The employment and unemployment indicator shows several data points. The first figure is the number of people in the labor force, which includes the number of people who are either working or looking for work. The second two figures, the number of people who are employed and the number of people who are unemployed, are the two subcategories of the labor force. The unemployment rate is a calculation of the number of people who are in the labor force and unemployed as a percentage of the total number of people in the labor force.

The unemployment rate does not include people who are not employed and not in the labor force. This includes adults who are neither working nor looking for work. For example, full-time students may choose not to seek any employment during their college career, and are thus not considered in the unemployment rate. Stay-at-home parents and other caregivers are also considered outside of the labor force, and therefore outside the scope of the unemployment rate.

The unemployment rate is a key economic indicator, and is illustrative of economic conditions in the county at the individual scale.

There are additional considerations to the unemployment rate. Because it does not count those who are outside the labor force, it can exclude individuals who were looking for a job previously, but have since given up. The impact of this on the overall unemployment rate is difficult to quantify, but it is important to note because it shows that no statistic is perfect.

The unemployment rates for Champaign County, the City of Champaign, and the City of Urbana are extremely similar between 2000 and 2023.

All three areas saw a dramatic increase in the unemployment rate between 2006 and 2009. The unemployment rates for all three areas decreased overall between 2010 and 2019. However, the unemployment rate in all three areas rose sharply in 2020 due to the effects of the COVID-19 pandemic. The unemployment rate in all three areas dropped again in 2021 as pandemic restrictions were removed, and were almost back to 2019 rates in 2022. However, the unemployment rate in all three areas rose slightly from 2022 to 2023.

This data is sourced from the Illinois Department of Employment Security’s Local Area Unemployment Statistics (LAUS), and from the U.S. Bureau of Labor Statistics.

Sources: Illinois Department of Employment Security, Local Area Unemployment Statistics (LAUS); U.S. Bureau of Labor Statistics.

The statistic reflects the seasonally adjusted unemployment rate in member states of the European Union in November 2024. The seasonally adjusted unemployment rate in Spain in November 2024 was 11.2 percent.The unemployment rate represents the share of the unemployed in all potential employees available to the job market. Unemployment rates in the EU The unemployment rate is an important measure of a country or region’s economic health, and despite unemployment levels in the European Union falling slightly from a peak in early 2013 , they remain high, especially in comparison to what the rates were before the worldwide recession started in 2008. This confirms the continuing stagnation in European markets, which hits young job seekers particularly hard as they struggle to compete against older, more experienced workers for a job, suffering under jobless rates twice as high as general unemployment. Some companies, such as Microsoft and Fujitsu, have created thousands of jobs in some of the countries which have particularly dire unemployment rates, creating a beacon of hope. However, some industries such as information technology, face the conundrum of a deficit of qualified workers in the local unemployed work force, and have to hire workers from abroad instead of helping decrease the local unemployment rates. This skills mismatch has no quick solution, as workers require time for retraining to fill the openings in the growing science-, technology-, or engineering-based jobs, and too few students choose degrees that would help them obtain these positions. Worldwide unemployment also remains high, with the rates being worst in the Middle East and North Africa. Estimates by the International Labour Organization predict that the problem will stabilize in coming years, but not improve until at least 2017.

In 2024, the unemployment rate in Russia was measured at approximately 2.53 percent, down from 3.08 percent in the previous year. That was the lowest figure over the observed period.Unemployment An unemployed person is defined as someone who is out of work and usually looking for work actively. Unemployment in a country is measured using the unemployment rate, which is an index calculated by dividing the number of workers out of work by the total workforce of a country, and then multiplying that figure by 100. The labor force is made up of the people who are old enough and physically fit enough to work. The unemployment rate is an important economic factor, but economists do not always agree on what exactly causes unemployment. They do agree, however, that unemployment typically rises during bad times for the economy, i.e., recessions. As for other important economic factors, Russia’s inflation rate has been decreasing for a few years now, while its real (inflation-adjusted) gross domestic product is still recovering from a steep decline in 2009. As most other economies, Russia’s economy focuses mostly on the Services and Industry sector, while the Agriculture sector plays little to no role when it comes to gross domestic product generation. Consequently, the vast majority of the labor force works in those two sectors. However, Russia is the leading wheat exporter worldwide, followed by the United States and Canada.

