Graph and download economic data for Noncyclical Rate of Unemployment (NROU) from Q1 1949 to Q4 2035 about NAIRU, long-term, projection, unemployment, rate, and USA.

Estimates of Okun's coefficient are obtained using new estimates of cyclical GNP and cyclical unemployment rates for the post-war USA. Empirical estimates of the coefficient are near ?0.25, somewhat smaller in magnitude than other recent estimates obtained applying similar econometric techniques to different estimates of cyclical output and unemployment. Tests fail to reject the hypothesis of parameter stability across an hypothesized break between the third and fourth quarters of 1973, suggesting similar relationships between cyclical output and unemployment both before and after the supply shocks of the 1970s.

The seasonally-adjusted national unemployment rate is measured on a monthly basis in the United States. In February 2025, the national unemployment rate was at 4.1 percent. Seasonal adjustment is a statistical method of removing the seasonal component of a time series that is used when analyzing non-seasonal trends. U.S. monthly unemployment rate According to the Bureau of Labor Statistics - the principle fact-finding agency for the U.S. Federal Government in labor economics and statistics - unemployment decreased dramatically between 2010 and 2019. This trend of decreasing unemployment followed after a high in 2010 resulting from the 2008 financial crisis. However, after a smaller financial crisis due to the COVID-19 pandemic, unemployment reached 8.1 percent in 2020. As the economy recovered, the unemployment rate fell to 5.3 in 2021, and fell even further in 2022. Additional statistics from the BLS paint an interesting picture of unemployment in the United States. In November 2023, the states with the highest (seasonally adjusted) unemployment rate were the Nevada and the District of Columbia. Unemployment was the lowest in Maryland, at 1.8 percent. Workers in the agricultural and related industries suffered the highest unemployment rate of any industry at seven percent in December 2023.

United States - Noncyclical Rate of Unemployment was 4.11% in October of 2035, according to the United States Federal Reserve. Historically, United States - Noncyclical Rate of Unemployment reached a record high of 6.24 in April of 1978 and a record low of 4.11 in October of 2035. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Noncyclical Rate of Unemployment - last updated from the United States Federal Reserve on June of 2025.

Kenya’s unemployment rate was 5.43 percent in 2024. This represents a steady decline from the increase after the financial crisis. What is unemployment? The unemployment rate of a country refers to the share of people who want to work but cannot find jobs. This includes workers who have lost jobs and are searching for new ones, workers whose jobs ended due to an economic downturn, and workers for whom there are no jobs because the labor supply in their industry is larger than the number of jobs available. Different statistics suggest which factors contribute to the overall unemployment rate. The Kenyan context The first type, so-called “search unemployment”, is hardest to see in the data. The closest proxy is Kenya’s inflation rate. As workers take new jobs faster, employers are forced to increase wages, leading to higher employment. Jobs lost due to economic downturns, called “cyclical unemployment”, can be seen by decreases in the GDP growth rate, which are not significant in Kenya. Finally, “structural unemployment” refers to workers changing the industry, or even economic sector, in which they are working. In Kenya, more and more workers switch to the services sector. This is often a result of urbanization, but any structural shift in the economy’s composition can lead to this unemployment.

A dominant trend in recent modeling of labor market fluctuations is to treat unemployment inflows as acyclical. This trend has been encouraged by recent influential papers that stress the role of longer unemployment spells, rather than more unemployment spells, in accounting for recessionary unemployment. After reviewing an empirical literature going back several decades, we apply a convenient log change decomposition to Current Population Survey data to characterize rising unemployment in each postwar recession. We conclude that a complete understanding of cyclical unemployment requires an explanation of countercyclical inflow rates, especially for job losers (layoffs), as well as procyclical outflow rates. (JEL E24, E32)

Unemployment rate by age class, year and quarter. Percentage value and cyclical change (seasonally adjusted data) and trend (raw data). Year 2017, Tav. 4 quarterly notes on employment.

