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Japan Overtime Pay Year-over-Year (YoY)
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The current value of the Overtime Pay Year-over-Year (YoY) in Japan is 0.2 %. The Overtime Pay Year-over-Year (YoY) in Japan decreased to 0.2 % on 11/1/2023, after it was 0.2 % on 8/1/2023. From 1/1/1991 to 4/1/2024, the average GDP in Japan was 0.01 %. The all-time high was reached on 5/1/2021 with 19.9 %, while the lowest value was recorded on 5/1/2020 with -26.3 %.
Overtime Pay Year-over-Year (YoY) ·
3 years
5 years
10 years
25 Years
Max
| Overtime Compensation YoY | |
|---|---|
| 1/1/1991 | 2.1 % |
| 2/1/1991 | 2 % |
| 3/1/1991 | 0.1 % |
| 4/1/1994 | 0.2 % |
| 6/1/1994 | 2.2 % |
| 7/1/1994 | 0.4 % |
| 8/1/1994 | 0.8 % |
| 9/1/1994 | 3.1 % |
| 10/1/1994 | 5.5 % |
| 11/1/1994 | 7.5 % |
| 12/1/1994 | 9 % |
| 1/1/1995 | 5.6 % |
| 2/1/1995 | 7.1 % |
| 3/1/1995 | 6.6 % |
| 4/1/1995 | 6.6 % |
| 5/1/1995 | 4.9 % |
| 6/1/1995 | 2.3 % |
| 7/1/1995 | 2.5 % |
| 8/1/1995 | 4.2 % |
| 9/1/1995 | 2.7 % |
| 10/1/1995 | 0.6 % |
| 11/1/1995 | 0.9 % |
| 12/1/1995 | 1 % |
| 1/1/1996 | 1.7 % |
| 2/1/1996 | 3.8 % |
| 3/1/1996 | 5.2 % |
| 4/1/1996 | 2.4 % |
| 5/1/1996 | 3 % |
| 6/1/1996 | 3.9 % |
| 7/1/1996 | 7.4 % |
| 8/1/1996 | 6.2 % |
| 9/1/1996 | 6.4 % |
| 10/1/1996 | 7.1 % |
| 11/1/1996 | 6.6 % |
| 12/1/1996 | 6.3 % |
| 1/1/1997 | 6.2 % |
| 2/1/1997 | 5.1 % |
| 3/1/1997 | 3.6 % |
| 4/1/1997 | 7.6 % |
| 5/1/1997 | 6.3 % |
| 6/1/1997 | 4.8 % |
| 7/1/1997 | 2.8 % |
| 8/1/1997 | 3 % |
| 9/1/1997 | 2 % |
| 10/1/1997 | 0.9 % |
| 11/1/1997 | 0.5 % |
| 5/1/1999 | 0.7 % |
| 6/1/1999 | 0.2 % |
| 7/1/1999 | 2.5 % |
| 8/1/1999 | 3 % |
| 9/1/1999 | 3.8 % |
| 10/1/1999 | 3.4 % |
| 11/1/1999 | 6.4 % |
| 12/1/1999 | 3.9 % |
| 1/1/2000 | 2.4 % |
| 2/1/2000 | 2.9 % |
| 3/1/2000 | 4 % |
| 4/1/2000 | 3.9 % |
| 5/1/2000 | 4 % |
| 6/1/2000 | 4.9 % |
| 7/1/2000 | 4 % |
| 8/1/2000 | 4.5 % |
| 9/1/2000 | 5.5 % |
| 10/1/2000 | 4.3 % |
| 11/1/2000 | 4 % |
| 12/1/2000 | 3.8 % |
| 1/1/2001 | 2.5 % |
| 2/1/2001 | 1.4 % |
