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The current value of the Job Quits in United States is 3.452 M . The Job Quits in United States increased to 3.452 M on 4/1/2024, after it was 3.409 M on 3/1/2024. From 12/1/2000 to 5/1/2024, the average GDP in United States was 2.83 M . The all-time high was reached on 4/1/2022 with 4.52 M , while the lowest value was recorded on 8/1/2009 with 1.56 M .
Job Quits ·
3 years
5 years
10 years
25 Years
Max
Job resignations | |
---|---|
12/1/2000 | 2.88 M |
1/1/2001 | 3.25 M |
2/1/2001 | 3.05 M |
3/1/2001 | 3.05 M |
4/1/2001 | 3.16 M |
5/1/2001 | 2.99 M |
6/1/2001 | 2.88 M |
7/1/2001 | 2.95 M |
8/1/2001 | 2.93 M |
9/1/2001 | 2.7 M |
10/1/2001 | 2.8 M |
11/1/2001 | 2.56 M |
12/1/2001 | 2.56 M |
1/1/2002 | 2.85 M |
2/1/2002 | 2.57 M |
3/1/2002 | 2.49 M |
4/1/2002 | 2.62 M |
5/1/2002 | 2.54 M |
6/1/2002 | 2.52 M |
7/1/2002 | 2.56 M |
8/1/2002 | 2.56 M |
9/1/2002 | 2.52 M |
10/1/2002 | 2.42 M |
11/1/2002 | 2.4 M |
12/1/2002 | 2.5 M |
1/1/2003 | 2.43 M |
2/1/2003 | 2.49 M |
3/1/2003 | 2.39 M |
4/1/2003 | 2.28 M |
5/1/2003 | 2.29 M |
6/1/2003 | 2.32 M |
7/1/2003 | 2.27 M |
8/1/2003 | 2.2 M |
9/1/2003 | 2.35 M |
10/1/2003 | 2.42 M |
11/1/2003 | 2.41 M |
12/1/2003 | 2.44 M |
1/1/2004 | 2.35 M |
2/1/2004 | 2.45 M |
3/1/2004 | 2.58 M |
4/1/2004 | 2.53 M |
5/1/2004 | 2.43 M |
6/1/2004 | 2.66 M |
7/1/2004 | 2.61 M |
8/1/2004 | 2.59 M |
9/1/2004 | 2.53 M |
10/1/2004 | 2.55 M |
11/1/2004 | 2.79 M |
12/1/2004 | 2.7 M |
1/1/2005 | 2.73 M |
2/1/2005 | 2.65 M |
3/1/2005 | 2.75 M |
4/1/2005 | 2.74 M |
5/1/2005 | 2.76 M |
6/1/2005 | 2.75 M |
7/1/2005 | 2.74 M |
8/1/2005 | 2.96 M |
9/1/2005 | 3.05 M |
10/1/2005 | 2.86 M |
11/1/2005 | 2.9 M |
12/1/2005 | 2.85 M |
1/1/2006 | 2.95 M |
2/1/2006 | 2.97 M |
3/1/2006 | 2.96 M |
4/1/2006 | 2.78 M |
5/1/2006 | 3 M |
6/1/2006 | 3.04 M |
7/1/2006 | 3.05 M |
8/1/2006 | 3.06 M |
9/1/2006 | 2.89 M |
10/1/2006 | 2.98 M |
11/1/2006 | 3.04 M |
12/1/2006 | 3 M |
1/1/2007 | 2.96 M |
2/1/2007 | 2.93 M |
3/1/2007 | 3 M |
4/1/2007 | 2.91 M |
5/1/2007 | 3.01 M |
6/1/2007 | 2.91 M |
7/1/2007 | 2.95 M |
8/1/2007 | 3.01 M |
9/1/2007 | 2.69 M |
10/1/2007 | 2.93 M |
11/1/2007 | 2.78 M |
12/1/2007 | 2.77 M |
1/1/2008 | 2.85 M |
2/1/2008 | 2.88 M |
3/1/2008 | 2.66 M |
4/1/2008 | 2.84 M |
5/1/2008 | 2.61 M |
6/1/2008 | 2.6 M |
7/1/2008 | 2.49 M |
8/1/2008 | 2.45 M |
9/1/2008 | 2.47 M |
10/1/2008 | 2.35 M |
11/1/2008 | 2.16 M |
12/1/2008 | 2.08 M |