The unemployment rate of the United Kingdom was 4.5 percent in March 2025, an increase from the previous month. Before the arrival of the COVID-19 pandemic, the UK had relatively low levels of unemployment, comparable with the mid-1970s. Between January 2000 and the most recent month, unemployment was highest in November 2011 when the unemployment rate hit 8.5 percent. Will unemployment continue to rise in 2025? Although low by historic standards, there has been a noticeable uptick in the UK's unemployment rate, with other labor market indicators also pointing to further loosening. In December 2024, the number of job vacancies in the UK, fell to its lowest level since May 2021, while payrolled employment declined by 47,000 compared with November. Whether this is a continuation of a broader cooling of the labor market since 2022, or a reaction to more recent economic developments, such as upcoming tax rises for employers, remains to be seen. Forecasts made in late 2024 suggest that the unemployment rate will remain relatively stable in 2025, averaging out at 4.1 percent, and falling again to four percent in 2026.
Demographics of the unemployed As of the third quarter of 2024, the unemployment rate for men was slightly higher than that of women, at 4.4 percent, compared to 4.1 percent. During the financial crisis at the end of the 2000s, the unemployment rate for women peaked at a quarterly rate of 7.7 percent, whereas for men, the rate was 9.1 percent. Unemployment is also heavily associated with age, and young people in general are far more vulnerable to unemployment than older age groups. In late 2011, for example, the unemployment rate for those aged between 16 and 24 reached 22.3 percent, compared with 8.2 percent for people aged 25 to 34, while older age groups had even lower peaks during this time.

Unemployment Rate in Germany remained unchanged at 6.30 percent in May. This dataset provides the latest reported value for - Germany Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Australia Cyclical Employment data was reported at 0.624 % in Nov 2024. This records an increase from the previous number of 0.544 % for Oct 2024. Australia Cyclical Employment data is updated monthly, averaging -0.026 % from Mar 1978 (Median) to Nov 2024, with 561 observations. The data reached an all-time high of 3.017 % in Jul 1990 and a record low of -5.338 % in Jul 2020. Australia Cyclical Employment data remains active status in CEIC and is reported by Department of Employment and Workplace Relations. The data is categorized under Global Database’s Australia – Table AU.G004: Leading Indicator of Employment. The series is temporarily suspended from July 2020 until more certainty emerges in the underlying trend in labour market activity over the COVID-19 period.

Review of Economics and Statistics: Forthcoming.

Volatility of employment and output simulations under various policy regimes.

