Initial Jobless Claims in the United States decreased to 216 thousand in the week ending November 22 of 2025 from 222 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Continuing Jobless Claims in the United States increased to 1960 thousand in the week ending November 15 of 2025 from 1953 thousand in the previous week. This dataset provides the latest reported value for - United States Continuing Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Non Farm Payrolls in the United States increased by 119 thousand in September of 2025. This dataset provides the latest reported value for - United States Non Farm Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States Pandemic Unemployment Assistance (PUA): Initial Claims: US data was reported at 0.127 Person th in 11 Feb 2023. This records a decrease from the previous number of 0.197 Person th for 04 Feb 2023. United States Pandemic Unemployment Assistance (PUA): Initial Claims: US data is updated weekly, averaging 27.727 Person th from Apr 2020 (Median) to 11 Feb 2023, with 150 observations. The data reached an all-time high of 1,352.180 Person th in 23 May 2020 and a record low of 0.127 Person th in 11 Feb 2023. United States Pandemic Unemployment Assistance (PUA): Initial Claims: US data remains active status in CEIC and is reported by U.S. Department of Labor. The data is categorized under Global Database’s United States – Table US.G149: Unemployment Insurance: Weekly Pandemic Claims (Discontinued). [COVID-19-IMPACT]

Unemployment Rate in South Africa decreased to 31.90 percent in the third quarter of 2025 from 33.20 percent in the second quarter of 2025. This dataset provides - South Africa Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

UPDATES OF THIS DATASET ARE TEMPORARILY SUSPENDED. This dataset contains statewide unemployment insurance payment activities by month and industry group in Iowa. Industry groups are based on NAICS sectors (North American Industry Classification System). Data available starting in January 2010.

UPDATES OF THIS DATASET ARE TEMPORARILY SUSPENDED. This dataset contains Iowa unemployment insurance benefit payments, weeks compensated, and number of benefit recipients by county. County data is based on the recipient’s place of residence. (2000 to date)

Unemployment Rate in Canada decreased to 6.90 percent in October from 7.10 percent in September of 2025. This dataset provides - Canada Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Municipal Fiscal Indicators is an annual compendium of information compiled by the Office of Policy and Management, Office of Finance, Municipal Finance Services Unit (MFS). The data contained in Indicators provides key financial and demographic information on municipalities in Connecticut. Municipal Fiscal Indicators contains the most current financial data available for each of Connecticut's 169 municipalities. The majority of this data was compiled from the audited financial statements that are filed annually with the State of Connecticut, Office of Policy and Management, Office of Finance. This database of information includes selected demographic and economic data relating to, or having an impact upon, a municipality’s financial condition. The most recent edition is for the Fiscal Years Ended 2015-2019 published in April 2021. Data on the Municipal Fiscal Indicators is included in the following datasets: Municipal Fiscal Indicators, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-2019/sb4i-6vik Municipal Fiscal Indicators: Grand List Components, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Grand-List-Components-/ifrb-kp2b Municipal Fiscal Indicators: Pension Funding Information For Defined Benefit Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Pension-Funding-Inform/civu-w22d Municipal Fiscal Indicators: Type and Number of Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Type-and-Number-of-Pen/9f65-c4yr Municipal Fiscal Indicators: Other Post-Employment Benefits (OPEB), 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Other-Post-Employment-/sa26-46h8 Municipal Fiscal Indicators: Economic and Grand List Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Economic-and-Grand-Lis/wpbp-b657 Municipal Fiscal Indicators: Benchmark Labor Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Benchmark-Labor-Data-2/db37-h23r Municipal Fiscal Indicators: Unemployment, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Unemployment-2019/cugp-2za3