| 8/1/2002 | 1.1 % |
| 9/1/2002 | 1.7 % |
| 10/1/2002 | 2.3 % |
| 11/1/2002 | 5.4 % |
| 12/1/2002 | 5.2 % |
| 1/1/2003 | 4.7 % |
| 2/1/2003 | 5.3 % |
| 3/1/2003 | 4.1 % |
| 4/1/2003 | 2.2 % |
| 5/1/2003 | 3.7 % |
| 6/1/2003 | 2.6 % |
| 7/1/2003 | 4.2 % |
| 8/1/2003 | 2.7 % |
| 9/1/2003 | 3.9 % |
| 10/1/2003 | 2.7 % |
| 11/1/2003 | 3.8 % |
| 12/1/2003 | 2.8 % |
| 1/1/2004 | 6.4 % |
| 2/1/2004 | 4 % |
| 3/1/2004 | 4.7 % |
| 4/1/2004 | 5.1 % |
| 5/1/2004 | 5.7 % |
| 6/1/2004 | 5.8 % |
| 7/1/2004 | 6.3 % |
| 8/1/2004 | 5.1 % |
| 9/1/2004 | 3.7 % |
| 10/1/2004 | 4.2 % |
| 11/1/2004 | 2.6 % |
| 12/1/2004 | 4.9 % |
| 1/1/2005 | 0.8 % |
| 2/1/2005 | 1.5 % |
| 3/1/2005 | 0.1 % |
| 4/1/2005 | 2.3 % |
| 5/1/2005 | 1.4 % |
| 6/1/2005 | 2.8 % |
| 7/1/2005 | 2.1 % |
| 8/1/2005 | 1.7 % |
| 9/1/2005 | 1.5 % |
| 10/1/2005 | 2 % |
| 11/1/2005 | 1.4 % |
| 12/1/2005 | 2.2 % |
| 1/1/2006 | 2.8 % |
| 2/1/2006 | 2 % |
| 3/1/2006 | 3.3 % |
| 4/1/2006 | 2 % |
| 5/1/2006 | 3 % |
| 6/1/2006 | 2.2 % |
| 7/1/2006 | 2.3 % |
| 8/1/2006 | 2.5 % |
| 9/1/2006 | 3.5 % |
| 10/1/2006 | 1.8 % |
| 11/1/2006 | 3.2 % |
| 12/1/2006 | 2.4 % |
| 1/1/2007 | 0.1 % |
| 2/1/2007 | 1 % |
| 3/1/2007 | 0.1 % |
| 4/1/2007 | 1.4 % |
| 5/1/2007 | 1.2 % |
| 6/1/2007 | 0.3 % |
| 8/1/2007 | 0.8 % |
| 9/1/2007 | 0.9 % |
| 10/1/2007 | 0.8 % |
| 2/1/2008 | 1.8 % |
| 3/1/2008 | 3.4 % |
| 4/1/2008 | 0.1 % |
| 7/1/2008 | 0.2 % |
| 1/1/2010 | 2.3 % |
| 2/1/2010 | 8 % |
| 3/1/2010 | 12.7 % |
| 4/1/2010 | 12.3 % |
| 5/1/2010 | 11.2 % |
| 6/1/2010 | 12.1 % |
| 7/1/2010 | 12.3 % |
| 8/1/2010 | 10.8 % |
| 9/1/2010 | 10 % |
| 10/1/2010 | 6.4 % |
| 11/1/2010 | 6.3 % |
| 12/1/2010 | 6.2 % |
| 1/1/2011 | 3.5 % |
| 2/1/2011 | 4.3 % |
| 3/1/2011 | 1.6 % |
| 7/1/2011 | 0.2 % |
| 10/1/2011 | 2.8 % |
| 11/1/2011 | 2.1 % |
| 12/1/2011 | 1.6 % |
| 1/1/2012 | 2.9 % |
| 2/1/2012 | 3.9 % |
| 3/1/2012 | 4.4 % |
| 4/1/2012 | 5.6 % |
| 5/1/2012 | 6.8 % |
| 6/1/2012 | 4.9 % |
| 7/1/2012 | 0.9 % |
| 8/1/2012 | 2.4 % |
| 4/1/2013 | 0.1 % |
| 5/1/2013 | 0.1 % |
| 6/1/2013 | 0.7 % |
| 7/1/2013 | 2.1 % |
