In August 2025, the agriculture and related private wage and salary workers industry had the highest unemployment rate in the United States, at seven percent. In comparison, financial activities workers had the lowest unemployment rate, at 1.6 percent. The average for all industries was 4.5 percent. U.S. unemployment There are several factors that impact unemployment, as it fluctuates with the state of the economy. Unfortunately, the forecasted unemployment rate in the United States is expected to increase as we head into the latter half of the decade. Those with a bachelor’s degree or higher saw the lowest unemployment rate from 1992 to 2022 in the United States, which is attributed to the fact that higher levels of education are seen as more desirable in the workforce. Nevada unemployment Nevada is one of the states with the highest unemployment rates in the country and Vermont typically has one of the lowest unemployment rates. These are seasonally adjusted rates, which means that seasonal factors such as holiday periods and weather events that influence employment periods are removed. Nevada's economy consists of industries that are currently suffering high unemployment rates such as tourism. As of May 2023, about 5.4 percent of Nevada's population was unemployed, possibly due to the lingering impact of the coronavirus pandemic.

The seasonally-adjusted national unemployment rate is measured on a monthly basis in the United States. In June 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.

Unemployment Rate in Argentina decreased to 7.60 percent in the second quarter of 2025 from 7.90 percent in the first quarter of 2025. This dataset provides - Argentina Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Among European Union countries in July 2025, Spain had the highest unemployment rate at 10.4 percent, followed by Finland at 10 percent. By contrast, Malta has the lowest unemployment rate in Europe, at 2.6 percent. The overall rate of unemployment in the European Union was 5.9 percent in the same month - a historical low-point for unemployment in the EU, which had been at over 10 percent for much of the 2010s.

Marie Jahoda’s latent deprivation model proposes that unemployed people have a worse mental health compared to employed people. This is because they suffer not only from a lack of the manifest function of employment (earning money), but also from a lack of five so-called latent functions of employment: Time structure, social contact, collective purpose (i.e., the sense of being useful to other people), status, and activity. In order to test the basic assumptions of this theory, a study based on meta-analytic methods was conducted. Results showed that employed people reported higher levels on all five latent functions, as well as on the manifest function, compared to unemployed people. They also report more latent functions than people who are out of the labor force (OLF). Moreover, OLF-people reported more manifest and latent functions than unemployed people. Specific analyses for three OLF-subgroups found retired people to be almost as deprived of the latent functions (but not the manifest function) as unemployed people, while students were more similar to employed people but still experienced some manifest and latent deprivation. For homemakers, the effect sizes pointed in the expected direction, but they were not significant. Thus, the proposition that employment is the best provider of the latent functions was generally endorsed, although homemakers need further scrutiny in future studies. All latent functions, as well as the manifest function, emerged as significant independent predictors of mental health, when the influence of the other manifest and latent functions was controlled. Together, the dimensions in the model explained 19% of variation in mental health.

Chile CG: Expenditure: SS: Unemployment data was reported at 319,125.839 CLP mn in 2023. This records a decrease from the previous number of 340,018.223 CLP mn for 2022. Chile CG: Expenditure: SS: Unemployment data is updated yearly, averaging 58,222.000 CLP mn from Dec 1987 (Median) to 2023, with 37 observations. The data reached an all-time high of 340,018.223 CLP mn in 2022 and a record low of 2,504.000 CLP mn in 1990. Chile CG: Expenditure: SS: Unemployment data remains active status in CEIC and is reported by Chilean Budget Estimation Directory. The data is categorized under Global Database’s Chile – Table CL.F012: Central Government: Expenditure: by Functional Classification.

In Eurostat database, ESSPROS data on expenditure and receipts, data on net social protection benefits, data on Pension beneficiaries and data on Unemployment benefits recipients for the total of schemes are currently disseminated. The qualitative information is available in the dedicated "Social protection" section of the Eurostat website. ESSPROS data, quality reports and the qualitative information are released annually. Among the three main categories of data sources - administrative data, national accounts and other estimates, surveys/census - most of the countries report administrative data and register-based data as their main data sources.

Data on expenditure and receipts correspond to two collections "EXPEND" (Social protection expenditure) and "RECEIPTS" (Social protection receipts).

