In March 2024, the youth unemployment rate in Italy was 22.8 percent. The problem of unemployment in Italy became critical in the first years of the financial crisis, which started in 2008. Although the labor market crisis seriously affected the entire Italian working population, it particularly impacted the youngest part of the labor force. Between 2008 and 2014, the share of unemployed individuals aged between 15 and 24 years increased by more than 15 percentage points. Despite a steady decline observed after 2014, youth unemployment still stood at almost 30 percent as of 2020. The effects of the 2011-2012 financial crisis: dream job versus harsh reality Newly graduated and often looking for a first job, young people are particularly vulnerable to stagnation in the labor market. Considering the difficulties in finding a job during and after the years of the financial crisis, about 48 percent of young Italians declared in 2018 that they would accept a job that does not meet their career aspiration. One fourth of the respondents stated that they would accept a monthly salary of 500 euros. Youth unemployment rate in the EU: a serious challenge for Spain and ItalyItaly was the country with the fifth-highest youth unemployment rate among the EU member states in August 2023. The country with the highest youth unemployment was Spain, where more than one out of four individuals were unemployed.

In 1990, the unemployment rate of the United States stood at 5.6 percent. Since then there have been many significant fluctuations to this number - the 2008 financial crisis left millions of people without work, as did the COVID-19 pandemic. By the end of 2022 and throughout 2023, the unemployment rate came to 3.6 percent, the lowest rate seen for decades. However, 2024 saw an increase up to four percent. For monthly updates on unemployment in the United States visit either the monthly national unemployment rate here, or the monthly state unemployment rate here. Both are seasonally adjusted. UnemploymentUnemployment is defined as a situation when an employed person is laid off, fired or quits his work and is still actively looking for a job. Unemployment can be found even in the healthiest economies, and many economists consider an unemployment rate at or below five percent to mean there is 'full employment' within an economy. If former employed persons go back to school or leave the job to take care of children they are no longer part of the active labor force and therefore not counted among the unemployed. Unemployment can also be the effect of events that are not part of the normal dynamics of an economy. Layoffs can be the result of technological progress, for example when robots replace workers in automobile production. Sometimes unemployment is caused by job outsourcing, due to the fact that employers often search for cheap labor around the globe and not only domestically. In 2022, the tech sector in the U.S. experienced significant lay-offs amid growing economic uncertainty. In the fourth quarter of 2022, more than 70,000 workers were laid off, despite low unemployment nationwide. The unemployment rate in the United States varies from state to state. In 2021, California had the highest number of unemployed persons with 1.38 million out of work.

Panama Unemployment: Female: Age 30-39 data was reported at 12,832.000 Person in 2017. This records an increase from the previous number of 12,050.000 Person for 2016. Panama Unemployment: Female: Age 30-39 data is updated yearly, averaging 10,672.500 Person from Aug 2004 (Median) to 2017, with 14 observations. The data reached an all-time high of 21,935.000 Person in 2004 and a record low of 6,978.000 Person in 2012. Panama Unemployment: Female: Age 30-39 data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Panama – Table PA.G007: Unemployment.

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.

Sri Lanka Unemployment: 30-39 yo data was reported at 40,828.000 Person in Dec 2017. This records a decrease from the previous number of 63,489.000 Person for Sep 2017. Sri Lanka Unemployment: 30-39 yo data is updated quarterly, averaging 50,084.000 Person from Mar 2016 (Median) to Dec 2017, with 8 observations. The data reached an all-time high of 63,489.000 Person in Sep 2017 and a record low of 40,828.000 Person in Dec 2017. Sri Lanka Unemployment: 30-39 yo data remains active status in CEIC and is reported by Department of Census and Statistics. The data is categorized under Global Database’s Sri Lanka – Table LK.G014: Labour Force Survey: MYPE 2012: Unemployment.

Abstract

Statistical information on all aspects of the population is vital for the design, implementation, monitoring and evaluation of economic and social development plan and policy issues. Labor force survey is one of the most important sources of data for assessing the role of the population of the country in the economic and social development process. It is useful to indicate the extent of available and unutilized human resources that must be absorbed by the national economy to ensure full employment and economic wellbeing of the population. Statistics on the labor force further present the economic activity status and its relationship to other social and economic characteristics of the population. Seasonal and other variations as well as changes over time in the size, distribution, and characteristics of employed and unemployed population can be monitored using up-to-date information from labor force surveys. It serves as an input for assessing the achievements of the Millennium Development Goals (MDGs). Furthermore, labor force data is also useful as a springboard for monitoring and evaluation of the five years growth and transformation plan of the country.