Estimates of the natural rate of unemployment are important in many macroeconomic models used by economists and policy advisors. This paper shows how such estimates might benefit from closer attention to regional developments. Regional business cycles do not move in lock-step, and greater dispersion among regions can affect estimates of the natural rate of unemployment. There is microeconomic evidence that employers are more reluctant to cut wages than they are to raise them. Accordingly, the relationship between wage inflation and vacancies is convex: An increase in vacancies raises wage inflation at an increasing rate. The authors' empirical results are consistent with this and indicate that if all else had remained constant, the reduction in the dispersion of regional unemployment rates between 1982 and 2000 would have meant a two-percentage-point drop in the natural rate of aggregate unemployment.

The Texas Workforce Commission provides Texas Labor Market Information with counts for the civilian labor force, employment, unemployment, and unemployment rate estimates by place of residence. According to the U.S. Bureau of Labor Statistics, the definition of unemployed is to be "jobless, actively seeking work, and available to take a job." The unemployment rate is an important indicator of economic and workforce health in Austin over time. Unemployment Rate for the City of Austin = Number of Unemployed / Civilian Labor Force

Unemployment Rate in Vietnam decreased to 2.20 percent in the first quarter of 2025 from 2.22 percent in the fourth quarter of 2024. This dataset provides the latest reported value for - Vietnam Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Important Note: This item is in mature support as of June 2023 and will be retired in December 2025. This map shows the unemployment rate in the United States in 2022 in a multiscale map by country, state, county, ZIP Code, tract, and block group.The pop-up is configured to include the following information for each geography level:Unemployment rate (%)Population count of persons over the age of 16 within work forceCount of employed and unemployed civil population (over age 16)Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This paper analyses the risk of unemployment, unemployment duration, and the risk of longterm unemployment immediately after apprenticeship graduation. Unemployed apprenticeship graduates constitute a large share of unemployed youth in Germany but unemployment incidence within this group is unequally distributed. Our paper extends previous research in three dimensions. It shows that (i) individual productivity assessment of the training firm, (ii) initial selection into high reputation training firms and occupations, and (iii) adverse selection of employer moving graduates are correlated with unemployment after apprenticeship graduation. The empirical evidence is obtained from the second longitudinal version of the linked employer-employee panel data from the IAB (LIAB). This large data set allows us to calculate the exact unemployment spell length of apprenticeship graduates. In addition, we can include individual, employer, occupation as well as industrial relation characteristics before and after apprenticeship graduation into our list of explanatory variables for unemployment risk. We show in several robustness checks that our results are remarkably stable when we vary the employees included in the sample, the definition of unemployment, and the list of explanatory variables.

Economic forecasters pay especially close attention to labor market indicators during periods of economic uncertainty. Labor market data are thought to provide early evidence about changes in the course of the economy. This article examines whether monthly changes in labor market indicators are useful for predicting real GDP. It then examines whether weekly changes in initial and continuing unemployment insurance claims are useful for helping to predict changes in important labor market indicators. Incoming monthly data on nonfarm payroll jobs and the index of aggregate weekly hours help predict changes in real GDP growth, but data on the civilian unemployment rate do not. The authors also find that unemployment insurance claims help to predict changes in monthly labor variables. As others have found, these predictions work best in periods of recession. However, this article shows that there was also some predictive ability during the 1990s expansion.

Unemployment Rate in Tunisia remained unchanged at 15.70 percent in the first quarter of 2025 from 15.70 percent in the fourth quarter of 2024. This dataset provides - Tunisia Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The ratio of the population above 15 years old, the labor force, and the employed to the total population and the unemployment rate by region for the human resources survey.