| 8/1/2013 | 2.6 % |
| 9/1/2013 | 3.3 % |
| 10/1/2013 | 5.5 % |
| 11/1/2013 | 5.4 % |
| 12/1/2013 | 4.6 % |
| 1/1/2014 | 4.4 % |
| 2/1/2014 | 4.1 % |
| 3/1/2014 | 5.3 % |
| 4/1/2014 | 5.6 % |
| 5/1/2014 | 3.6 % |
| 6/1/2014 | 2.7 % |
| 7/1/2014 | 3.2 % |
| 8/1/2014 | 0.8 % |
| 9/1/2014 | 1.5 % |
| 10/1/2014 | 0.8 % |
| 11/1/2014 | 0.6 % |
| 12/1/2014 | 0.2 % |
| 1/1/2015 | 2.1 % |
| 2/1/2015 | 0.5 % |
| 7/1/2015 | 0.7 % |
| 8/1/2015 | 1.6 % |
| 9/1/2015 | 1.3 % |
| 10/1/2015 | 1.8 % |
| 11/1/2015 | 1.2 % |
| 12/1/2015 | 1.3 % |
| 2/1/2016 | 0.1 % |
| 3/1/2016 | 1.3 % |
| 4/1/2016 | 1.1 % |
| 5/1/2016 | 0.5 % |
| 2/1/2017 | 0.5 % |
| 5/1/2017 | 0.3 % |
| 7/1/2017 | 0.2 % |
| 8/1/2017 | 1.3 % |
| 9/1/2017 | 1.2 % |
| 11/1/2017 | 1.9 % |
| 12/1/2017 | 0.6 % |
| 1/1/2018 | 0.1 % |
| 2/1/2018 | 0.4 % |
| 3/1/2018 | 2.2 % |
| 4/1/2018 | 1.8 % |
| 5/1/2018 | 2 % |
| 6/1/2018 | 3.5 % |
| 7/1/2018 | 1.6 % |
| 8/1/2018 | 1.3 % |
| 9/1/2018 | 0.2 % |
| 10/1/2018 | 1.7 % |
| 11/1/2018 | 0.6 % |
| 5/1/2019 | 0.9 % |
| 7/1/2019 | 0.1 % |
| 8/1/2019 | 0.1 % |
| 4/1/2021 | 5.4 % |
| 5/1/2021 | 19.9 % |
| 6/1/2021 | 18 % |
| 7/1/2021 | 11.6 % |
| 8/1/2021 | 6 % |
| 9/1/2021 | 4.6 % |
| 10/1/2021 | 2.3 % |
| 11/1/2021 | 2.9 % |
| 12/1/2021 | 5.2 % |
| 1/1/2022 | 4.3 % |
| 2/1/2022 | 4.9 % |
| 3/1/2022 | 4.2 % |
| 4/1/2022 | 5 % |
| 5/1/2022 | 5.3 % |
| 6/1/2022 | 4.8 % |
| 7/1/2022 | 4.7 % |
| 8/1/2022 | 4.1 % |
| 9/1/2022 | 6.8 % |
| 10/1/2022 | 7.7 % |
| 11/1/2022 | 5.4 % |
| 12/1/2022 | 2.9 % |
| 1/1/2023 | 0.5 % |
| 2/1/2023 | 1.2 % |
| 3/1/2023 | 1.2 % |
| 5/1/2023 | 0.5 % |
| 6/1/2023 | 1.9 % |
| 8/1/2023 | 0.2 % |
| 11/1/2023 | 0.2 % |
Overtime Pay Year-over-Year (YoY) History
| Date | Value |
|---|---|
| 11/1/2023 | 0.2 % |
| 8/1/2023 | 0.2 % |
| 6/1/2023 | 1.9 % |
| 5/1/2023 | 0.5 % |
| 3/1/2023 | 1.2 % |
| 2/1/2023 | 1.2 % |
| 1/1/2023 | 0.5 % |
| 12/1/2022 | 2.9 % |
| 11/1/2022 | 5.4 % |
| 10/1/2022 | 7.7 % |
Similar Macro Indicators to Overtime Pay Year-over-Year (YoY)
| Name | Current | Previous | Frequency |
|---|---|---|---|