The collection "EXPEND" is structured in twelve tables:

1. spr_exp_type – The data include the expenditure broken down by social benefits, administration costs and other expenditure.
2. spr_exp_func – The data include the expenditure on social benefits classified by functions of social protection. Early estimates for social protection expenditure broken down by function are also included in this table.
3. spr_exp_pens – The data include the social benefits relating to pensions (old age, disability, survivors and unemployment pensions). In addition, the data are split between means-tested and non-means-tested benefits.
4. spr_exp_ftm – The data include expenditure on social benefits by function, type of benefits and means-testing.
5. spr_exp_fsi – The data include expenditure on the sickness and healthcare function by type of benefit and means-testing.
6. spr_exp_fdi – The data include the expenditure on disability function by type of benefit and means-testing.
7. spr_exp_fol – The data include the expenditure on old age function by type of benefit and means-testing.
8. spr_exp_fsu – The data include expenditure on survivors’ function by type of benefit and means-testing.
9. spr_exp_ffa – The data include expenditure on family/children function by type of benefit and means-testing.
10. spr_exp_fun – The data include expenditure on unemployment function by type of benefit and means-testing.
11. spr_exp_fho – The data include expenditure on housing function by type of benefit.
12. spr_exp_fex – The data include expenditure on social exclusion n.e.c function by type of benefit and means-testing.

Data on expenditure are made available in the following units: million euro, million euro at constant prices (using either 2010 or 2015 as base year), euro per inhabitant, euro per inhabitant at constant prices (using either 2010 or 2015 as base year), million units of national currency, million units of national currency at constant prices (using either 2010 or 2015 as base year), million pps, million pps per inhabitant, percentage of GDP. Depending on the table, the data can also be expressed as percentage of expenditure, percentage of total benefits or percentage of a given function.

Whenever applied, the split of social protection benefits by function encompasses eight functions:

• Sickness /Health care
• Disability
• Old age
• Survivors
• Family/children
• Unemployment
• Housing
• Social exclusion not elsewhere classified as well as the total of social benefits.

The detailed classification of benefits by type includes cash benefits and benefits in kind.

The collection "RECEIPTS" includes the following four tables:

1. spr_rec_sumt- The data include the receipts of social protection broken down by the different types of receipts - social contributions, general government contributions and other receipts.
2. spr_rec_scon – The data include social contributions by type of contributors: employer, protected persons, employees, self-employed, pensioners and other protected persons.
3. spr_rec_sums – The data include social contributions by the different sectors of origin: corporations, central government, state and local government, households, non-profit institutions, rest of the world.
4. spr_rec_suts – The data include social protection receipts by type and institutional sector.

Data on receipts are made available in the following units: million euro, million euro at constant prices (using either 2010 or 2015 as base year), euro per inhabitant, euro per inhabitant at constant prices (using either 2010 or 2015 as base year), million units of national currency, million units of national currency at constant prices (using either 2010 or 2015 as base year), million pps, million pps pr inhabitant, percentage of GDP, percentage of total receipt.

Data on net social protection benefits correspond to the table " spr_net_ben" (Net social protection benefits) in the collection "NET".

The ESSPROS module on net social protection benefits (restricted approach) measures net expenditure by collecting information on the average rates of taxes and social contributions paid by recipients of each cash benefit reported in the Core system. These rates are then applied to the gross expenditure on each benefit to obtain a net value as follows:

Net social benefits = Gross social benefits * (1 – AITR – AISCR)

where AITR / AISCR are the Average itemized tax / social contribution rates.

The net social protection benefits are complemented by the value of “Fiscal benefits” provided in the form of tax breaks that would be defined as social protection benefits, if they were provided in cash. Tax breaks promoting the provision of social protection or promoting private insurance plans are excluded. Exceptionally, if some fiscal benefits cannot be taken into account in the assessment of the actual taxes and social contributions paid on social benefits (this happens for few countries), then the value of net benefits should be complemented by the residual value of the fiscal benefit. In this case the formula above becomes:

Net social benefits = Gross social benefits * (1 – AITR – AISCR) + Residual fiscal benefits

In ESSPROS, fiscal benefits are defined as social protection provided in the form of tax breaks that would be defined as social protection benefits if they were provided in cash, excluding tax breaks promoting the provision of social protection or promoting private insurance plans.

According to a 2015 decision of the Working Group on Social Protection Statistics, fiscal benefits in the form of payable tax credits should be included in full (both cash component plus fiscal component) in the ESSPROS Core System while all other forms of fiscal benefit are excluded from the Core System and dealt with in the NET modules (the existing restricted approach module and the planned enlarged approach modules).

More information is available in the Annex I "Payable tax credits".

Data on pension beneficiaries correspond to the collection "PENS" that contains the table "spr_pns_ben" (Pension Beneficiaries at 31 December of each year).

The data include the number of recipients of one or more periodic cash benefits under a social protection scheme falling within seven pension categories grouped into four basic functions.