Data of the US Employment and Unemployment rates since 1940. The data is obtained from the USA Bureau of Labor Statistics and includes the employment status of the civilian noninstitutional population from 1940 to the present day. The numbers in the dataset are measured in thousands and provide important information on the labor market in the US over several decades. This dataset can be used by researchers, policymakers, and analysts to understand the trends and fluctuations in the US labor market, as well as to develop strategies for improving employment and reducing unemployment rates.

This statistic shows the unemployment rate in Greece from 1999 to 2024. In 2024, the unemployment rate in Greece was around 10.13 percent. Today, Greece reports the highest unemployment rate of all EU states. Greece's financial situation Greece is a developed country with a high-income economy, whose primary industry revolves around tourism and shipping. Agriculture also plays an important role for the country’s economy, more specifically for the EU. Greece had experienced large amounts of economic growth from the 1950s to the 1970s, however was economically devastated by the Great Recession in 2009 as well its own government debt crisis. Since the early 2000s, small increases in national debt were present within the Greek economy. These small increases turned into rather substantial surges between 2008 and 2011, which resulted in a large amount of accumulated public debt. However, financial assistance from several countries around the world as well as stimulus packages from the EU were issued to Greece, with the hopes of structural adjustments in the government and better decision making within the country in order to decrease national debt and increase productivity. The financial assistance helped stabilize Greece’s debt over the past several years, however many countries are arguing just how useful this support is, mostly because Greece has not made significant strides to improve its economy. As a result, consumers have become less optimistic about the possibility of a short term economic recovery in Greece. Additionally, investors have remained hesitant on investing into the country, generally due to an increasing debt-to-GDP ratio, which is ranked atop all countries in the European Union. The so-called debt-to-GDP ratio is an important indicator of a country’s ability to pay back its debts without incurring further debt.

Unemployment Rate in India decreased to 7.90 percent in February from 8.20 percent in January of 2025. This dataset provides - India Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Unemployment Rate in South Africa increased to 32.90 percent in the first quarter of 2025 from 31.90 percent in the fourth quarter of 2024. This dataset provides - South Africa Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

We investigate the macroeconomic consequences of fluctuations in the effectiveness of the labor market matching process with a focus on the Great Recession. We conduct our analysis in the context of an estimated medium-scale dynamic stochastic general equilibrium model with sticky prices and equilibrium search unemployment that features a shock to the matching efficiency (or mismatch shock). We find that this shock is not important for unemployment fluctuations in normal times. However, it plays a somewhat larger role during the Great Recession when it contributes to raise the actual unemployment rate by around 1.3 percentage points and the natural rate by around 2 percentage points. The mismatch shock is the dominant driver of the natural rate of unemployment and explains part of the recent shift of the Beveridge curve.

****Dataset Overview**** This dataset contains historical macroeconomic data, featuring key economic indicators in the United States. It includes important metrics such as the Consumer Price Index (CPI), Retail Sales, Unemployment Rate, Industrial Production, Money Supply (M2), and more. The dataset spans from 1993 to the present and includes monthly data on various economic indicators, processed to show their rate of change (either percentage or absolute difference, depending on the indicator).

provenance

The data in this dataset is sourced from the Federal Reserve Economic Data (FRED) database, hosted by the Federal Reserve Bank of St. Louis. FRED provides access to a wide range of economic data, including key macroeconomic indicators for the United States. My work involved calculating the rate of change (ROC) for each indicator and reorganizing the data into a more usable format for analysis. For more information and access to the full database, visit FRED's website.

Purpose and Use for the Kaggle Community:

This dataset is a valuable resource for data scientists, economists, and analysts interested in understanding macroeconomic trends, performing time series analysis, or building predictive models. With the rate of change included, users can quickly assess the growth or contraction in these indicators month-over-month. This dataset can be used for:

• Exploratory Data Analysis (EDA): Understanding historical economic trends. -Time Series Forecasting: Building models to predict future economic conditions. -Macroeconomic Analysis: Analyzing the relationship between various economic indicators. -Machine Learning Projects: Using the data as features to predict financial or economic outcomes. -By utilizing this dataset, users can perform in-depth analysis on the impact of macroeconomic changes, compare the historical performance of various indicators, and experiment with different time series forecasting techniques.