1/1/2009 | 1.98 M |
2/1/2009 | 1.95 M |
3/1/2009 | 1.83 M |
4/1/2009 | 1.71 M |
5/1/2009 | 1.68 M |
6/1/2009 | 1.69 M |
7/1/2009 | 1.69 M |
8/1/2009 | 1.56 M |
9/1/2009 | 1.63 M |
10/1/2009 | 1.66 M |
11/1/2009 | 1.81 M |
12/1/2009 | 1.77 M |
1/1/2010 | 1.74 M |
2/1/2010 | 1.84 M |
3/1/2010 | 1.86 M |
4/1/2010 | 1.9 M |
5/1/2010 | 1.81 M |
6/1/2010 | 1.91 M |
7/1/2010 | 1.79 M |
8/1/2010 | 1.84 M |
9/1/2010 | 1.9 M |
10/1/2010 | 1.85 M |
11/1/2010 | 1.89 M |
12/1/2010 | 1.98 M |
1/1/2011 | 1.83 M |
2/1/2011 | 1.96 M |
3/1/2011 | 2.03 M |
4/1/2011 | 1.88 M |
5/1/2011 | 1.97 M |
6/1/2011 | 1.92 M |
7/1/2011 | 1.99 M |
8/1/2011 | 2.03 M |
9/1/2011 | 2.04 M |
10/1/2011 | 2 M |
11/1/2011 | 2.04 M |
12/1/2011 | 1.98 M |
1/1/2012 | 2.03 M |
2/1/2012 | 2.13 M |
3/1/2012 | 2.17 M |
4/1/2012 | 2.13 M |
5/1/2012 | 2.14 M |
6/1/2012 | 2.15 M |
7/1/2012 | 2.07 M |
8/1/2012 | 2.07 M |
9/1/2012 | 1.95 M |
10/1/2012 | 2.04 M |
11/1/2012 | 2.08 M |
12/1/2012 | 2.05 M |
1/1/2013 | 2.28 M |
2/1/2013 | 2.3 M |
3/1/2013 | 2.12 M |
4/1/2013 | 2.3 M |
5/1/2013 | 2.23 M |
6/1/2013 | 2.2 M |
7/1/2013 | 2.36 M |
8/1/2013 | 2.32 M |
9/1/2013 | 2.3 M |
10/1/2013 | 2.37 M |
11/1/2013 | 2.39 M |
12/1/2013 | 2.29 M |
1/1/2014 | 2.31 M |
2/1/2014 | 2.41 M |
3/1/2014 | 2.45 M |
4/1/2014 | 2.47 M |
5/1/2014 | 2.48 M |
6/1/2014 | 2.51 M |
7/1/2014 | 2.63 M |
8/1/2014 | 2.55 M |
9/1/2014 | 2.73 M |
10/1/2014 | 2.72 M |
11/1/2014 | 2.6 M |
12/1/2014 | 2.55 M |
1/1/2015 | 2.76 M |
2/1/2015 | 2.74 M |
3/1/2015 | 2.75 M |
4/1/2015 | 2.71 M |
5/1/2015 | 2.74 M |
6/1/2015 | 2.76 M |
7/1/2015 | 2.76 M |
8/1/2015 | 2.88 M |
9/1/2015 | 2.78 M |
10/1/2015 | 2.81 M |
11/1/2015 | 2.9 M |
12/1/2015 | 3.06 M |
1/1/2016 | 2.88 M |
2/1/2016 | 2.99 M |
3/1/2016 | 2.92 M |
4/1/2016 | 2.96 M |
5/1/2016 | 3.01 M |
6/1/2016 | 3.02 M |
7/1/2016 | 2.97 M |
8/1/2016 | 3 M |
9/1/2016 | 3.05 M |
10/1/2016 | 3.07 M |
11/1/2016 | 3.03 M |
12/1/2016 | 2.99 M |
1/1/2017 | 3.19 M |
2/1/2017 | 3.09 M |
3/1/2017 | 3.15 M |
4/1/2017 | 3.03 M |
5/1/2017 | 3.11 M |
6/1/2017 | 3.15 M |
7/1/2017 | 3.1 M |
8/1/2017 | 3.1 M |
9/1/2017 | 3.19 M |
10/1/2017 | 3.22 M |
11/1/2017 | 3.19 M |
12/1/2017 | 3.22 M |
1/1/2018 | 3.02 M |
2/1/2018 | 3.22 M |
3/1/2018 | 3.32 M |
4/1/2018 | 3.37 M |
5/1/2018 | 3.38 M |
6/1/2018 | 3.38 M |
7/1/2018 | 3.43 M |
8/1/2018 | 3.42 M |
9/1/2018 | 3.39 M |
10/1/2018 | 3.46 M |
11/1/2018 | 3.51 M |
12/1/2018 | 3.41 M |
1/1/2019 | 3.52 M |