The seven categories of pensions in this module are:

• Disability pension;
• Early retirement benefit due to reduced capacity to work;
• Old-age pension;
• Anticipated old-age pension;
• Partial pension;
• Survivor's pension;
• Early retirement benefit due to labour market reasons.

The four functions of the module are:

• Disability (grouping the first two categories of pensions above);
• Old age (grouping the next three);
• Survivors (the sixth category);
• Unemployment (the last category).

The data, available by gender for the total of schemes, are expressed in "units".

ESSPROS data on pension beneficiaries may represent aggregates of multiple types of pensions, granted for various purposes, under different conditions, and to different groups with different levels of entitlement. It is essential, therefore, that users understand exactly what the data show and can interpret the figures correctly. In view of this, please see the publication "Social protection statistics - pension expenditure and pension beneficiaries" and more specifically the section "interpretation of data".

Data on Unemployment benefits recipients correspond to the collection “UBR” that contains the table “spr_ubr_ben”.

The data include the number of recipients of one or more periodic cash benefits as classified under the unemployment function:

• Full unemployment;
• Partial

The activity rate expresses the ratio to the working-age population (15-64) of people who actually enter the labour market, whether they are employed or unemployed. That rate therefore reflects behaviour in relation to the labour market, which is itself a function of a considerable number of variables relating as much to the individual, his family and his culture as to the economic and institutional context in which he operates.

See also: - on our website 'Labour Market Statistics', The Employment Accounts and the IWEPS Working Paper No 13.

Note: From 2011, the indicators are calculated on the basis of Steunpunt Werk estimates, which showed a break in series in 2017: the methodology for estimating non-taxable students is changed and employees of international organisations have been included in the employed assets. In 2019, the source used by Steunpunt Werk for the number of outgoing cross-border commuters changes, which leads to a drop in employment, and therefore also in activity, and an increase in the unemployment rate, which can be significant in some border municipalities. As a result of this problem and the delay in Steunpunt Werk’s estimates due to the increasing difficulty of obtaining sufficiently detailed data on employed workers, from 2019 the indicators are calculated on the basis of provisional estimates from IWEPS.

More information on the IWEPS website:

• the "\2"

• the "\2"

• labour market statistics

This article considers an endogenous kink threshold regression model with an unknown threshold value in a time series as well as a panel data framework, where both the threshold variable and regressors are allowed to be endogenous. We construct our estimators from a nonparametric control function approach and derive the consistency and asymptotic distribution of our proposed estimators. Monte Carlo simulations are used to assess the finite sample performance of our proposed estimators. Finally, we apply our model to analyze the impact of COVID-19 cases on labor markets in the United States and Canada.

Apart from a few individual studies the labor market and it’s segregation, the relation between supply and demand, employment structure, and unemployment is until today not analyzed in its historical dimension. The same applies to the history of labor market policy. The present study’s aim is to fill this gap by describing the most important elements of the labor market policy: the employment service, the job creation, and the unemployment benefit. The researcher addresses one of the most important problems of the modern, on the intense division of labor based economy: the labor market coverage with highly skilled workers. The reason for the complexity of this task lies in the strong segmentation of the labor market (numerous sub-markets and sectors with different requirements on qualifications) and – since the industrialization – the fact, that the labor market is in a process of constant, sometimes short term changes. To manage this situation, market transparency to the largest possible extent is necessary, which is a central field of the employment service’s responsibility. To this basic function (the supply of the labor market with adequate skilled workers, called by Anselm Faust ‘market function’) further important functions are attached, for example the prevention of unemployment. The not commercially labor service was expanded in Germany to an inherent part of modern labor market policy in a period between 30 and 40 years. The labor service was in the end of the 19th century insignificant and both institutionally and in terms of a policy of interests fragmented. But in 1927 it was integrated by the law about labor service and unemployment insurance into a system of coordinated public institutions and public policies. The purpose of this new implemented law is to balance and to influence the labor market, the employment policy, and to ensure a basic social care of the unemployed. The goals of the labor market policy, developed in a long historical process til today, can be summarized as follows: - to influence quantity, composition and qualification of possible and actual labor force in direction to an optimal structure and development; - to induce the best possible adaption between available labor force and working places; - to use the labor force productively, fully and continuously to enable the individual and public increase in welfare or benefit; - to protect the economically active population from the consequences of unemployment. The preset study addresses the most important elements in historical view and in policy terms, i.e. the labor service, job creation, and unemployment compensation. The labor market is the place to meet demand and supply. Therefore, labor service is the organized market process and the contact point, where supply and demand for labor does coincide. The history of labor service, job creation, and unemployment compensation in Germany between 1890 and 1918 is analyzed in terms of: - its social and economical preconditions: the structure of the labor market, the development and social meaning of employment and unemployment; - the theoretical and ideological subsumption as well as the social interests derived from the social and economic conditions. - the function of labor market policy within the German ‘Kaiserreich’s’ (German Royal empire’s) conflicts of interests and the political importance of labor market policy, resulting from the conflicts of interests; - the strategies for solving the labor market conflicts, the actions and investments and their organizational arrangement; - the government’s part by solving conflicts and organizing the labor market; - the relevance of labor market instruments to organize labor market processes and to protect the unemployed. (see: Faust, A., 1986: Arbeitsmarktpolitik im Deutschen Kaiserreich. Arbeitsvermittlung, Arbeitsbeschaffung und Arbeitslosenunter¬stützung 1890-1918. Stuttgart: Franz Steiner, S. 2f, S. 10).