****Column Descriptions****

Year: The year of the observation.

Month: The month of the observation (1-12).

Industrial Production: Monthly data on the total output of US factories, mines, and utilities.

Manufacturers' New Orders: Durable Goods: Measures the value of new orders placed with manufacturers for durable goods, indicating future production activity.

Consumer Price Index (CPIAUCSL): A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.

Unemployment Rate: The percentage of the total labor force that is unemployed but actively seeking employment.

Retail Sales: The total receipts of retail stores, indicating consumer spending and economic activity.

Producer Price Index: Measures the average change over time in the selling prices received by domestic producers for their output.

Personal Consumption Expenditures (PCE): A measure of the prices paid by consumers for goods and services, used in calculating inflation.

National Home Price Index: A measure of changes in residential real estate prices across the country.

All Employees, Total Nonfarm: The number of nonfarm payroll employees, an important indicator of the labor market.

Labor Force Participation Rate: The percentage of the working-age population that is either employed or actively looking for work.

Federal Funds Effective Rate: The interest rate at which depository institutions lend reserve balances to other depository institutions overnight.

Building Permits: The number of building permits issued for residential and non-residential buildings, a leading indicator of construction activity.

Money Supply (M2): The total money supply, including cash, checking deposits, and easily convertible near money.

Personal Income: The total income received by individuals from all sources, including wages, investments, and government transfers.

Trade Balance: The difference between a country's imports and exports, indicating the net trade flow.

Consumer Sentiment: The index reflecting consumer sentiment and expectations for the future economic outlook.

Consumer Confidence: A measure of how optimistic or pessimistic consumers are regarding their expected financial situation and the economy.

Notes on Interest Rates Please note that for the Federal Funds Effective Rate (FEDFUNDS), the dataset includes the absolute change in basis points (bps), not the rate of change. This means that the dataset reflects the direct change in the interest rate rather than the percentage change month-over-month. The change is represented in basis points, where 1 basis point equals 0.01%.