2/1/2019 | 3.55 M |
3/1/2019 | 3.53 M |
4/1/2019 | 3.5 M |
5/1/2019 | 3.47 M |
6/1/2019 | 3.47 M |
7/1/2019 | 3.65 M |
8/1/2019 | 3.54 M |
9/1/2019 | 3.45 M |
10/1/2019 | 3.45 M |
11/1/2019 | 3.54 M |
12/1/2019 | 3.49 M |
1/1/2020 | 3.57 M |
2/1/2020 | 3.46 M |
3/1/2020 | 2.93 M |
4/1/2020 | 2 M |
5/1/2020 | 2.25 M |
6/1/2020 | 2.59 M |
7/1/2020 | 3.04 M |
8/1/2020 | 2.92 M |
9/1/2020 | 3.16 M |
10/1/2020 | 3.31 M |
11/1/2020 | 3.26 M |
12/1/2020 | 3.38 M |
1/1/2021 | 3.29 M |
2/1/2021 | 3.44 M |
3/1/2021 | 3.65 M |
4/1/2021 | 3.94 M |
5/1/2021 | 3.83 M |
6/1/2021 | 4.01 M |
7/1/2021 | 4.08 M |
8/1/2021 | 4.17 M |
9/1/2021 | 4.29 M |
10/1/2021 | 4.14 M |
11/1/2021 | 4.47 M |
12/1/2021 | 4.31 M |
1/1/2022 | 4.44 M |
2/1/2022 | 4.27 M |
3/1/2022 | 4.45 M |
4/1/2022 | 4.52 M |
5/1/2022 | 4.25 M |
6/1/2022 | 4.16 M |
7/1/2022 | 4.05 M |
8/1/2022 | 4.2 M |
9/1/2022 | 4.06 M |
10/1/2022 | 3.95 M |
11/1/2022 | 4.09 M |
12/1/2022 | 4.1 M |
1/1/2023 | 3.88 M |
2/1/2023 | 3.96 M |
3/1/2023 | 3.81 M |
4/1/2023 | 3.61 M |
5/1/2023 | 4.01 M |
6/1/2023 | 3.72 M |
7/1/2023 | 3.62 M |
8/1/2023 | 3.6 M |
9/1/2023 | 3.6 M |
10/1/2023 | 3.63 M |
11/1/2023 | 3.52 M |
12/1/2023 | 3.44 M |
1/1/2024 | 3.45 M |
2/1/2024 | 3.53 M |
3/1/2024 | 3.41 M |
4/1/2024 | 3.45 M |
Job Quits History
Date | Value |
---|---|
4/1/2024 | 3.452 M |
3/1/2024 | 3.409 M |
2/1/2024 | 3.527 M |
1/1/2024 | 3.446 M |
12/1/2023 | 3.439 M |
11/1/2023 | 3.516 M |
10/1/2023 | 3.634 M |
9/1/2023 | 3.596 M |
8/1/2023 | 3.595 M |
7/1/2023 | 3.615 M |
Similar Macro Indicators to Job Quits
Name | Current | Previous | Frequency |
---|---|---|---|
🇺🇸 ADP Employment Change | 152,000 | 188,000 | Monthly |
🇺🇸 Announcements of Hiring Plans | 4,236 Persons | 9,802 Persons | Monthly |
🇺🇸 Average Hourly Earnings | 0.4 % | 0.2 % | Monthly |
🇺🇸 Average Hourly Earnings YoY | 4.1 % | 4 % | Monthly |
🇺🇸 Average Weekly Hours | 34.3 Hours | 34.3 Hours | Monthly |
🇺🇸 Cancellation rate | 2.2 % | 2.2 % | Monthly |
🇺🇸 Challenger Job Cuts | 55,597 Persons | 72,821 Persons | Monthly |
🇺🇸 Continued Jobless Claims | 1.875 M | 1.869 M | frequency_weekly |
🇺🇸 Employed persons | 161.496 M | 161.864 M | Monthly |
🇺🇸 Employment Cost Index | 1.2 % | 0.9 % | Quarter |
🇺🇸 Employment Cost Index Benefits | 1.1 % | 0.7 % | Quarter |
🇺🇸 Employment Cost Index Wages | 1.1 % | 1.1 % | Quarter |
🇺🇸 Employment rate | 60.1 % | 60.2 % | Monthly |
🇺🇸 Full-time employment | 133.496 M | 133.66 M | Monthly |