Datatables in the search- and downloadsystem HISTAT (Topic: Erwerbstätigkeit (=employment) ) Annotation: HISTAT is offered in German.

A. Arbeitslosigkeit (=Unemployment)

A.01 Arbeitsgesuche auf 100 offene Stellen (1907-1918) (number of applications to 100 vacancies) A.02 Die Arbeitslosenquote in den Gewerkschaften (1904-1918) (unemployment rate in lobor unions) A.03 Die Arbeitslosenquoten in den Gewerkschaftsverbänden (1904-1918) (unemployment rates in trade union associations) A.04 die geschlechtsspezifische Arbeitslosenqu...

Le taux d’activité exprime le rapport à la population d’âge actif (15 à 64 ans) des personnes qui se présentent effectivement sur le marché du travail, qu’elles soient occupées ou chômeuses. Ce taux traduit donc un comportement par rapport au marché du travail comportement qui est lui-même fonction d’un nombre considérable de variables tenant autant à l’individu, à sa famille et à sa culture, qu’au contexte économique et institutionnel dans lequel il évolue. Voir aussi : - sur notre site ’Statistiques – Marché du travail', Les comptes de l’emploi et le Working Paper de l’IWEPS n°13. Note : A partir de 2011, les indicateurs sont calculés sur la base des estimations du Steunpunt Werk, marquées par une rupture de série en 2017 : la méthode d'estimation des étudiants non assujettis est modifiée et les salariés d'organismes internationaux ont été intégrés aux actifs occupés. En 2019, la source utilisée par le Steunpunt Werk pour le nombre de frontaliers sortants change, ce qui entraine une baisse de l’emploi, donc aussi de l’activité, et une hausse du taux de chômage qui peuvent être importantes dans certaines communes frontalières. Suite à ce problème et au retard des estimations du Steunpunt Werk du à la difficulté croissante d’obtenir des données suffisamment détaillées sur les travailleurs salariés, à partir de 2019, les indicateurs sont calculés sur la base d’estimations provisoires de l’IWEPS. Plus d'informations sur le site de l'IWEPS : - la "\2" - les "\2" - les statistiques du marché du travail

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Central Bank Discount Window Operations: Function, Importance, and Impact on Monetary Policy

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The data on expenditure under the various social protection schemes are drawn up according to the ESSPROS (European System of integrated Social Protection Statistics) Manual issued by Eurostat. Generally, the objectives of ESSPROS are to provide a comprehensive, realistic and coherent description of social protection which: (i) covers social benefits and their financing; (ii) is geared towards international comparability; and (iii) is completely harmonised with other statistics, particularly the National Accounts, in its main concepts. The Unemployment function consists of the following benefits: Special Unemployment Benefit, Unemployment Benefit, Social Assistance Board, Subsidiary Unemployment Assistance, Unemployment Assistance and Unemployment Assistance Tapering. Spatial ESSPROS data is represented per 1000 population. The data source used to compile the beneficiaries data is the System for the Administration of Social Benefits (SABS) database held by the Department of Social Security. Beneficiaries are grouped according to their ID card number. If a person received a particular benefit more than once in a calendar year, the records show one beneficiary. Beneficiaries obtaining more than one benefit under the same function are counted once. Beneficiaries living abroad are not included in the data.

In Eurostat database, ESSPROS data on expenditure and receipts, data on net social protection benefits, data on Pension beneficiaries and data on Unemployment benefits recipients for the total of schemes are currently disseminated. The qualitative information is available in the dedicated "Social protection" section of the Eurostat website. ESSPROS data, quality reports and the qualitative information are released annually. Among the three main categories of data sources - administrative data, national accounts and other estimates, surveys/census - most of the countries report administrative data and register-based data as their main data sources.