Geographic coverage

The 2012 Urban Employment and Unemployment Survey (UEUS) covered all urban parts of the country except three zones of Afar, Six zones of Somali, where the residents are pastoralists.

Analysis unit

• Households
• Individuals

Universe

This survey follows household approach and covers households residing in conventional households and thus, population residing in the collective quarters such as universities/colleges, hotel/hostel, monasteries, and homeless population etc., were not covered by this survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The list of households obtained from the 2007 population and housing census was used to select EAs. A fresh list of households from each EA was prepared at the beginning of the survey period. The list was then used as a frame to select 30 households from sample EAs.

The country was divided into two broad categories - major urban centers and other urban center categories.

Category I: In this category all regional capitals and five other major urban centers that have a high population size as compared to others were included. Each urban center in this category was considered as a reporting level. This category has a total of 16 reporting levels. To select the sample, a stratified two-stage cluster sample design was implemented. The primary sampling units were EAs of each reporting level.

Category II: Urban centers other than those under category I were grouped into this category. A stratified three stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire was organized into seven sections. Section 1 - Area identification of the selected household Section 2 - Particulars of household members Section 3 - Economic activity status during the last seven days Section 4 - Unemployment rate and characteristics of unemployed persons Section 5 - Economic activity status the population during the last six months Section 6 - Employment in the informal sector of Employment Section 7 - Economic activity of children aged 5-17 years

A structured questionnaire was used to solicit the required data in the survey. The draft questionnaire was tested by undertaking a pretest in selected kebeles (lower administrative unit) in Addis Ababa. Based on the pretest, the content, logical flow, layout and presentation of the questionnaire was amended. The questionnaire used in the field for data collection was prepared in Amharic language. Most questions have pre coded answers and column numbers were assigned for each question.

Cleaning operations

The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the field supervisors and the heads of branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry by the subject matter experts.

Using the computer edit specifications prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing, or data cleaning was made by referring back to the filled-in questionnaire. This is an important part of data processing operation in attaining the required level of data quality. Consistency checks and re-checks were also made based on frequency and tabulation results. This was done by senior programmers using CSPro software in collaboration with the senior subject experts from Manpower Statistics Team of the CSA.

Response rate

Response rate was 99.68%.

Unemployment rate, participation rate, and employment rate by educational attainment, gender and age group, annual.

Civilian labor force data consists of the number of employed persons, the number of unemployed persons, an unemployment rate and the total count of both employed and unemployed persons (total civilian labor force). Labor force refers to an estimate of the number of persons, 16 years of age and older, classified as employed or unemployed. The civilian labor force, which is presented in these data tables, excludes the Armed Forces, i.e., the civilian labor force equals employed civilians plus the unemployed. Employed persons are those individuals, 16 years of age and older, who did any work at all during the survey week as paid employees, in their own business, profession or farm, or who worked 15 hours or more as unpaid workers in a family operated business. Also counted as employed are those persons who had jobs or businesses from which they were temporarily absent because of illness, bad weather, vacation, labor-management dispute, or personal reasons. Individuals are counted only once even though they may hold more than one job. Unemployed persons comprise all persons who did not work during the survey week but who made specific efforts to find a job within the previous four weeks and were available for work during the survey week (except for temporary illness). Also included as unemployed are those who did not work at all, were available for work, but were not actively seeking work because they were either waiting to be called back to a job from which they were laid off or waiting to report to a new job within 30 days. The unemployment rate represents the number of unemployed persons as a percent of the total civilian labor force.

This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2008 – 2012. The indicators are the percent of occupied housing units with more than one person per room (i.e., crowded housing); the percent of households living below the federal poverty level; the percent of persons in the labor force over the age of 16 years that are unemployed; the percent of persons over the age of 25 years without a high school diploma; the percent of the population under 18 or over 64 years of age (i.e., dependency); and per capita income. Indicators for Chicago as a whole are provided in the final row of the table. See the full dataset description for more information at: https://data.cityofchicago.org/api/views/fwb8-6aw5/files/A5KBlegGR2nWI1jgP6pjJl32CTPwPbkl9KU3FxlZk-A?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\ECONOMIC_INDICATORS\Dataset_Description_socioeconomic_indicators_2012_FOR_PORTAL_ONLY.pdf

Spain Unemployment: Male: 30 to 34 Years data was reported at 158.500 Person th in Jun 2018. This records a decrease from the previous number of 192.400 Person th for Mar 2018. Spain Unemployment: Male: 30 to 34 Years data is updated quarterly, averaging 190.300 Person th from Jun 1987 (Median) to Jun 2018, with 125 observations. The data reached an all-time high of 482.600 Person th in Dec 2012 and a record low of 100.800 Person th in Sep 2005. Spain Unemployment: Male: 30 to 34 Years data remains active status in CEIC and is reported by National Statistics Institute. The data is categorized under Global Database’s Spain – Table ES.G028: Unemployment: Labour Force Survey: by Age Group and Sex.