Enquête naar de arbeidskrachten (EAK) Doel en korte beschrijving De steekproefenquête naar de arbeidskrachten is een enquête bij particuliere huishoudens, die gedurende het hele jaar wordt gehouden. Ze is gebaseerd op de antwoorden van ongeveer 110.000 personen (respondenten) van 15-89 jaar. Haar voornaamste doelstelling is de populatie van 15-89 jaar op te delen in drie groepen (nl. werkende personen, werklozen en niet-beroepsactieve personen), en over elk van deze categorieën beschrijvende en verklarende gegevens te verstrekken. Deze enquête wordt ook in de andere EU-lidstaten uitgevoerd en wordt gecoördineerd door de statistische dienst van de Europese Unie, EUROSTAT. In België wordt de EAK georganiseerd door Statbel (Algemene Directie Statistiek - Statistics Belgium). De bedoeling is informatie te vergaren die op Europees vlak vergelijkbaar is, o.m. inzake werkgelegenheids- en werkloosheidscijfers overeenkomstig de definities van het Internationaal Arbeidsbureau (IAB), en daarnaast gegevens te verzamelen en te verspreiden die elders niet verkregen kunnen worden. Voorbeelden hiervan zijn mobiliteit van de werknemers, motivatie voor deeltijds werken, de verschillende vormen van tijdelijke arbeid, beroep, onderwijsniveau van de bevolking op beroepsactieve leeftijd,… Populatie Leden van privé-huishoudens van 15-89 jaar Basis van de steekproef Demografische gegevens van het Rijksregister Dataverzamelingsmethode en eventuele steekproefomvang De informatie wordt voor de eerste bevraging verzameld via face to face interviews. Sinds 2017 volgen daarna nog drie kortere opvolgbevragingen die via het web of telefonisch gebeuren. Gezinnen die uitsluitend bestaan uit niet-beroepsactieve personen ouder dan 64 jaar mogen ook telefonisch worden bevraagd. Jaarlijks nemen in België ongeveer 34.000 unieke huishoudens deel aan deze enquête. Respons Gemiddeld bedraagt de respons in de eerste bevraging 68% en in de opvolgbevragingen tussen de 90% en 95%. Frequentie Driemaandelijks. Timing publicatie Resultaten beschikbaar +/- 3 maanden na de referentieperiode Formulieren Enquête naar de arbeidskrachten 2024 (PDF, 1 Mb) Definities De enquête is geharmoniseerd op Europees niveau. De definities over werkgelegenheid en werkloosheid die worden gehanteerd zijn die van het Internationaal Arbeidsbureau (IAB), waardoor een vergelijkbaarheid van de resultaten op internationaal vlak wordt gewaarborgd. Personen met een job (werkende personen) zijn personen die gedurende de referentieweek arbeid verrichtten ‘tegen betaling’ of met als doel ‘winst te maken’ ongeacht de duur (ook al was dit maar één uur), of die een job hadden maar tijdelijk afwezig waren. Men kan bijvoorbeeld tijdelijk afwezig zijn omwille van vakantie, ziekte, technische of economische redenen (tijdelijke werkloosheid),…. Ook de meewerkende familieleden worden tot de werkenden gerekend. Sinds 2021 worden personen die een ononderbroken periode van langer dan drie maanden tijdelijke werkloos zijn bij de werklozen of niet-beroepsactieven gerekend en niet meer bij de werkenden. Werklozen zijn alle personen die: (a) tijdens de referentieweek geen werk hadden, d.w.z. niet in loondienst of als zelfstandige werkten; (b) voor werk beschikbaar waren, d.w.z. voor werk in loondienst of als zelfstandige beschikbaar waren binnen twee weken na de referentieweek; (c) actief werk zochten, d.w.z. gedurende de laatste vier weken met inbegrip van de referentieweek gerichte stappen hadden ondernomen om werk in loondienst of als zelfstandige te zoeken, of die werk hadden gevonden en binnen ten hoogste drie maanden zouden beginnen te werken. Opgelet! De IAB‐werkloosheidscijfers staan los van een eventuele inschrijving bij VDAB, Actiris, FOREM of ADG, evenals van het ontvangen van een uitkering van de RVA, en zijn dus niet vergelijkbaar met de administratieve werkloosheidscijfers. De beroepsbevolking is samengesteld uit de werkloze en de werkende bevolking. Niet‐beroepsactieven zijn alle personen die niet beschouwd worden als personen met een betrekking of als werklozen. De werkgelegenheidsgraad geeft het percentage werkende personen in een bepaalde leeftijdsgroep weer. De werkgelegenheidsgraad in het kader van de Europa 2020‐strategie geeft het percentage werkende personen in de bevolking van 20 tot 64 jaar weer. De werkloosheidsgraad geeft het percentage werklozen in de beroepsbevolking (werkende personen + werklozen) binnen een bepaalde leeftijdsgroep weer. De activiteitsgraad geeft het percentage beroepsbevolking (werkende personen + werklozen) in de totale bevolking binnen een bepaalde leeftijdsgroep weer. Bovenstaande indicatoren (werkgelegenheidsgraad, werkloosheidsgraad en activiteitsgraad) zijn de belangrijkste indicatoren om de arbeidsmarktevolutie op internationaal niveau te vergelijken. Laaggeschoolden zijn die personen die maximaal een diploma hebben van het lager secundair onderwijs. Middengeschoolden zijn personen die een diploma behaald hebben van het hoger secundair onderwijs, maar geen diploma van het hoger onderwijs. Hooggeschoolden hebben een diploma van het hoger onderwijs. Metadata Werkgelegenheid, werkloosheid, arbeidsmarkt.pdf Enquête naar de arbeidskrachten (EAK).pdf Methodologie enquêtes Wijzigingen in de Enquête naar de arbeidskrachten (EAK) in 2021 EAK: De methodologische verbeteringen in de Enquête naar de Arbeidskrachten 2017 (PDF, 98 Kb) EAK: voorstelling van de enquête vanaf 2017 (PDF, 105.77 Kb) EAK: voorstelling van de enquête tot 2016 (PDF, 98.44 Kb) Nota naar aanleiding van publicatie gegevens T4 2024 & jaarresultaten 2024 (pdf) Wetgeving Koninklijk besluit 10 JANUARI 1999 betreffende een steekproefenquête naar de arbeidskrachten (PDF, 17.26 Kb) Koninklijk besluit tot wijziging van het koninklijk besluit van 10 januari 1999 betreffende een steekproefenquête naar de arbeidskrachten (PDF, 17.48 Kb)