Ethics reference: 2022_FBMSREC 046Abstract: Small, Medium, and Micro Enterprises (SMMEs) play a pivotal role in driving economic growth and fostering development globally, as well as in the specific context of South Africa. This importance is particularly evident in the civil, building, and mechanical engineering industries, where SMMEs contribute significantly to the country's Gross Domestic Product (GDP) and hold the potential to alleviate poverty, reduce unemployment, and promote inclusivity and fairness. This dataset explores the multifaceted significance of SMMEs in these industries.Globally, SMMEs are recognized as engines of economic growth due to their capacity to innovate, create jobs, and generate income. In South Africa, these enterprises have a profound impact on the economy, contributing poverty alleviation and income generation social inclusion. SMMEs involvement in the civil engineering, building construction, and mechanical engineering sectors are particularly crucial, as they drive infrastructure development, job creation, and skills enhancement as a future economic jack. While, the primary objective of the study is to investigate the challenges faced by SMMEs through the broader economic trends by unidentified household brand names which influence lack of capital cash-flow based on the marketing tools.The vital role of SMMEs in South Africa's GDP and poverty reduction is underscored by their potential to create employment opportunities, especially for marginalized communities, which finds expression in in the engineering and construction sector like many other economic sectors. These enterprises facilitate skills development and contribute to localized economic growth, thereby advancing inclusivity and social equity. However, SMMEs in the civil, building, and mechanical engineering industries confront an array of challenges, including limited access to financing, inadequate skills development, regulatory hurdles, and market access constraints. This empirical study evaluated the marketing tools that will influence capital cash-flow in SMMEs. The study employed comparative methodology comprised of quantitative closed-ended questionnaire and qualitative open-ended questionnaire. Which linked well with pragmatic paradigm. The study aimed at utilising 130 SMMEs participants from the engineering sector. Backed by this dataset, the study revealed that most private sectors are engaged in supporting the growth and mentoring of SMMEs. Additionally, the Free-State government provides financial support and facilitates marketing access to the SMMEs. The study recommends that the Free-State government should intensify programmes of skills knowledge development and marketing competition to maintain capital cash-flow sustainability in SMMEs.

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.

"The Honoring Investments in Recruiting and Employing American Military Veterans Act of 2017 (HIRE Vets Act or the Act), requires the Secretary of Labor to establish a program, by rule, that recognizes employer efforts to recruit, employ, and retain veterans. Employer-applicants meeting criteria established in the rule will receive a “HIRE Vets Medallion Award.” As described in the Act, there are different awards for large employers (500-plus employees), medium employers (51-499 employees), and small employers (50 or fewer employees). Additionally, there are two award tiers: platinum and gold. For each award, the employer must satisfy a set of criteria. This dataset contains information from the application form completed by employers who applied to the HIRE Veterans Medallion Program. Information collected includes: company identifier, personal identifier, hiring/retention statistics and program information in application process from businesses who wish to be recognized for outstanding veteran hiring/retention. Under the Act, VETS will: 1. Solicit applications No later than January 31; and 2. Stop accepting applications on April 30."

Technology companies worldwide saw a significant reduction in their workforce in 2025. One of the most recent tech layoffs was by Amazon on October 27, 2025, with ****** employees being laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of ******* employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of ******* laid-off employees in the global tech sector by the end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks, leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.

The "Expanded Jobseeker Payment and Youth Allowance - monthly profile" publication has introduced expanded reporting populations for income support recipients. As a result, the reporting population for Jobseeker Payment has changed to include recipients who are current but on zero rate of payment and those who are suspended from payment. The reporting population for Youth Allowance has changed to include those who are suspended from payment. The expanded report will replace the standard report after June 2023.

Additional data for JobSeeker Payment and Youth Allowance (other) recipients in the monthly profile includes:

• A monthly time series by rate of payment, providing details of recipients who are current on payment and in receipt of a full, part or zero rate of payment, and those who are suspended from payment (table 2)

• By work capacity status, showing those who have a partial capacity to work and those who have full capacity (table 7)

• By payment duration (table 8)

The “JobSeeker Payment and Youth Allowance recipients – monthly profile” is a monthly report, covering the Income Support payments of JobSeeker Payment and Youth Allowance (other). It also includes data on Youth Allowance (student and apprentice), Sickness Allowance and Bereavement Allowance. The report includes payment recipient numbers by demographics such as age, gender, state, earnings and Statistical Area Level 2.

Municipal Fiscal Indicators is an annual compendium of information compiled by the Office of Policy and Management, Office of Finance, Municipal Finance Services Unit (MFS). The data contained in Indicators provides key financial and demographic information on municipalities in Connecticut.

Municipal Fiscal Indicators contains the most current financial data available for each of Connecticut's 169 municipalities. The majority of this data was compiled from the audited financial statements that are filed annually with the State of Connecticut, Office of Policy and Management, Office of Finance. This database of information includes selected demographic and economic data relating to, or having an impact upon, a municipality’s financial condition. The most recent edition is for the Fiscal Years Ended 2015-2019 published in April 2021.