Unemployment Rate in Brazil decreased to 6.20 percent in May from 6.60 percent in April of 2025. This dataset provides the latest reported value for - Brazil Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In Denmark, the highest unemployment rate in 2022 could be found among youth between 25 and 29 years, who had an unemployment rate of 4.4 percent that year. People between 30 and 34 years had the second highest unemployment rate. On the other hand, the lowest unemployment rate was among people between 50 and 54 years of age. The unemployment rate in Denmark rose sharply in 2020 following the outbreak of COVID-19, but decreased again the following years.

 Receiving unemployment benefits in Denmark 

By law, Danes have the right to receive unemployment benefits. There are requirements to be eligible for unemployment benefits in Denmark: being a member of an unemployment insurance fund called “A-kasse” is necessary as well as having earned a specific amount of money in the past. In addition, being registered at the Public Employment Service is required and as of January 2019, only people who have stayed in Denmark, Greenland, Faroe Islands or another EU/EEA country in seven out of the last eight years can claim unemployment benefits.

Labor market

Since 2012, the number of employed Danes was growing each year, but experienced a setback in 2020 due to COVID-19. The Danish labor market is known for the Flexicurity Model – a combination of market economy and a welfare state, and due to this model, the labor market can reflect the needs of employers and guarantee the welfare of employees which creates many possibilities for both sides.

Korea Unemployment: Year Avg: 30 to 34 Years Old data was reported at 111.000 Person th in 2017. This records an increase from the previous number of 108.000 Person th for 2016. Korea Unemployment: Year Avg: 30 to 34 Years Old data is updated yearly, averaging 110.000 Person th from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 123.000 Person th in 2000 and a record low of 95.000 Person th in 2012. Korea Unemployment: Year Avg: 30 to 34 Years Old data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.G023: 4 Weeks Job Search: Unemployment Rate & Unemployment: Year Average (Annual).

Sri Lanka Unemployment: Female: 30-39 yo data was reported at 26,041.000 Person in Dec 2017. This records a decrease from the previous number of 43,290.000 Person for Sep 2017. Sri Lanka Unemployment: Female: 30-39 yo data is updated quarterly, averaging 37,097.500 Person from Mar 2016 (Median) to Dec 2017, with 8 observations. The data reached an all-time high of 47,326.000 Person in Mar 2017 and a record low of 26,041.000 Person in Dec 2017. Sri Lanka Unemployment: Female: 30-39 yo data remains active status in CEIC and is reported by Department of Census and Statistics. The data is categorized under Global Database’s Sri Lanka – Table LK.G014: Labour Force Survey: MYPE 2012: Unemployment.

Unemployment Rate in Taiwan remained unchanged at 3.34 percent in June. This dataset provides the latest reported value for - Taiwan Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

The Department of Statistics (DOS) carried out four rounds of the 2012 Employment and Unemployment Survey (EUS) during 2012. The survey rounds covered a total sample of about fifty three thousand households Nation-wide (53.4 thousands). The sampled households were selected using a stratified cluster sampling design.

It is worthy to mention that the DOS employed new technology in the data collection and processing. Data was collected using the electronic questionnaire instead of a hard copy, namely a hand held device (PDA).

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a representative sample on the national level (Kingdom), governorates, and the three Regions (Central, North and South).

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

Survey Frame

The sample of this survey is based on the frame provided by the data of the Population and Housing Census, 2004. The Kingdom was divided into strata, where each city with a population of 100,000 persons or more was considered as a large city. The total number of these cities is 6. Each governorate (except for the 6 large cities) was divided into rural and urban areas. The rest of the urban areas in each governorate were considered as an independent stratum. The same was applied to rural areas where they were considered as an independent stratum. The total number of strata was 30.

And because of the existence of significant variation in the social and economic characteristics in large cities, in particular, and in urban areas in general, each stratum of the large cities and urban strata was divided into four sub-stratums according to the socio- economic characteristics provided by the population and housing census 2004 aiming at providing homogeneous strata.

Sample Design

The sample of this survey was designed using a stratified cluster sampling method. The sample is considered representative on the Kingdom, rural, urban, regions and governorates levels, however, it does not represent the non-Jordanians.