To ensure respondent confidentiality, estimates below a certain threshold are suppressed. For Canada, Quebec, Ontario, Alberta and British Columbia suppression is applied to all data below 1,500. The threshold level for Newfoundland and Labrador, Nova Scotia, New Brunswick, Manitoba and Saskatchewan is 500, while in Prince Edward Island, estimates under 200 are suppressed. For census metropolitan areas (CMAs) and economic regions (ERs), use their respective provincial suppression levels mentioned above. Estimates are based on smaller sample sizes the more detailed the table becomes, which could result in lower data quality. Fluctuations in economic time series are caused by seasonal, cyclical and irregular movements. A seasonally adjusted series is one from which seasonal movements have been eliminated. Seasonal movements are defined as those which are caused by regular annual events such as climate, holidays, vacation periods and cycles related to crops, production and retail sales associated with Christmas and Easter. It should be noted that the seasonally adjusted series contain irregular as well as longer-term cyclical fluctuations. The seasonal adjustment program is a complicated computer program which differentiates between these seasonal, cyclical and irregular movements in a series over a number of years and, on the basis of past movements, estimates appropriate seasonal factors for current data. On an annual basis, the historic series of seasonally adjusted data are revised in light of the most recent information on changes in seasonality. Number of civilian, non-institutionalized persons 15 years of age and over who, during the reference week, were employed or unemployed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, worked for pay or profit, or performed unpaid family work or had a job but were not at work due to own illness or disability, personal or family responsibilities, labour dispute, vacation, or other reason. Those persons on layoff and persons without work but who had a job to start at a definite date in the future are not considered employed. Estimates in thousands, rounded to the nearest hundred. Number of persons who, during the reference week, were without work, had looked for work in the past four weeks, and were available for work. Those persons on layoff or who had a new job to start in four weeks or less are considered unemployed. Estimates in thousands, rounded to the nearest hundred. The unemployment rate is the number of unemployed persons expressed as a percentage of the labour force. The unemployment rate for a particular group (age, gender, marital status, etc.) is the number unemployed in that group expressed as a percentage of the labour force for that group. Estimates are percentages, rounded to the nearest tenth. Industry refers to the general nature of the business carried out by the employer for whom the respondent works (main job only). Industry estimates in this table are based on the 2022 North American Industry Classification System (NAICS). Formerly Management of companies and administrative and other support services"." This combines the North American Industry Classification System (NAICS) codes 11 to 91. This combines the North American Industry Classification System (NAICS) codes 11 to 33. This combines the North American Industry Classification System (NAICS) codes 41 to 91. Unemployed persons who have never worked before, and those unemployed persons who last worked more than 1 year ago. For more information on seasonal adjustment see Seasonally adjusted data - Frequently asked questions." Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 1100 - Farming - not elsewhere classified (nec). When the type of farm activity cannot be distinguished between crop and livestock, (for example: mixed farming). Labour Force Survey (LFS) North American Industry Classification System (NAICS) code exception: add group 2100 - Mining - not elsewhere classified (nec). Whenever the type of mining activity cannot be distinguished. Also referred to as Natural resources. The standard error (SE) of an estimate is an indicator of the variability associated with this estimate, as the estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals and calculate coefficients of variation (CVs). The confidence interval can be built by adding the SE to an estimate in order to determine the upper limit of this interval, and by subtracting the same amount from the estimate to determine the lower limit. The CV can be calculated by dividing the SE by the estimate. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of the standard errors for 12 previous months The standard error (SE) for the month-to-month change is an indicator of the variability associated with the estimate of the change between two consecutive months, because each monthly estimate is based on a sample rather than the entire population. To construct confidence intervals, the SE is added to an estimate in order to determine the upper limit of this interval, and then subtracted from the estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate approximately 68% of the time, and within two standard errors approximately 95% of the time. For example, if the estimated employment level increases by 20,000 from one month to another and the associated SE is 29,000, the true value of the employment change has a 68% chance of falling between -9,000 and +49,000. Because such a confidence interval includes zero, the 20,000 change would not be considered statistically significant. However, if the increase is 30,000, the confidence interval would be +1,000 to +59,000, and the 30,000 increase would be considered statistically significant. (Note that 30,000 is above the SE of 29,000, and that the confidence interval does not include zero.) Similarly, if the estimated employment declines by 30,000, then the true value of the decline would fall between -59,000 and -1,000. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months. They are updated twice a year The standard error (SE) for the year-over-year change is an indicator of the variability associated with the estimate of the change between a given month in a given year and the same month of the previous year, because each month's estimate is based on a sample rather than the entire population. The SE can be used to construct confidence intervals: it can be added to an estimate in order to determine the upper limit of this interval, and then subtracted from the same estimate to determine the lower limit. Using this method, the true value will fall within one SE of the estimate, approximately 68% of the time, and within two standard errors, approximately 95% of the time. For example, if the estimated employment level increases by 160,000 over 12 months and the associated SE is 55,000, the true value of the change in employment has approximately a 68% chance of falling between +105,000 and +215,000. This change would be considered statistically significant at the 68% level as the confidence interval excludes zero. However, if the increase is 40,000, the interval would be -15,000 to +95,000, and this increase would not be considered statistically significant since the interval includes zero. See Section 7 of the Guide to the Labour Force Survey (opens new window) for more information. The standard errors presented in this table are the average of standard errors for 12 previous months and are updated twice a year Excluding the territories. Starting in 2006, enhancements to the Labour Force Survey data processing system may have introduced a level shift in some estimates, particularly for less common labour force characteristics. Use caution when comparing estimates before and after 2006. For more information, contact statcan.labour-travail.statcan@statcan.gc.ca

The labor market by itself can create cyclical outcomes, even in the absence of exogenous shocks. We propose a theory in which the search behavior of the employed has profound aggregate implications for the unemployed. There is a strategic complementarity between active on-the-job search and vacancy posting by firms, which leads to multiple equilibria: in the presence of sorting, active on-the-job search improves the quality of the pool of searchers. This encourages vacancy posting, which in turn makes costly on-the-job search more attractive—a self-fulfilling equilibrium. The model provides a rationale for the Jobless Recovery, the outward shift of the Beveridge curve during the boom and for pro-cyclical frictional wage dispersion. Central to the model's mechanism is the fact that the employed crowd out the unemployed when on-the-job search picks up during recovery. We also illustrate this mechanism in a stylized calibration exercise.

