Employment Rate in the United States decreased to 59.70 percent in May from 60 percent in April of 2025. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Infra-Annual Labor Statistics: Employment Rate Total: From 25 to 54 Years for United States (LREM25TTUSM156S) from Jan 1977 to Apr 2025 about 25 to 54 years, employment-population ratio, employment, population, rate, and USA.

In 2024, about 62.7 percent of the Asian community was employed. The highest employment rate was found among Mexican-Americans, at 64.1 percent, and the lowest employment rate was found among Puerto Ricans, at 55.2 percent. In total, around 60 percent of all working-age Americans were employed at this time.

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.

The adult employment-to-population in 2022 was higher in Iceland than any other Organization for Economic Cooperation and Development (OECD) country, at 83.3 percent. The Netherlands ranked second with nearly 82 percent. On the other hand, it was lowest in Türkiye. The average for the entire bloc was 67.8. The employment-to-population ratio is a valuable addition to the unemployment rate because it includes the entire adult population, whereas the unemployment rate only considers those who are working or actively seeking a job, that is, only the workforce.

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.

Labor Force Participation Rate in the United States decreased to 62.40 percent in May from 62.60 percent in April 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.

Full Time Employment in the United States decreased to 134840 Thousand in May from 135463 Thousand in April of 2025. This dataset provides - United States Full Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Employment Level - High School Graduates, No College, 25 Yrs. & over (LNU02027660) from Jan 1992 to May 2025 about secondary schooling, secondary, 25 years +, tertiary schooling, education, household survey, employment, and USA.

Employment Rate in Ireland increased to 74.70 percent in the first quarter of 2025 from 74.30 percent in the fourth quarter of 2024. This dataset provides - Ireland Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

Data highlights the relationship between educational attainment and the labour market by examining employment rates by age group and sex for the Organisation for Economic Co-operation and Development (OECD), Canada, provinces and territories. The data come from the Labour Force Survey.

Graph and download economic data for Employment-Population Ratio - 25-54 Yrs. (LNS12300060) from Jan 1948 to May 2025 about 25 to 64 years, employment-population ratio, employment, population, and USA.

The Bahamas had the highest female employment-to-population ratio in the world in 2023 at 90 percent of the women in working age in some form of employment. Iceland had the second highest female employment rate of the countries, reaching 81 percent.

Employment levels and rates by country of birth and nationality, UK, published quarterly, non-seasonally adjusted. Labour Force Survey. These are official statistics in development.

United States US: Employment Rate: Age 15-74 data was reported at 67.118 % in 2026. This records an increase from the previous number of 66.828 % for 2025. United States US: Employment Rate: Age 15-74 data is updated yearly, averaging 65.646 % from Dec 1985 (Median) to 2026, with 42 observations. The data reached an all-time high of 69.750 % in 2000 and a record low of 61.414 % in 2010. United States US: Employment Rate: Age 15-74 data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.EO: Employment and Unemployment: Forecast: OECD Member: Annual.

Vietnam Employment: Female: High Level Professionals data was reported at 2,068.200 Person th in Jun 2018. This records an increase from the previous number of 2,044.300 Person th for Mar 2018. Vietnam Employment: Female: High Level Professionals data is updated quarterly, averaging 1,848.500 Person th from Jun 2012 (Median) to Jun 2018, with 25 observations. The data reached an all-time high of 2,216.600 Person th in Dec 2017 and a record low of 1,442.300 Person th in Jun 2012. Vietnam Employment: Female: High Level Professionals data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G024: Employment by Occupation: Quarterly.

Vietnam Employment: Male: High Level Professionals data was reported at 1,752.100 Person th in Jun 2018. This records an increase from the previous number of 1,677.600 Person th for Mar 2018. Vietnam Employment: Male: High Level Professionals data is updated quarterly, averaging 1,575.600 Person th from Jun 2012 (Median) to Jun 2018, with 25 observations. The data reached an all-time high of 1,800.800 Person th in Dec 2017 and a record low of 1,368.500 Person th in Dec 2012. Vietnam Employment: Male: High Level Professionals data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G024: Employment by Occupation: Quarterly.

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.