Data on the Municipal Fiscal Indicators is included in the following datasets:

Municipal Fiscal Indicators, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-2019/sb4i-6vik

Municipal Fiscal Indicators: Grand List Components, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Grand-List-Components-/ifrb-kp2b

Municipal Fiscal Indicators: Pension Funding Information For Defined Benefit Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Pension-Funding-Inform/civu-w22d

Municipal Fiscal Indicators: Type and Number of Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Type-and-Number-of-Pen/9f65-c4yr

Municipal Fiscal Indicators: Other Post-Employment Benefits (OPEB), 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Other-Post-Employment-/sa26-46h8

Municipal Fiscal Indicators: Economic and Grand List Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Economic-and-Grand-Lis/wpbp-b657

Municipal Fiscal Indicators: Benchmark Labor Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Benchmark-Labor-Data-2/db37-h23r

Municipal Fiscal Indicators: Unemployment, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Unemployment-2019/cugp-2za3

Municipal Fiscal Indicators is an annual compendium of information compiled by the Office of Policy and Management, Office of Finance, Municipal Finance Services Unit (MFS). The data contained in Indicators provides key financial and demographic information on municipalities in Connecticut.

Municipal Fiscal Indicators contains the most current financial data available for each of Connecticut's 169 municipalities. The majority of this data was compiled from the audited financial statements that are filed annually with the State of Connecticut, Office of Policy and Management, Office of Finance. This database of information includes selected demographic and economic data relating to, or having an impact upon, a municipality’s financial condition. The most recent edition is for the Fiscal Years Ended 2015-2019 published in April 2021.

Data on the Municipal Fiscal Indicators is included in the following datasets:

Municipal Fiscal Indicators, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-2019/sb4i-6vik

Municipal Fiscal Indicators: Grand List Components, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Grand-List-Components-/ifrb-kp2b

Municipal Fiscal Indicators: Pension Funding Information For Defined Benefit Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Pension-Funding-Inform/civu-w22d

Municipal Fiscal Indicators: Type and Number of Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Type-and-Number-of-Pen/9f65-c4yr

Municipal Fiscal Indicators: Other Post-Employment Benefits (OPEB), 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Other-Post-Employment-/sa26-46h8

Municipal Fiscal Indicators: Economic and Grand List Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Economic-and-Grand-Lis/wpbp-b657

Municipal Fiscal Indicators: Benchmark Labor Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Benchmark-Labor-Data-2/db37-h23r

Municipal Fiscal Indicators: Unemployment, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Unemployment-2019/cugp-2za3

Municipal Fiscal Indicators is an annual compendium of information compiled by the Office of Policy and Management, Office of Finance, Municipal Finance Services Unit (MFS). The data contained in Indicators provides key financial and demographic information on municipalities in Connecticut. Municipal Fiscal Indicators contains the most current financial data available for each of Connecticut's 169 municipalities. The majority of this data was compiled from the audited financial statements that are filed annually with the State of Connecticut, Office of Policy and Management, Office of Finance. This database of information includes selected demographic and economic data relating to, or having an impact upon, a municipality’s financial condition. The most recent edition is for the Fiscal Years Ended 2015-2019 published in April 2021. Other Post-Employment Benefits (OPEB) for Connecticut Municipalities, compiled by the CT Office of Policy and Management in the annual Municipal Fiscal Indicators report. Post-employment benefits are typically provided by municipalities to former employees or their beneficiaries as compensation for services rendered while these employees were still active. These benefits are generally divided into two broad categories – pension benefits (retirement income) and Other Post-employment Benefits (postemployment benefits other than pensions, referenced as OPEB). Forms of OPEB typically include healthcare benefits and benefits such as disability and life insurance provided outside of the pension plan. Data on the Municipal Fiscal Indicators is included in the following datasets: Municipal Fiscal Indicators, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-2019/sb4i-6vik Municipal Fiscal Indicators: Grand List Components, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Grand-List-Components-/ifrb-kp2b Municipal Fiscal Indicators: Pension Funding Information For Defined Benefit Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Pension-Funding-Inform/civu-w22d Municipal Fiscal Indicators: Type and Number of Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Type-and-Number-of-Pen/9f65-c4yr Municipal Fiscal Indicators: Other Post-Employment Benefits (OPEB), 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Other-Post-Employment-/sa26-46h8 Municipal Fiscal Indicators: Economic and Grand List Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Economic-and-Grand-Lis/wpbp-b657 Municipal Fiscal Indicators: Benchmark Labor Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Benchmark-Labor-Data-2/db37-h23r Municipal Fiscal Indicators: Unemployment, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Unemployment-2019/cugp-2za3