Data on expenditure and receipts correspond to two collections "EXPEND" (Social protection expenditure) and "RECEIPTS" (Social protection receipts).

The collection "EXPEND" is structured in twelve tables:

1. spr_exp_type – The data include the expenditure broken down by social benefits, administration costs and other expenditure.
2. spr_exp_func – The data include the expenditure on social benefits classified by functions of social protection. Early estimates for social protection expenditure broken down by function are also included in this table.
3. spr_exp_pens – The data include the social benefits relating to pensions (old age, disability, survivors and unemployment pensions). In addition, the data are split between means-tested and non-means-tested benefits.
4. spr_exp_ftm – The data include expenditure on social benefits by function, type of benefits and means-testing.
5. spr_exp_fsi – The data include expenditure on the sickness and healthcare function by type of benefit and means-testing.
6. spr_exp_fdi – The data include the expenditure on disability function by type of benefit and means-testing.
7. spr_exp_fol – The data include the expenditure on old age function by type of benefit and means-testing.
8. spr_exp_fsu – The data include expenditure on survivors’ function by type of benefit and means-testing.
9. spr_exp_ffa – The data include expenditure on family/children function by type of benefit and means-testing.
10. spr_exp_fun – The data include expenditure on unemployment function by type of benefit and means-testing.
11. spr_exp_fho – The data include expenditure on housing function by type of benefit.
12. spr_exp_fex – The data include expenditure on social exclusion n.e.c function by type of benefit and means-testing.

Data on expenditure are made available in the following units: million euro, million euro at constant prices (using either 2010 or 2015 as base year), euro per inhabitant, euro per inhabitant at constant prices (using either 2010 or 2015 as base year), million units of national currency, million units of national currency at constant prices (using either 2010 or 2015 as base year), million pps, million pps per inhabitant, percentage of GDP. Depending on the table, the data can also be expressed as percentage of expenditure, percentage of total benefits or percentage of a given function.

Whenever applied, the split of social protection benefits by function encompasses eight functions:

• Sickness /Health care
• Disability
• Old age
• Survivors
• Family/children
• Unemployment
• Housing
• Social exclusion not elsewhere classified as well as the total of social benefits.

The detailed classification of benefits by type includes cash benefits and benefits in kind.

The collection "RECEIPTS" includes the following four tables:

1. spr_rec_sumt- The data include the receipts of social protection broken down by the different types of receipts - social contributions, general government contributions and other receipts.
2. spr_rec_scon – The data include social contributions by type of contributors: employer, protected persons, employees, self-employed, pensioners and other protected persons.
3. spr_rec_sums – The data include social contributions by the different sectors of origin: corporations, central government, state and local government, households, non-profit institutions, rest of the world.
4. spr_rec_suts – The data include social protection receipts by type and institutional sector.

Data on receipts are made available in the following units: million euro, million euro at constant prices (using either 2010 or 2015 as base year), euro per inhabitant, euro per inhabitant at constant prices (using either 2010 or 2015 as base year), million units of national currency, million units of national currency at constant prices (using either 2010 or 2015 as base year), million pps, million pps pr inhabitant, percentage of GDP, percentage of total receipt.

Data on net social protection benefits correspond to the table " spr_net_ben" (Net social protection benefits) in the collection "NET".

The ESSPROS module on net social protection benefits (restricted approach) measures net expenditure by collecting information on the average rates of taxes and social contributions paid by recipients of each cash benefit reported in the Core system. These rates are then applied to the gross expenditure on each benefit to obtain a net value as follows:

Net social benefits = Gross social benefits * (1 – AITR – AISCR)

where AITR / AISCR are the Average itemized tax / social contribution rates.

The net social protection benefits are complemented by the value of “Fiscal benefits” provided in the form of tax breaks that would be defined as social protection benefits, if they were provided in cash. Tax breaks promoting the provision of social protection or promoting private insurance plans are excluded. Exceptionally, if some fiscal benefits cannot be taken into account in the assessment of the actual taxes and social contributions paid on social benefits (this happens for few countries), then the value of net benefits should be complemented by the residual value of the fiscal benefit. In this case the formula above becomes:

Net social benefits = Gross social benefits * (1 – AITR – AISCR) + Residual fiscal benefits

In ESSPROS, fiscal benefits are defined as social protection provided in the form of tax breaks that would be defined as social protection benefits if they were provided in cash, excluding tax breaks promoting the provision of social protection or promoting private insurance plans.