This paper argues that the textbook search and matching model cannot generate the observed business-cycle-frequency fluctuations in unemployment and job vacancies in response to shocks of a plausible magnitude. In the United States, the standard deviation of the vacancy-unemployment ratio is almost 20 times as large as the standard deviation of average labor productivity, while the search model predicts that the two variables should have nearly the same volatility. A shock that changes average labor productivity primarily alters the present value of wages, generating only a small movement along a downward-sloping Beveridge curve (unemployment-vacancy locus). A shock to the separation rate generates a counterfactually positive correlation between unemployment and vacancies. In both cases, the model exhibits virtually no propagation.

The misery index (the unweighted sum of unemployment and inflation rates) was probably the first attempt to develop a single statistic to measure the level of a population’s economic malaise. In this letter, we develop a dynamic approach to decompose the misery index using two basic relations of modern macroeconomics: the expectations-augmented Phillips curve and Okun’s law. Our reformulation of the misery index is closer in spirit to Okun’s idea. However, we are able to offer an improved version of the index, mainly based on output and unemployment. Specifically, this new Okun’s index measures the level of economic discomfort as a function of three key factors: (1) the misery index in the previous period; (2) the output gap in growth rate terms; and (3) cyclical unemployment. This dynamic approach differs substantially from the standard one utilised to develop the misery index, and allow us to obtain an index with five main interesting features: (1) it focuses on output, unemployment and inflation; (2) it considers only objective variables; (3) it allows a distinction between short-run and long-run phenomena; (4) it places more importance on output and unemployment rather than inflation; and (5) it weights recessions more than expansions.

Since the oil price shock in 1974 unemployment increased significantly and also did not really decline in periods of economic upswings in Europe. This is especially the case for the countries of the European Union; therefore we face a special need for explanation. Looking at the member states on finds considerable differences. Since 1977 the unemployment rate within the EU is higher than the average unemployment rate of all OECD countries. The economic upswing in the second half of the 80s relaxed the labor market but nevertheless the unemployment rate remained on a high level. This study deals with the development of unemployment between 1974 and 1993 in four different G7 countries: Germany, France, Great Britain and Italy.

Besides the common trend of an increasing unemployment rate, there are significantly different developments within the four countries. The analysis is divided in two parts: the first part looks at the reasons for the increase in unemployment in the considered countries; the second part aims to explain the difference between the developments of unemployment during economic cycles in the different countries.

After the description of similarities and differences of labor markets in the four countries it follows a long term analysis based on annual data as well as a short and medium term analysis on quarterly data. This is due to the fact that short and medium term developments are mainly influenced by cyclical economic developments but long term developments are mainly influenced by other factors like demographical and structural changes. A concrete question within this framework is if an increase in production potential can contribute to a decrease in unemployment.

For the long term analysis among others the Hysteresis-hypothesis (Hysteresis = Greek: to remain; denotes the remaining effect; in this context: remaining of unemployment) used for the explanation of the persistence of a high unemployment rate.

According to this approach consisting unemployment is barely decreased after economic recovery despite full utilization of capacity. According to the Hysteresis-hypothesis there are two reasons for this. The first reason is that for long term unemployed the abilities to work and the qualification level decreased, their human capital is partly devalued. The second reason is that employees give up wage restraint, because they do not fear unemployment anymore and therefore enforce higher real wages. Besides economic recovery companies are not willing to hire long term unemployed with a lower expected productivity for the higher established tariff wages. In the context of the empirical investigation a multiple explanatory approach is chosen which takes supply side and demand side factors into consideration.