🇯🇵 Employed persons | 67.61 M | 67.51 M | Monthly |
🇯🇵 Employment rate | 61.4 % | 61.2 % | Monthly |
🇯🇵 Full-time employment | 23.77 M | 23.009 M | Monthly |
🇯🇵 Job Opportunities | 836,938 | 832,062 | Monthly |
🇯🇵 Labor force participation rate | 63.3 % | 63.1 % | Monthly |
🇯🇵 Minimum Wages | 1,002 JPY/Hour | 961 JPY/Hour | Annually |
🇯🇵 Part-time work | 7.693 M | 7.729 M | Monthly |
🇯🇵 Population | 124.3 M | 124.95 M | Annually |
🇯🇵 Productivity | 97.1 points | 103.7 points | Monthly |
🇯🇵 Ratio of Jobs to Applications | 1.24 | 1.26 | Monthly |
🇯🇵 Real Earnings Excluding Bonuses | 1.1 % | -1.3 % | Monthly |
🇯🇵 Real Earnings Including Bonuses | -0.1 % | -0.8 % | Monthly |
🇯🇵 Retirement Age Men | 64 Years | 64 Years | Annually |
🇯🇵 Retirement Age Women | 64 Years | 64 Years | Annually |
🇯🇵 Unemployed Persons | 1.68 M | 1.72 M | Monthly |
🇯🇵 Unemployment Rate | 2.4 % | 2.5 % | Monthly |
🇯🇵 Wage Growth | 2.1 % | 1 % | Monthly |
🇯🇵 Wages | 332,301 JPY/Month | 339,957 JPY/Month | Monthly |
🇯🇵 Wages in Manufacturing | 354,182 JPY/Month | 354,079 JPY/Month | Monthly |
🇯🇵 Youth Unemployment Rate | 4 % | 4.1 % | Monthly |
In Japan, non-scheduled cash earnings refer to wages paid for work conducted beyond regular working hours, including allowances for work performed on days off, during nighttime, early morning hours, and overnight shifts.
Macro pages for other countries in Asia
- 🇨🇳China
- 🇮🇳India
- 🇮🇩Indonesia
- 🇸🇦Saudi Arabia
- 🇸🇬Singapore
- 🇰🇷South Korea
- 🇹🇷Turkey
- 🇦🇫Afghanistan
- 🇦🇲Armenia
- 🇦🇿Azerbaijan
- 🇧🇭Bahrain
- 🇧🇩Bangladesh
- 🇧🇹Bhutan
- 🇧🇳Brunei
- 🇰🇭Cambodia
- 🇹🇱East Timor
- 🇬🇪Georgia
- 🇭🇰Hong Kong
- 🇮🇷Iran
- 🇮🇶Iraq
- 🇮🇱Israel
- 🇯🇴Jordan
- 🇰🇿Kazakhstan
- 🇰🇼Kuwait
- 🇰🇬Kyrgyzstan
- 🇱🇦Laos
- 🇱🇧Lebanon
- 🇲🇴Macau
- 🇲🇾Malaysia
- 🇲🇻Maldives
- 🇲🇳Mongolia
- 🇲🇲Myanmar
- 🇳🇵Nepal
- 🇰🇵North Korea
- 🇴🇲Oman
- 🇵🇰Pakistan
- 🇵🇸Palestine
- 🇵🇭Philippines
- 🇶🇦Qatar
- 🇱🇰Sri Lanka
- 🇸🇾Syria
- 🇹🇼Taiwan
- 🇹🇯Tajikistan
- 🇹🇭Thailand
- 🇹🇲Turkmenistan
- 🇦🇪United Arab Emirates
- 🇺🇿Uzbekistan
- 🇻🇳Vietnam
- 🇾🇪Yemen
What is Overtime Pay Year-over-Year (YoY)?