The Urban Institute established the Reentry Mapping Network (RMN), a group of jurisdictions applying a data-driven, spatial approach to prisoner reentry. The purpose of the study was to examine three National Institute of Justice-funded RMN sites: Milwaukee, Wisconsin, Washington, DC, and Winston-Salem, North Carolina. As members of the Reentry Mapping Network, the three sites collected local data related to incarceration, reentry, and community well-being. The Nonprofit Center of Milwaukee's Neighborhood Data Center was the lead Reentry Mapping Network partner in Milwaukee. Data on a total of 168 census tracts in Milwaukee (Part 1) during the calendar year 2003 were obtained from the Wisconsin Department of Corrections. NeighborhoodInfo DC was the lead reentry mapping network partner in Washington, DC. Data on a total of 7,286 ex-offenders in Washington, DC (Part 2) during the calendar year 2004 were obtained from the Court Services and Offender Supervision Agency (CSOSA) for the District of Columbia. The Winston-Salem Reentry Mapping Network project was managed by the Center for Community Safety (CCS), a public service and research center of Winston-Salem State University. Data on a total of 2,896 ex-offenders in Forsyth County (Part 3) during the calendar year 2003 were obtained from the North Carolina Department of Corrections (DOC), the Forsyth County Sheriff's Department (Forsyth County Detention Center [FCDC]), and the North Carolina Department of Juvenile Justice and Delinquency Prevention (DJJDP). The Milwaukee, Wisconsin Data (Part 1) contain a total of 95 variables including race, ethnicity, gender, marital status, education, job status, dependents, general risk assessment, alcohol risk, drug risk, need for alcohol treatment, and need for drug treatment. Also included are four geographic variables. The Washington, DC Data (Part 2) contain a total of 13 variables including supervision type, whether supervision began in calendar year 2004, date supervision period began, date supervision period ended, sex, marital status, ethnicity, age, education, unemployment status, state, and Census tract. The Winston-Salem, North Carolina Data (Part 3) contain a total of 14 variables including race, sex, primary offense, admittance date, date pardoned, street, city, state, status, jurisdiction, and age at admission.

Municipal Fiscal Indicators is an annual compendium of information compiled by the Office of Policy and Management, Office of Finance, Municipal Finance Services Unit (MFS). The data contained in Indicators provides key financial and demographic information on municipalities in Connecticut. Municipal Fiscal Indicators contains the most current financial data available for each of Connecticut's 169 municipalities. The majority of this data was compiled from the audited financial statements that are filed annually with the State of Connecticut, Office of Policy and Management, Office of Finance. This database of information includes selected demographic and economic data relating to, or having an impact upon, a municipality’s financial condition. The most recent edition is for the Fiscal Years Ended 2015-2019 published in April 2021. Data on the Municipal Fiscal Indicators is included in the following datasets: Municipal Fiscal Indicators, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-2019/sb4i-6vik Municipal Fiscal Indicators: Grand List Components, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Grand-List-Components-/ifrb-kp2b Municipal Fiscal Indicators: Pension Funding Information For Defined Benefit Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Pension-Funding-Inform/civu-w22d Municipal Fiscal Indicators: Type and Number of Pension Plans, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Type-and-Number-of-Pen/9f65-c4yr Municipal Fiscal Indicators: Other Post-Employment Benefits (OPEB), 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Other-Post-Employment-/sa26-46h8 Municipal Fiscal Indicators: Economic and Grand List Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Economic-and-Grand-Lis/wpbp-b657 Municipal Fiscal Indicators: Benchmark Labor Data, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Benchmark-Labor-Data-2/db37-h23r Municipal Fiscal Indicators: Unemployment, 2019 https://data.ct.gov/Local-Government/Municipal-Fiscal-Indicators-Unemployment-2019/cugp-2za3