The short and medium term analysis refers to Okun´s law (=an increase in the unemployment rate is connected with a decrease of the GDP; if the unemployment rate stays unchanged, the GDP grows with 3% p.a.) and aims to analyze more detailed the reactions of unemployment to economic cycles. A geometrical lag-model is compared with a lag-model ager Almon. This should ensure a precise as possible analysis of the Okun´s relations and coefficients.

Register of tables in HISTAT:

A.: Unemployment in the European G7 countries B.: Analysis of unemployment in the Federal Republic of Germany C.: Basic numbers: International comparison

A.: Unemployment in the European G7 countries A.1. Determinates of unemployment in the EU, Germany (1974-1993) A.2. Determinates of unemployment in the EU, France (1974-1993) A.3. Determinates of unemployment in the EU, Great Britain (1974-1993) A.4. Determinates of unemployment in the EU, Italy (1974-1993)

B: Analysis of unemployment in the Federal Republic of Germany B.1. Growth of unemployment in the Federal Republic of Germany (1984-1991) B.2. Output and unemployment in the Federal Republic of Germany (1961-1990)

C: Basic numbers: International comparison C.1. Unemployment in EU countries, the USA, Japan and Switzerland (1960-1996) C.2. Gainful employments in EU countries, the USA, Japan and Switzerland (after inland and residency concept) (1960-1996) C.3. Employees in EU countries, the USA and Japan (1960-1996) C.4. Population in EU countries, the USA and Japan (1960-1996)

In his article with the provocative title ‘‘Are Recessions Good for Your Health?’’, Ruhm (J. Health Econ. 21(4) (2000) 659) has found robust and consistent evidence that the total mortality rate, age-specific mortality rates as well as most specific mortality causes are pro-cyclical. His finding that high unemployment rates are associated with lower mortality and vice versa stands in stark contrast to Brenner’s earlier work, who found the opposite effect, possibly after a time lag. Ruhm controls for state-specific effects in a panel of US states over the period 1972–1991, whereas Brenner’s work is based on time-series analysis. Extending and improving upon Ruhm’s original analysis, we analyse the effect of state unemployment and economic growth rates on mortality in the states of Germany over the period 1980–2000, both in a static and a dynamic econometric model. Controlling for state-specific effects, we find evidence that aggregate mortality rates for all age groups taken together as well as most specific age groups are lower in recessions. The same is true for mortality from cardiovascular diseases, pneumonia and influenza, motor vehicle accidents and suicides, but not for necessarily for other specific mortality causes. In particular, there is never a statistically significant effect on homicides, other external effects and malignant neoplasms. There are also few differences apparent between the effect on male and female mortality. If we do not control for state-specific effects, then we often arrive at the opposite result with higher unemployment being associated with higher mortality. This suggests that a failure to control for time-invariant state-specific effects leads to omitted variable bias, which would erroneously suggest that mortality rates move counter-cyclically. Overall, we can confirm Ruhm’s main finding for another country: recessions lower some, but not all, mortality rates in the case of Germany.

This paper studies the degree to which observable and unobservable worker characteristics account for the variation in the aggregate duration of unemployment. I model the distribution of unobserved worker heterogeneity as time varying to capture the interaction of latent attributes with changes in labor-market conditions. Unobserved heterogeneity is the main explanation for the duration dependence of unemployment hazards. Both cyclical and low-frequency variations in the mean duration of unemployment are mainly driven by one subgroup: workers who, for unobserved reasons, stay unemployed for a long time. In contrast, changes in the composition of observable characteristics of workers have negligible effects.

On the basis of aggregate data for the early 1990s, we analyse the determinants of unemployment duration for laid-off male workers in Wallonia (Belgium). Our results indicate that if ranking in recruitment occurs, the standard mixed proportional hazard specification can be too restrictive, leading to an overstatement of the extent of true negative duration dependence. We conclude that negative duration dependence is largely spurious. We also decompose the time variation of the hazard in (unobserved) compositional and direct cyclical and seasonal effects. We find counter-cyclical variation in the quality of young workers, but none for the prime aged.