According to a 2015 decision of the Working Group on Social Protection Statistics, fiscal benefits in the form of payable tax credits should be included in full (both cash component plus fiscal component) in the ESSPROS Core System while all other forms of fiscal benefit are excluded from the Core System and dealt with in the NET modules (the existing restricted approach module and the planned enlarged approach modules).

More information is available in the Annex I "Payable tax credits".

Data on pension beneficiaries correspond to the collection "PENS" that contains the table "spr_pns_ben" (Pension Beneficiaries at 31 December of each year).

The data include the number of recipients of one or more periodic cash benefits under a social protection scheme falling within seven pension categories grouped into four basic functions.

The seven categories of pensions in this module are:

• Disability pension;
• Early retirement benefit due to reduced capacity to work;
• Old-age pension;
• Anticipated old-age pension;
• Partial pension;
• Survivor's pension;
• Early retirement benefit due to labour market reasons.

The four functions of the module are:

• Disability (grouping the first two categories of pensions above);
• Old age (grouping the next three);
• Survivors (the sixth category);
• Unemployment (the last category).

The data, available by gender for the total of schemes, are expressed in "units".

ESSPROS data on pension beneficiaries may represent aggregates of multiple types of pensions, granted for various purposes, under different conditions, and to different groups with different levels of entitlement. It is essential, therefore, that users understand exactly what the data show and can interpret the figures correctly. In view of this, please see the publication "Social protection statistics - pension expenditure and pension beneficiaries" and more specifically the section "interpretation of data".

Data on Unemployment benefits recipients correspond to the collection “UBR” that contains the table “spr_ubr_ben”.

The data include the number of recipients of one or more periodic cash benefits as classified under the unemployment function:

• Full unemployment;
• Partial

Le taux d’activité exprime le rapport à la population d’âge actif (15 à 64 ans) des personnes qui se présentent effectivement sur le marché du travail, qu’elles soient occupées ou chômeuses. Ce taux traduit donc un comportement par rapport au marché du travail comportement qui est lui-même fonction d’un nombre considérable de variables tenant autant à l’individu, à sa famille et à sa culture, qu’au contexte économique et institutionnel dans lequel il évolue. Voir aussi : - sur notre site ’Statistiques – Marché du travail', Les comptes de l’emploi et le Working Paper de l’IWEPS n°13. Note : A partir de 2011, les indicateurs sont calculés sur la base des estimations du Steunpunt Werk, marquées par une rupture de série en 2017 : la méthode d'estimation des étudiants non assujettis est modifiée et les salariés d'organismes internationaux ont été intégrés aux actifs occupés. En 2019, la source utilisée par le Steunpunt Werk pour le nombre de frontaliers sortants change, ce qui entraine une baisse de l’emploi, donc aussi de l’activité, et une hausse du taux de chômage qui peuvent être importantes dans certaines communes frontalières. Suite à ce problème et au retard des estimations du Steunpunt Werk du à la difficulté croissante d’obtenir des données suffisamment détaillées sur les travailleurs salariés, à partir de 2019, les indicateurs sont calculés sur la base d’estimations provisoires de l’IWEPS. Plus d'informations sur le site de l'IWEPS : - la "\2" - les "\2" - les statistiques du marché du travail

The database contains continuous chronological series of the main indicators of dynamics for the US economy in 1950-1996 and the results of a preliminary approximation of the corresponding analytical trends up to 2010. The database includes the values of GNP and GDP in the current and fixed prices, price deflators, shares of various industry groups in the structure of the domestic product, indicators of the dynamics for the total national income, values of exports and imports of goods, population data, indicators of general and sectoral employment and unemployment, basic indices of values for intermediate and final products in material production, the current volumes of capital investments, basic indices of production costs and consumer prices, as well as indicators of the national wealth of the United States. Particular attention was paid to inflation rates, the growth of military spending, the dynamics of public debt and such derived socio-economic indicators as the values of the total national product, income and wealth per capita. Due to some ongoing revisions to the US System of National Accounts (NIPA) introduced by the Bureau of Economic Analysis of the US Department of Commerce, all series have been updated to reflect the President's Economic Report of 1997. All the given series of indicators were verified with primary data sources and provided with reference linear charts of statistical trends. The basis for compiling the database was the official reference publications of the US federal departments, as well as statistical materials accumulated and processed in the Section of Economic Databases at the Institute for the USA and Canada of the Russian Academy of Sciences in 1985-1997.

Abstract The purpose of this study is to show that, in both the Hansen and Summers versions, the secular stagnation hypothesis is based on questionable theoretical foundations that seem inconsistent both with the neoclassical approach to growth and with the heterodox models of demand-led growth. We build on F. Petri´s recent critique of the incoherent use of the neoclassical investment function in situations with persistent labor unemployment, and show that the recent debate is directly connected with the old debate about the two problems raised by R. Harrod. The analysis allows us to understand some questions related to the current debate, such as the Gordon-Summers controversy on whether the causes of the recent stagnation tendencies in developed countries should be attributed to supply or demand factors.