🇺🇸 Initial Jobless Claims | 216,000 | 228,000 | frequency_weekly |
🇺🇸 Job Opportunities | 8.14 M | 7.919 M | Monthly |
🇺🇸 Job Opportunities | 7.418 M | 7.939 M | Monthly |
🇺🇸 Labor costs | 120.4 points | 120.1 points | Quarter |
🇺🇸 Labor force participation rate | 62.6 % | 62.7 % | Monthly |
🇺🇸 Layoffs and Terminations | 1.498 M | 1.678 M | Monthly |
🇺🇸 Long-term unemployment rate | 0.8 % | 0.74 % | Monthly |
🇺🇸 Manufacturing wages | -46,000 | -6,000 | Monthly |
🇺🇸 Minimum Wages | 7.25 USD/Hour | 7.25 USD/Hour | Annually |
🇺🇸 Non-Agricultural Productivity QoQ | 2.5 % | 0.4 % | Quarter |
🇺🇸 Non-farm Payrolls | 272,000 | 165,000 | Monthly |
🇺🇸 Nonfarm Private Employment | 229,000 | 158,000 | Monthly |
🇺🇸 Part-time work | 28.004 M | 27.718 M | Monthly |
🇺🇸 Population | 335.89 M | 334.13 M | Annually |
🇺🇸 Productivity | 111.909 points | 111.827 points | Quarter |
🇺🇸 Retirement Age Men | 66.67 Years | 66.5 Years | Annually |
🇺🇸 Retirement Age Women | 66.67 Years | 66.5 Years | Annually |
🇺🇸 State payroll accounting | 43,000 | 7,000 | Monthly |
🇺🇸 U6 Unemployment Rate | 7.4 % | 7.4 % | Monthly |
🇺🇸 Unemployed Persons | 6.984 M | 6.834 M | Monthly |
🇺🇸 Unemployment Claims 4-Week Average | 240,750 | 238,250 | frequency_weekly |
🇺🇸 Unemployment Rate | 4.1 % | 4.1 % | Monthly |
🇺🇸 Unit Labor Costs QoQ | 0.4 % | 3.8 % | Quarter |
🇺🇸 Wage Growth | 6.3 % | 6.4 % | Monthly |
🇺🇸 Wages | 29.99 USD/Hour | 29.85 USD/Hour | Monthly |
🇺🇸 Wages in Manufacturing | 28.1 USD/Hour | 27.98 USD/Hour | Monthly |
🇺🇸 Youth Unemployment Rate | 9.5 % | 9.2 % | Monthly |
In the United States, job quits represent voluntary separations initiated by employees. Consequently, the quits rate serves as an indicator of workers' willingness or ability to leave their jobs. This rate is calculated by dividing the number of quits by the total employment and then multiplying the result by 100.
Macro pages for other countries in America
- 🇦🇷Argentina
- 🇦🇼Aruba
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- 🇧🇴Bolivia
- 🇧🇷Brazil
- 🇨🇦Canada
- 🇰🇾Cayman Islands
- 🇨🇱Chile
- 🇨🇴Colombia
- 🇨🇷Costa Rica
- 🇨🇺Cuba
- 🇩🇴Dominican Republic
- 🇪🇨Ecuador
- 🇸🇻El Salvador
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- 🇯🇲Jamaica
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- 🇸🇷Suriname
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- 🇺🇾Uruguay
- 🇻🇪Venezuela
- 🇦🇬Antigua and Barbuda
- 🇩🇲Dominica
- 🇬🇩Grenada
What is Job Quits?
