Blockchain expert will be the most in-demand profession in the future Spanish job market as of 2020, with 6.79 percent of jobs offered. Machine learning specialist and health and welfare specialist ranked second and third respectively with over 5 percent each. The future job market presents a very different outlook compared to the present job market; in 2019 the most in-demand job in Spain was sales representative.

According to the study realized by IPSOS, most of the people who lost their jobs worked either in the sales or in the logistics industry. For 17 percent of respondents working in IT and Telecommunications nothing changed during the coronavirus crisis in Romania, while 13 percent of people working in tourism were forced to apply for technical unemployment.
For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

Employment Rate in the United States remained unchanged at 59.70 percent in June. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Individuals working in the private sector were more concerned about the potential impact of coronavirus (COVID-19) on their jobs than those working in the public sector. As a result, 66 percent of respondents working in the private sector stated that the pandemic would affect their work. In comparison, only 50 percent of respondents working in the public sector reported the same concern. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. 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.

In September 2022, Belgium and the Netherlands' share of tech jobs that were characterized as hard to fill in relation to total tech jobs was at ** percent. A job position that is hard to fill is defined as a job that remained for more than ***** days on the relevant job posting site Indeed.com without finding a suitable candidate. By contrast, for the United Kingdom, the share of tech jobs that were hard to fill was below ** percent.

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

Long-term Industry Projections for a 10-year time horizon are produced for the State and its labor market regions to provide individuals and organizations with an insight into future industry trends to make informed decisions on individual career and organizational program development. Long-term projections are revised every 2 years. Data are not available for geographies below the labor market regions. Detail may not add to summary lines due to suppression of confidential data.

City Labor Market: Demand-Supply Ratio: Kunming data was reported at 2.130 NA in Mar 2020. This records an increase from the previous number of 1.900 NA for Dec 2018. City Labor Market: Demand-Supply Ratio: Kunming data is updated quarterly, averaging 1.520 NA from Mar 2008 (Median) to Mar 2020, with 26 observations. The data reached an all-time high of 2.260 NA in Mar 2012 and a record low of 1.050 NA in Mar 2014. City Labor Market: Demand-Supply Ratio: Kunming data remains active status in CEIC and is reported by Ministry of Human Resources and Social Security. The data is categorized under China Premium Database’s Labour Market – Table CN.GJ: City Labor Market: Demand-Supply Ratio.

Labor Force Participation Rate in the United States decreased to 62.30 percent in June from 62.40 percent in May of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

These tables contain the number of online job adverts split by local authority and occupation (SOC 2020).

This dataset presents data on the summary statistics of employment and population for metropolitan areas following the Greater Capital City Statistical Area (GCCSA) regions as of December 2020. The boundaries for this dataset follow the 2016 edition of the Australian Statistical Geography Standard (ASGS).

The Australian Department of Education, Skills and Employment publishes a range of labour market data on its Labour Market Information Portal. The data provided includes unemployment rate, employment rate, participation rate, youth unemployment rate, unemployment duration, population by age group and employment by industry and occupation.

AURIN has spatially enabled the original data. Data Source: ABS Labour Force Survey. All statistics are 12-month averages of original data, December 2020. The ABS advises that analysis of regional labour force estimates should typically be based on annual averages, which are important for understanding the state of the labour market and providing medium and long-term signals. The application of annual averages, however, is unlikely to accurately or quickly detect turning points in the regional data during periods of significant change (such as during the onset of the COVID-19 pandemic). Original data at the ABS Statistical Area 4 (SA4) level can be found in Table 16

