5-year ACS datasets..These tabulations are produced to provide estimates of workers at the location of their workplace. Estimates of counts of workers at the workplace may differ from those of other programs because of variations in definitions, coverage, methods of collection, reference periods, and estimation procedures. The ACS is a household survey which provides data that pertains to individuals, families, and households..Workers include members of the Armed Forces and civilians who were at work last week..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Turkey Job Placement: Year to Date: Men Topwear Sewing Element data was reported at 120.000 Number in Jan 2021. This records a decrease from the previous number of 573.000 Number for Jun 2020. Turkey Job Placement: Year to Date: Men Topwear Sewing Element data is updated monthly, averaging 154.000 Number from Jun 2016 (Median) to Jan 2021, with 13 observations. The data reached an all-time high of 573.000 Number in Jun 2020 and a record low of 0.000 Number in Jun 2016. Turkey Job Placement: Year to Date: Men Topwear Sewing Element data remains active status in CEIC and is reported by Turkish Employment Agency. The data is categorized under Global Database’s Turkey – Table TR.G115: Open Jobs and Job Placements by Profession: Year to Date. [COVID-19-IMPACT]