This paper provides an empirical investigation of the wage, price and unemployment dynamics that have taken place in Spain during the last two decades. The aim of this paper is to shed light on the impact of the European economic integration on Spanish labour market and the convergence to a European level of prosperity. We found that the Balassa-Samuelson effect, product market competition, and capital liberalization have been the main driving forces in this period. The adjustment dynamics show that Spanish inflation has adjusted in the long run to the European purchasi ng power parity level (as measured by the German price level) corrected for the Balassa-Samuelson effect. In the medium run this long-run convergence was achieved by two types of Phillips curve mechanisms; one where the inflation/unemployment trade-off was triggered off for different levels of the interest rate and real wage costs, another one where the trade-off was a function of the real exchange rate and the interest rate. Excess wages and/or increasing cost levels in the tradable secto r led to higher unemployment rather than higher prices. Thus, much of the burden of adjustment was carried by unemployment in this period.

Abstract

The 2011 Third Quarter Labour Force Survey aims to collect information on the supply side of the labour market. It provides information on the extent of available and unused labour time and on relationships between employment and income. Thus, the data collected can be used for:

Macro-economic monitoring:- from an economic point of view, a main objective of collecting data on the economically active population is to provide basic information on the size and structure of a country's workforce. The unemployment rate in particular is widely used as an overall indicator of the current performance of a country's economy.

Human resources development: The economy is changing all the time. In order to meet the needs of the changing economy, people need to be trained. These areas of training must therefore be identified.

Employment policies: For an economy to work at its maximum potential, all persons wanting to have work should have jobs. Some persons may wish to have full-time jobs, and can only find part-time work. We need to know what proportion of the labour force these people represent in order to assess the social effects of government employment policies.

Income Support and social programmes: For the majority of people, employment income is their main means of support. People need not only jobs, but more importantly, productive jobs in order to receive reasonable incomes. We need to know what levels of income are being earned by different groups of persons.

Geographic coverage

National Coverage

Analysis unit

• Households;
• Individuals.

Universe

The survey covered all de jure non-institutional household members (usual residents), it focuses on the employment, unemployment and current activity or inactivity status of all persons aged 15 years and over resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

Every quarter (three months) approximately 1,000 households are interviewed, there is a one third overlap between the households interviewed between each round of the survey.

The Multi-Stage sampling procedure developed for the St. Lucia MS (Master Sample) Frame is used for the execution of the labour force survey:

The two stage process of sample selection in the ST. LUCIA MS entails the selection of the PSUs within the districts. This is followed by the systematic selection of the cluster of households or USU (Ultimate Sampling Units) within the selected PSUs. The two stages in the design is elaborated as follows:

a. In the first stage, a sampling frame is constructed consisting of all of the enumeration districts from the census of 2001. The size of each enumeration district is measured in units of clusters of households. In the case of the ST. LUCIA MS, approximately seven or eight households were allocated per cluster. The clusters which are allocated to the EDs all have an equal probability of selection within the specified geographic domain in which they are allocated. In addition, the number of clusters allocated to an ED is a measure of the size of the ED. Clusters, therefore ensure the selection of EDs or Primary Sampling Units with probability proportional to the size of the ED. The ST. LUCIA MS frame consists of nine sub-samples / replicates, with each replicate selected with a probability of (1 / (16 * 9)) or 1 / 144.

b. In the second stage a non-compact cluster of households is selected within the selected PSU using systematic random sampling. There are three elements to the selection of this non-compact cluster. Firstly, there is the sample interval, which is a measure of the size of the ED in terms of the total number of households it contains. The larger the ED or PSU the larger will be the sample interval assigned and consequently the larger will be the number of clusters assigned to the ED. This approach ensures that the total number of households selected in any selected ED is approximately the same. In the case of the "Castries" in the ST. LUCIA MS frame the approximate number is five (5). Secondly, the random start is determined by use of a random number generator. With a Microsoft EXCEL spreadsheet the formulae takes the following form, =ROUND(RAND()*E1,0)+1, where E1 is the cell containing the sample interval (or total number of clusters assigned) RAND() is the function which generates the random number. The round() function is used to round the result to the nearest whole number. The third element of choosing the non compact cluster is a combination of the above. A random number (r) is choosen between 1 and the sample interval value, I, inclusive, then to this number is added the sample interval for the full list of households within the primary sample unit. Thus, the list of selected households would be r, r + I, r + 2I, r + 3I, r + 4I,……, r + (n - 1)I, where n is the cluster size assigned to the district, in the case of Castries n is five.