Job Offers in the United States increased to 7769 Thousand in May from 7395 Thousand in April of 2025. This dataset provides the latest reported value for - United States Job Openings - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This table contains information about the labor market situation of all young people aged 15 to 27 in the Netherlands who were registered in the Personal Records Database (BRP) on 1 October of the reference year. It is indicated whether young people work, whether they go to school, whether they receive benefits and whether they are registered as a job seeker with the UWV Werkbedrijf. For employed young people, a distinction is made between employees and the self-employed. The data can be broken down into various regional classifications (part of the country, province, COROP, labor market region and municipality). The total number of young people (aged 15 to 27) receiving benefits in this table is higher than in other StatLine tables on social security. This is because the number of benefits is counted over the entire month instead of the situation being presented on the last Friday of the month. This table also includes all social security benefits related to incapacity for work, illness, unemployment or social assistance benefits. The reporting period is the month of October of the reference year. Whether someone goes to school is determined on the basis of registration in government-funded education on 1 October of the reference year. The municipalities are classified according to the situation on 1 January 2020. Data available from 2005 up to and including 2019. Data on whether or not you are registered as a jobseeker with the UWV Werkbedrijf are available from 2018. Predecessors of this table used data from the UWV Werkbedrijf (formerly CWI). Because this source was not continued in 2019, a switch was made to Registered Job Seekers UWV (GWU). However, these data are not available for the years 2005-2017. The figures relate to the situation in October of the year in question. Status of the figures: The figures for the years 2005 to 2018 in this table are final. The figures for 2019 are provisional. Changes as of March 24, 2022: None, this table has been discontinued. Changes as of March 4, 2021: None, this is a new table. Compared to the predecessor of this table (see paragraph 3), the way in which registered job seekers are determined has changed. Unfortunately, for the years 2005-2017 it is no longer possible to make a breakdown according to whether or not registered jobseekers with the UWV. Moreover, the figure for 2018 differs slightly from the predecessor due to the use of this new source (see section 3). When will new numbers come out? Not applicable anymore. This table is followed by: Labor market situation for young people (15 to 27 years); region (2021 format). See section 3.

1. 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.

The Online Recruitment Sites industry has boomed since the 2000s as job searches have moved online and the internet has become an indispensable part of daily life. The internet has become the primary medium for communicating and accessing information, the main driving force behind this industry's rise. Job seekers and employers have increasingly turned to online recruitment sites to look for new openings and find new talent pools. Revenue generated from online recruitment sites is expected to grow at a CAGR of 8.3% to $15.7 billion over the five years to 2023. While growth has been fueled by an extremely tight labor market following pandemic disruptions, revenue is forecast to contract 4.6% in 2023. Low costs associated with starting an online company have encouraged new companies to begin operations online. The largest online recruitment sites have increased market share through organic growth and via the acquisition of smaller players, which have targeted niche industries. Incumbents hold a competitive advantage in developing brand names, which has made it difficult for new sites to gain market share. Nonetheless, low barriers to entry and strength in demand for professional and technical recruiting have enabled some niche job boards to succeed within their respective markets. The growing advantages associated with using online recruitment sites and the scalability of online platforms enable sizable profit margins. Online hiring played an integral role across the economy during the COVID-19 pandemic, as employers have also to interact with customers and employees in new ways. Driven by the rapid development and adoption of big data analytics and mobile computing, online recruitments sites are expected to provide a broader range of services that go beyond standard job posting services and resume collection. These services will enable online recruiters to compete more effectively with traditional recruiting companies and in-house hiring departments. Meanwhile, a steady labor market will likely create new job openings even as interest rates rise, particularly in small- and medium-sized businesses. Revenue across online recruitment sites is forecast to grow at a CAGR of 4.4% to $19.5 billion over the five years to 2028.

Italy's public expenditure on the labor market accounted for three percent of the GDP in 2020. The public spending on the job market includes public employment services (PES), training, hiring subsidies, job creations in the public sector, as well as unemployment benefits. Compared to the period from 2007 to 2019, the spending drastically increased in 2020, following the consequences brought in by the Coronavirus pandemic. In Italy, the total social expenditure amounts to 28.2 percent of the country's GDP, some of the highest among the OECD countries.

The North American tech industry posted over 1.7 million jobs requiring at least one of the skill areas shown here between December 2019 to November 2020. The highest number of open positions being 634,600 positions in software development methodologies (DevOps) expertise.

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.