At Eulerpool, we understand that macroeconomic indicators provide an essential foundation for assessing and interpreting the health of an economy. One such crucial category is "Job Quits," which, despite often being overshadowed by more prominent metrics like unemployment rates and GDP growth, presents invaluable insights into the labor market dynamics and economic sentiment. Exploring the intricacies of job quits can reveal patterns and underlying trends that contribute significantly to the broader economic narrative, making it a vital instrument for professionals, researchers, policymakers, and businesses. Job quits refer to voluntary separations initiated by employees, as opposed to layoffs or discharges initiated by employers. This category serves as a proxy for employee confidence in the labor market. When workers believe there are better opportunities elsewhere, they are more likely to voluntarily leave their current positions. Conversely, lower job quit rates can signal economic uncertainty, where employees are less inclined to depart stable employment out of fear they may not find comparable or better opportunities. The metric of job quits is a fundamental indicator of labor market fluidity and worker confidence. An increase in voluntary quits often denotes a robust job market where employees feel secure in their prospects of finding new, potentially superior employment. It can indicate wage growth, as workers leave for higher-paying opportunities, and it can reflect a competitive market where companies must strive harder to retain talent. Higher quit rates drive businesses to improve working conditions, offer better benefits, and innovate in employee engagement. However, elevated job quits may also suggest challenges for employers facing higher turnover rates. High turnover can incur substantial costs—both direct, such as recruiting and training new employees, and indirect, through the loss of institutional knowledge and lowered productivity. Therefore, trends in job quits provide vital signals to employers about the satisfaction and engagement levels of their workforce, prompting strategies to enhance employee retention and organizational stability. In contrast, low job quit rates may suggest a conservative labor market where employees perceive fewer external opportunities. This can occur during economic downturns or periods of stagnation, where job security becomes paramount, and the risk of unemployment outweighs the potential benefits of job switching. While this may provide short-term stability for businesses, prolonged periods of low job quits might indicate underlying economic issues, such as lackluster job creation, stagnant wages, or limited career progression opportunities. Analyzing job quits also yields sector-specific insights. Different industries may exhibit varied quit rates due to unique working conditions, growth prospects, and employee preferences. For instance, the technology sector typically experiences higher quit rates due to rapid innovation and competitive job offers, whereas more traditional industries, such as manufacturing, might exhibit lower rates largely due to specialized skill requirements and fewer external opportunities. Therefore, sectoral analysis of job quits is critical for understanding industry-specific labor dynamics and tailoring workforce strategies accordingly. Additionally, demographic factors play a pivotal role in job quit trends. Age, education level, and geographic location are crucial variables that influence job mobility. Younger workers are generally more likely to quit, driven by career advancement opportunities and fewer familial obligations. Conversely, older employees may display lower quit rates due to stability preferences and greater attachment to their current roles. Educational attainment also affects quits, with more educated workers often exhibiting higher mobility due to broader employment prospects. Geography additionally impacts job quit patterns, influenced by regional economic conditions, local labor market health, and urbanization. Urban areas, characterized by more diverse and dynamic job markets, usually experience higher quit rates compared to rural regions, where job opportunities are more constrained. Understanding these demographic nuances allows for a comprehensive analysis of labor market health and assists in developing targeted economic and labor policies. For policymakers, tracking job quits serves as a litmus test for the efficacy of labor market policies and economic health. Elevated quits can signal the success of job creation initiatives and confidence in the economy, prompting policies to sustain growth and manage inflationary pressures from rising wages. On the other hand, low quits necessitate interventions to stimulate job creation, enhance worker training programs, and possibly reevaluate regulatory frameworks that may stifle labor market fluidity and innovation. Businesses also benefit from closely monitoring job quit trends. High quit rates within an organization can indicate underlying issues such as inadequate compensation, poor working conditions, or weak management structures. By addressing these areas proactively, companies can improve employee satisfaction and reduce turnover costs. In addition, understanding broader quit trends helps businesses strategize talent acquisition and retention, ensuring they remain competitive and attractive to top talent in a dynamic labor market. At Eulerpool, we provide detailed and up-to-date macroeconomic data, including comprehensive analyses of job quit trends. Our platform allows users to delve into granular data, compare trends across sectors, and understand the broader economic implications of job quit patterns. This information is essential for making informed decisions, shaping labor policies, and strategizing business operations. In conclusion, the category of "Job Quits" offers a multifaceted view into the state of the labor market and by extension, the economy. Higher quit rates generally indicate a healthy, dynamic job market with abundant opportunities, whereas lower rates often reflect economic uncertainty or stagnation. Sectoral, demographic, and geographic analyses of job quits enrich our understanding of labor market health, enabling professionals, businesses, and policymakers to make data-driven decisions that foster economic growth and stability. At Eulerpool, we are committed to providing the highest quality macroeconomic data to support these critical analyses and decisions.