Overtime Pay YoY (Year over Year) is a critical category within the realm of macroeconomic indicators, providing valuable insights into the trends and fluctuations in compensation for overtime work over a specified period, typically a year. In the context of a dynamic and evolving labor market, understanding the patterns and implications of overtime pay can serve as a window into broader economic health, workforce productivity, employer behaviors, and economic policy impacts. At Eulerpool, we specialize in presenting comprehensive and nuanced macroeconomic data that is crucial for stakeholders ranging from policymakers to financial analysts, economists, and business leaders. This comprehensive description delves into the significance, determinants, and implications of the Overtime Pay YoY metric. The Overtime Pay YoY metric essentially measures the percentage change in the total compensation received by employees for overtime work when compared to the same period in the previous year. This indicator is indispensable for analyzing labor market dynamics, particularly during periods of economic expansion or contraction. An upward trend in Overtime Pay YoY often signals increased demand for labor, suggesting that businesses are operating at or near full capacity. Conversely, a downward trend may indicate reduced demand for labor, possibly due to economic slowdowns or structural changes in the economy. One of the fundamental reasons that Overtime Pay YoY is closely monitored is its relationship with overall employment levels and wage growth. When businesses require employees to work overtime, it typically reflects positive business performance and a robust economic environment. This trend can lead to higher disposable incomes for workers, subsequently boosting consumer spending, which is a critical driver of economic growth. Therefore, an increase in overtime pay can have a multiplier effect on overall economic activity. Moreover, the Overtime Pay YoY metric is a valuable tool for assessing inflationary pressures within the economy. Rising wages, including overtime pay, can lead to increased costs for businesses, which may, in turn, pass these costs onto consumers in the form of higher prices for goods and services. Therefore, significant increases in overtime pay can be a precursor to inflationary trends, requiring vigilant monitoring by economic policymakers. Various factors contribute to changes in overtime pay. Firstly, industry-specific trends play a significant role. For example, sectors such as manufacturing, retail, and healthcare often exhibit distinct patterns in overtime compensation due to their operational demands and peak activity periods. Technological advancements and automation also significantly impact overtime trends; as industries adopt more efficient technologies, the need for human labor, including overtime, may decrease. Labor market conditions, such as the unemployment rate and labor force participation rate, are equally influential. In a tight labor market with low unemployment, businesses may find it challenging to hire additional staff, resorting instead to offering existing employees overtime opportunities. Conversely, in a labor market with higher unemployment rates, there may be less need for overtime as employers can more easily hire additional employees. Government policies, including labor laws and regulations, also have a profound impact on overtime pay. Legislation such as the Fair Labor Standards Act (FLSA) in the United States sets standards for overtime pay eligibility and rates, ensuring workers are compensated fairly for their additional labor. Changes or proposals in such policies can lead to shifts in overtime compensation practices across industries. Furthermore, unionization and collective bargaining agreements are pivotal in determining overtime pay structures. Unionized workplaces often have negotiated terms that include premium overtime rates, impacting the overall trends in overtime pay. Understanding the extent of union influence in various sectors is essential for a nuanced analysis of Overtime Pay YoY. Seasonal factors and cyclical economic trends also play a role. For instance, retailers may see a spike in overtime pay during the holiday season due to increased consumer demand. Similarly, certain industries might experience cyclical overtime patterns aligned with broader economic cycles, such as construction booms correlating with economic upturns. It is also important to consider the impact of extraordinary events, such as economic recessions, pandemics, or geopolitical tensions, on overtime pay trends. The COVID-19 pandemic, for example, had profound impacts on various labor market aspects, including overtime pay. Certain sectors experienced increased demand for overtime due to workforce shortages and heightened operational demands, while others saw significant reductions as businesses scaled back operations or closed temporarily. Analyzing Overtime Pay YoY requires robust data collection and analytical techniques. At Eulerpool, our approach involves aggregating data from multiple reliable sources, ensuring accuracy, and consistency. We utilize advanced algorithms to contextualize and visualize this data, offering clear insights into overtime pay trends and their associated economic impacts. Understanding the implications of Overtime Pay YoY extends beyond mere observation. For policymakers, it provides actionable insights into labor market health, aiding in the formulation of policies that promote economic stability and growth. For businesses, it offers a benchmark to gauge competitive standing and operational efficiency. Financial analysts and investors can leverage this data to make informed decisions, anticipating market movements and investment opportunities. In summation, the Overtime Pay YoY metric is a multifaceted indicator that encapsulates various elements of economic activity, labor dynamics, and policy impacts. Its analysis is crucial for a comprehensive understanding of the macroeconomic environment. At Eulerpool, we are committed to delivering precise and insightful macroeconomic data, and our focus on Overtime Pay YoY is testament to our dedication to empowering stakeholders with the information needed to navigate the complexities of the economic landscape. Whether you are a policymaker, business leader, or financial analyst, our platform offers the depth and breadth of data necessary to make informed, strategic decisions.