Die Situation junger Arbeitsloser und die von staatlichen und nichtstaatlichen Organisationen (NGO) angebotene Hilfe. Themen: Familienhintergrund; Nationalität und Religionszugehörigkeit;Beruf der Eltern vor und nach 1989; Beruf der Geschwister;Familienstand; Anzahl und Alter der Kinder; Haushaltstyp;Bildungsabschluß; Dauer der Ausbildung; Schultyp undSpezialisierungsrichtung; Zusatzausbildung; Berufswunsch während derSchulzeit und Beratungsquellen; Übereinstimmung von erreichter Bildungund früherer sowie gegenwärtiger Karrierepläne; Beschäftigungsverlaufseit dem Schulabschluß; Anzahl und Arten der ausgeübten Tätigkeiten;Wirtschaftssektor und Dauer der Tätigkeit; Gründe für Beendigung derBeschäftigung; Erfahrungen mit amtlich registrierter und nichtregistrierter Arbeitslosigkeit; Dauer der Arbeitslosigkeit; Höhe derArbeitslosenunterstützung; Vorteile und Nachteile von Arbeitslosigkeit;Beteiligung an der Schattenwirtschaft; Art der Beschäftigung und Höheder Einkünfte; Strategien zur Arbeitsuche; Entschlossenheit, eineArbeit zu finden; bevorzugte Tätigkeiten; Unterstützung durch Familie,Freunde, Arbeitsamt und nicht staatlichen Organisationen; Erwartungenan die Zukunft; Optimismus oder Pessimismus hinsichtlich des eigenenErwerbsstatus und der finanziellen Situation in zehn Jahren; Wünschefür den zukünftigen beruflichen Status der eigenen Kinder; Freizeit;Mitgliedschaft in beruflichen, politischen, religiösen undFreizeitorganisationen; auswärtiger Urlaub im letzten Jahr; besuchteRestaurants in den letzten vier Wochen; Kirchgangshäufigkeit in denletzten vier Wochen; Besitz eines Autos, Satelliten-TV, Computer undMobiltelefon; Zeit für sich selbst, die Familie und Freunde;beliebteste Tätigkeit am Tag; Bedarf an Erholungsmöglichkeiten,Treffpunkten und Clubs; Beratung und Geldleistungen währendArbeitslosigkeit; politische Einstellungen; Ansichten über dieGeschlechterrollen; Qualität des Familienlebens in der Vergangenheitund Gegenwart; politische Haltung; Vorteile und Nachteile derMarktwirtschaft; preferiertes Modell für die Entwicklung des eigenenLandes; Ansichten zur Außenpolitik; Wahlbeteiligung bei nächster Wahl;Meinungen über lokale und Staatspolitiker, die Gewerkschaften und dieKirche; Bewertung der staatlichen und nicht staatlichenArbeitsmarktprogramme für junge Leute; Umfang der Aktivitäten undZielgruppen; Organisationsstruktur; Personal; Budget der NGO;Geschichte der Organisationen und Plänen für die weitere Entwicklung;Entwicklungshemmnisse; mögliche Partner; Berufsverlauf der Leiter undihre Wahrnehmungen der Jugend und deren Probleme. The match between the situations of young unemployed and theassistance offered to them by state agencies and non-governmentalorganizations (NGO).Topics: Family background; nationality status and religiousaffiliation; parents´professional status before and after 1989;occupational status of siblings; marital status; number and age ofchildren and type of household; educational background; years spent intofull-time education; type of school and specialty; involvement intopost-compulsory education; plans for employment status while at schooland source of career advice; sequence between education and past andpresent career aims; working careers since leaving school; number andtypes of jobs held; economic sector of the jobs and months they wereheld; why and how each job ended; experience of registered andunregistered unemployment; length of joblessness; amount of unemploymentbenefits; advantages and disadvantages of being jobless; involvementinto the second economy; types of jobs and amount of informal earnings;strategies applied in the job search; degree of determination to find ajob; work preferences; sources of assistance; forms of family support;forms of help received from friends, state labor offices andnon-governmental organizations; main source of assistance duringunemployment; expectations about the future; optimism or pessimism aboutown job status and financial situation in ten years; wishes for thefuture job status of own children; membership in occupational /industrial, political, recreational and religious organizations; numberof holidays spent away from home in the past year; number of restaurantsvisits in the past four weeks; church attendance in the past four weeks;use of motor car, satellite or cable TV, PC and mobile phone; time foroneself, for family and friends; favorite activities during the day;requests for clubs, meeting spaces and recreation facilities; counselingand money during unemployment; socio-political attitudes; views aboutgender roles; quality of family life during the past and present regime;political preferences; advantages and disadvantages of market economy;desired model for own country´s development; foreign policyorientations; plans to vote in the next election; opinions aboutnational and local politicians; trade unions and the church; evaluationof state and NGO programs available to young people in the labor market;scope of activities and target groups; organizational structure;personnel; budget of the NGO; history of the organization and plans fordevelopment; barriers for development; partners; professional career ofthe leaders; their perceptions of young people and youth problems.