A. Size of the Sample

As has been explained before the decision to use a sampling fraction of 1 : 16 and to assign nine replicates to each District (the geographic domain) was based on the need to take advantage of the small size of the countries covered by this MECOVI project. This was done by increasing the "spread" of the sample across EDs and as a result improving the precision of the estimates which can be obtained from it. In addition, attention was paid to ensuring that were the CSO of ST. LUCIA to consider developing further its Integrated Household Survey Programme, the ground work would have been laid through this Master Sample Frame design for periodic, ad hoc or continuous sample surveys. The achievement of this objective has already been demonstrated through the use of this Sample Frame in the conduct of St. Lucia's continuous Labour Force Survey.

Therefore for any one sub-sample given that there are nine, the sampling fraction is 1 / 16 by 1 / 9 or 1 / 144. If a periodic, ad hoc or quarterly survey included the use of three replicates then the sampling fraction for these three replicates would be 3 / 144 or 1 /16 by 3 / 9. In both cases the resultant sampling fraction is the product of the sampling probability for the Master Sampling frame and the probability of selection of a specific number of replicates.

B. Master Sample Domains of Study and Stratification

1. Domains of Study:

The Master Sample frame was subdivided into eleven areas for the purpose of the provision of estimates from samples selected from this frame. The following list of the ten domains or sub-populations is based on the Districts which formed the basis for the collection of information on the population in the 2001 Census.

The total number of PSUs in the ST. LUCIA MS is 401, a breakdown of the number of PSUs by District is shown in the table above. The average size of the PSUs was 118 approximately with a standard deviation of approximately 47. This configuration does not in the near term present a major problem for sample implementation, since the EDs/PSUs size does not exceed 100 by too great an extent, in addition, while consideration must be given to splitting EDs which have grown in size to over 200, there are not as exist in the case of St. Vincent and the Grenadines a significant number of excessively large EDs. Continuous maintenance of this situation is required and can be done by splitting all EDs over 200 in size into smaller ones of approximate size 100. The main objective of controlling the size of the PSUs, is to reduce variability and thereby improve the precision of estimates from the sample. The more equal the sizes of the PSUs the more likely the variance of characteristics between PSUs will be minimized and inversely the precision of the samples derived from the estimates from the Master Sample Frame increased.

1. Stratification

As shown in the table above each of the domains of study was stratified according to specific criteria. In the more urban domains the criteria used was the percentage of Managers, professional, sub-professionals in the population. The PSUs or EDs were therefore arranged in descending order of the proportion of this group in the population of the ED. In the rural domains the PSUs were arranged in descending order of the proportion of agriculture workers in the population of the ED. In the case of Canaries and Anse-la-Raye, the sizes of the populations in these domains mandated a joining of the two to allow for the creation of a large enough domain for reporting purposes.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire is administered to all members of the household. Questions 1 through 6 are to be completed for all members of the household, these questions cover age, sex, relation to head of household, country of birth etc. All subsequent questions refer to persons 15 year of age and older. The questionnaire is divided into five parts:

PART 1:For all members of the household (regardless of age) - Demographic and emigration questions
PART 2: To be completed for persons 15 years and older - Education, Training, activities during the reference week or month, working at a job, on vacation, methods of seeking work, availability for employment PART 3: For persons employed during the reference week - Number of actual hours of work, number of usual hours of work, seeking additional work, status in employment, industry and occupation of employment PART 3A: For persons holding more than one job during the reference week - Number of actual hours of work, number of usual hours of work, seeking additional work, status in employment, industry and occupation of employment
PART 4: For persons unemployed

Judgement on the problems of unemployment and the function of occupation and job from of the perspective of unemployed and employed women as well as among housewives.

Topics: reasons and length of unemployment; work orientation; roll of occupation versus roll of housewife; functions of occupation and job; burdens from unemployment; circle of friends; assessment of social situation; future expectations; personality structure; responsibility for unemployment; political conduct; party ties; trust in the political parties; life style; family; leisure time; partner conflicts; conflicts in child-raising; sport engagement; assessment of the situation in the job market; willingness to make concessions; mobility; activities for re-employment; experiences with the employment office.

Demography: age; marital status; school and occupational training; household structure; further education; employment of mother and head of household; net income; supports; employment; interest in politics; party preference; political discussions; party membership.

Also encoded were: size of municipality class; date of interview; length of interview; cooperation of respondent.