Sampling notes

The frame excludes the population living in remote areas (most of whom are nomads). In addition to that, the frame does not include collective dwellings, such as hotels, hospitals, work camps, prisons and alike. However, it is worth noting that the collective households identified in the harmonized data, through a variable indicating the household type, are those reported without heads in the raw data, and in which the relationship of all household members to head was reported "other".

This sample is also not representative for the non-Jordanian population.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed electronically on the PDA and revised by the DOS technical staff. It is divided into a number of main topics, each containing a clear and consistent group of questions, and designed in a way that facilitates the electronic data entry and verification. The questionnaire includes the characteristics of household members in addition to the identification information, which reflects the administrative as well as the statistical divisions of the Kingdom.

Cleaning operations

Raw Data

A tabulation results plan has been set based on the previous Employment and Unemployment Surveys while the required programs were prepared and tested. When all prior data processing steps were completed, the actual survey results were tabulated using an ORACLE package. The tabulations were then thoroughly checked for consistency of data. The final report was then prepared, containing detailed tabulations as well as the methodology of the survey.

Harmonized Data

• The SPSS package is used to clean and harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

The results of the fieldwork indicated that the number of successfully completed interviews was 48880 (with around 91% response rate).

Abstract

The Labor Force Survey (LFS) 2012-13, was carried out to generate reliable up-to-date information on employment and unemployment situations and other labor force characteristics of the Malawian population between the ages of 15-64 years. The National Statistical Office (NSO), together with the Ministry of Labor and Trade, the Ministry of Economic Planning and Development collaborated in conducting this survey.

The 2012-13 survey indicated that 7 million people within the age group 15-64 were in the labor force. Of this total, 3.3 million were males and 3.7 million were females. By sub-population groups, the results show that out of the total labor force, 87 percent were residents in the rural areas, 64 percent had no education and nearly half (48 percent) were under 30 years old. The labor force participation rates for both males and females were quite high. The rates ranged from 70 percent in the age group 15-19 to 97 percent in age groups 30-34 and 40-44.The specific objectives of the survey were: 1. To estimate the size of the labor force of people between 15-64 years by demographic characteristics. 2. To estimate the number of employed persons by occupation, industry and employment status. 3. To estimate the population which is not working together with their demographic characteristics. 4. To estimate youth unemployment.

The results of the survey provided statistics that served a wide variety of purposes. Some of these purposes are: - To monitor the economic situation. - To formulate and implement policies for decent work, employment creation and poverty reduction, income support as well as other social programs. - To provide indicators for monitoring the country's progress towards achieving both Millennium Development Goals (MGDS II and MDGs) goals.

Geographic coverage

National and regional levels for rural and urban areas.

Analysis unit

• Individuals
• Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the LFS was to provide employment and unemployment estimates at the national and regional levels and for rural and urban areas. A two stage sampling design was used. During the first stage, 550 clusters were drawn from the 2008 Population and Housing Census sample frame. 213 clusters from urban areas and 337 clusters from rural areas. At regional level, Northern, Central and Southern, 97 clusters, 192 clusters and 261 clusters were drawn respectively.

The NSO staff conducted an exhaustive listing of households in each of the selected clusters between July and September 2013. The household listing provided the frame for second stage of sampling, where a systematic sample of 20 households was drawn from each of LFS selected cluster. A total of 11,000 households were sampled; 4,260 from urban areas and 6,740 from rural areas. All men and women age 10 years and over in selected households were eligible for individual interviews.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were two types of questionnaires used in the 2012-13 LFS survey for data collection: the Household Questionnaire and the Individual Questionnaire. The contents were based on ILO model questionnaires, which were adapted for use in Malawi in collaboration with a wide range of stakeholders. The questionnaires were translated into two local languages, Chichewa and Tumbuka prior to pretesting.

Cleaning operations

All completed questionnaires were sent to the NSO Headquarters for data processing. The data processing started in February and was completed in June 2013. The data went through several rigorous stages of data cleaning such as structural edits, content edit and imputation. Data entry was done in the Census and Survey Processing System (CSPro), data entry application which was developed in-house. The final dataset was sent to the ILO Office in South Africa for data weighting and estimation.

Response rate

97.5%

Historical chart and dataset showing Japan unemployment rate by year from 1991 to 2024.

The unemployment rate in Africa was expected to reach seven percent in 2024. In the period under review, unemployment in the continent peaked at 7.2 percent in 2021. Unemployment levels varied significantly across African countries. South Africa was estimated to register the highest rate in 2024 at around 30 percent.