Employment is a key indicator of economic health. The unemployment rate provides a view to the state of Tempe’s economy. The regional economy that Tempe participates in is the most important factor in overall employment in the region and in Tempe.This page provides information for the Unemployment Rate performance measure.DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the ArcGIS Hub application. To make changes to this page, please visit https://tempegov.hub.arcgis.com:/overview/edit.

Analysis of ‘Unemployment and mental illness survey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/michaelacorley/unemployment-and-mental-illness-survey on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This is a paid research survey to explore the linkage between mental illness and unemployment. NAMI has conducted multiple surveys verifying the high unemployment rate among those with mental illness, but this is the only survey to date which targets causation (why they are unemployed). Statistical significance of the variance has long since been proven by previous, larger samples.

You are free to visualize and publish results, please just credit me by name.

Collection methodology

I received several messages about methodology of collection because various people would like to use this data for papers.

• I paid respondents on Survey Monkey in a general population sampling. I did not target any specific demographic as not to get skewed results. Survey Monkey stratifies the sample according to certain characteristics like income and location.

• I know that the general population sampling went well because the number of people self identifying as having a mental illness is consistent with larger samples.

• Although we disqualified people without a mental illness, they were still given the complete survey. That means that the data contains sampling of people with and without mental illness and a yes/no indicator.

Potential area's to investigate

• Linkage between unemployment and education level
• The effect of a gap on your resume on income level
• Symptom/side effects impact on employment
• The effectiveness of social welfare programs
• The linkage between gaps in your resume and hospitalizations due to mental illness

Content

***Sample size:** n = 334; 80 w/ mental illness - this proportion is approximately equal to estimates of the general population diagnosed with mental illness (typically estimated at 20-25% according to various studies).*

Questions:

I identify as having a mental illness Response
Education  Response
I have my own computer separate from a smart phone Response
I have been hospitalized before for my mental illness  Response
How many days were you hospitalized for your mental illness Open-Ended Response
I am currently employed at least part-time Response
I am legally disabled  Response
I have my regular access to the internet  Response
I live with my parents Response
I have a gap in my resume  Response
Total length of any gaps in my resume in months.  Open-Ended Response
Annual income (including any social welfare programs) in USD  Open-Ended Response
I am unemployed Response
I read outside of work and school  Response
Annual income from social welfare programs Open-Ended Response
I receive food stamps  Response
I am on section 8 housing  Response
How many times were you hospitalized for your mental illness  Open-Ended Response

I have one of the following issues in addition to my illness:
  Lack of concentration
  Anxiety
  Depression
  Obsessive thinking
  Mood swings
  Panic attacks
  Compulsive behavior
  Tiredness

Age Response
Gender Response
Household Income  Response
Region Response
Device Type Response

Important data transformation note

When comparing the actual rate to government statistics, it is important to take into account the labor force participation rate (the % of people who are legally considered to be in the workforce). People not included in the unemployment statistic, like discouraged workers (for example the mentally ill) will be "not participating" in the workforce.

Other studies

1. Nami: https://www.nami.org/Press-Media/Press-Releases/2014/Mental-Illness-NAMI-Report-Deplores-80-Percent-Une

2. NIH: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4182106/

--- Original source retains full ownership of the source dataset ---