The employment rate and household consumption are two indicators that are directly related. In this statistic, the year-on-year employment and household consumption variation in Spain are compared across the fourth quarter from 2015 to 2023. In the last quarter of 2023, the YoY employment rate in Spain amounted to 3.8 percent, while the household consumption rate indicated an inter-annual change of 2.3 percent.

Retail Trade Indices: Variation coefficient of the annual employment rate. Monthly. Autonomous Communities and Cities.

Table of INEBase Variation coefficient of the annual employment rate. Monthly. National. Retail Trade Indices

Employment rate by year, quarter and age class. Percentage value and cyclical variation (seasonally adjusted data) and trend (raw data). Year 2016, Tav. 4, Fourth Quarterly Note on Employment.

This statistic displays the variation rate of journalists looking for employment in Spain in 2018, by Autonomous Community. According to the source, Aragon registered an increase of 12.5 percent of journalists who actively sought. In other communities such as Castile-La Mancha and Murcia, the number of job seekers decreased 7 percent.

This graph presents the annual employment variation in France in 2017, distributed by region. The statistic displays that the employment growth rate was of 1.8 percent in Brittany and 0.1 percent in Normandy in 2017.

This statistic displays the annual variation rate of the number of journalists who actively looked for a job in Spain in 2018, by Autonomous Community and gender. In Cantabria, job demanders registered a decrease of 28 percent among women journalists. While in Asturias, the same number increased 11.6 percent when compared with 2017.

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

Retail Trade Indices: Coefficient of variation of the annual rate of the general national employment index. Monthly. National.

During the first quarter of 2024, the employment level in Latin America and the Caribbean fell through all branches of economic activity when compared to the same quarter of the previous year. In total, employment fell 1.9 percent. Community, social and personal services was the sector that experienced the largest fall in employment, reaching almost a three percent decline.

Number and percentage of residents aged 16-64 who are in employment by sex (000's) (Seasonally adjusted), for rolling quarters since 1992 by region and country.

The figures in this dataset are adjusted to compensate for seasonal variations in employment. Figures are released every month for rolling quarters. Data from ONS Table HI00.

The data are taken from the Labour Force Survey and Annual Population Survey, produced by the Office for National Statistics.

Click here to visit the Regional labour market statistics pages from the Office for National Statistics

There are notable differences in the employment rates between people with different original backgrounds. While the employment rate among the population with Danish origins was 79 percent, it was only 63 percent among non-western immigrants in 2021. There are also major variations in the unemployment rates of the foreign-born population in Denmark.

Unemployment rate is the number of unemployed persons as a percent of the labor force. The Bureau of Labor Statistics produces industry estimates of nonfarm payroll employment as part of the Current Population Survey. Employment data are seasonally adjusted to remove the effects of normal seasonal variation.

Abstract copyright UK Data Service and data collection copyright owner.The Skills Survey is a series of nationally representative sample surveys of individuals in employment aged 20-60 years old (since 2006, the surveys have additionally sampled those aged 61-65). The surveys aim to investigate the employed workforce in Great Britain. Although they were not originally planned as part of a series and had different funding sources and objectives, continuity in questionnaire design has meant the surveys now provide a unique, national representative picture of change in British workplaces as reported by individual job holders. This allows analysts to examine how various aspects of job quality and skill levels have changed over 30 years.The first surveys in the series were carried out in 1986 and 1992. These surveys also form part of this integrated data series, and are known as the Social Change and Economic Life Initiative (SCELI) and Employment in Britain (EIB) studies respectively.The 1997 survey was the first to collect primarily data on skills using the job requirements approach. This focused on collecting data on objective indicators of job skill as reported by respondents. The 2001 survey assessed how much had changed between the two surveys and a third survey in 2006 enhanced the time series data, while providing a resource for analysing skill and job requirements in the British economy at that time. The 2012 survey aimed to again add to the time series data and, coinciding as it did with a period of economic recession, to provide insight into whether workers in Britain felt under additional pressure/demand from employers as a result of redundancies and cut backs. In addition, a series dataset, covering 1986, 1992, 1997, 2001, 2006 and 2012 is also available . A follow-up to the 2012 survey was conducted in 2014, revisiting respondents who had agreed to be interviewed again. The 2017 survey was the seventh in the series, designed to examine to what extent pressures had continued as a result of austerity and economic uncertainties triggered, for example, by Brexit as well as examining additional issues such as productivity, fairness at work and the retirement intentions of older workers.Each survey comprises a large number of respondents: 4,047 in the 1986 survey; 3,855 in 1992; 2,467 in 1997; 4,470 in 2001; 7,787 in 2006; 3,200 in 2012; and 3,306 in 2017. The Skills and Employment Surveys Series Dataset, 1986, 1992, 1997, 2001, 2006, 2012 and 2017: Special Licence Access combines data from all seven surveys in the series, where common survey questions were asked. For each survey, weights are computed to take into account the differential probabilities of sample selection, the over-sampling of certain areas and some small response rate variations between groups (defined by sex, age and occupation). All surveys cover Great Britain except the Skills Survey, 2006 which covers the United Kingdom. The six surveys are all available separately from the UK Data Archive: Social Change and Economic Life Initiative Surveys, 1986-1987 (SN 2798)Employment in Britain 1992 (SN 5368)Skills Survey 1997 (SN 3993)Skills Survey 2001 (SN 4972)Skills Survey 2006 (SN 6004)Skills and Employment Survey 2012 (SN 7466 and 7467)Skills and Employment Survey 2017 (SNs 8580 and 8581) This Special Licence access version of this study includes finer detailed geographical variables (notably TTWA) than is available in the general release dataset (SN 8589).An earlier Skills and Employment Surveys Series Dataset, covering 1986, 1992, 1997, 2001, 2006 and 2012 is available under SN 7467.

Access the Data HereWhat is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.

Abstract: The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y). https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15100277%2F173368c0e01d219e35c1de9ba3f1bfd8%2F-------.JPG?generation=1684180716985041&alt=media" alt="">

Attribute Information:

Input variables: -> bank client data: 1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown') -> related with the last contact of the current campaign: 8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

->0ther attributes: 12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

->Social and economic context attributes 16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric)

->Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')

Relevant Tasks;

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15100277%2F45abc3004ad8be49e4b43f9f92de47da%2FFE.png?generation=1684181245860633&alt=media" alt="">

Relevant Papers;

What is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).This updated dataset contains 116 jobs attributes including the 10 most likely jobs to be impacted for each county, the total employment and employment by sector. An attribute list is included below.An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.Attribute ListFIPSCounty NameStateTotal JobsWhite Collar JobsBlue Collar JobsService JobsWhite Collar %Blue Collar %Service %Government JobsGovernment %Primarily Self-Employed JobsPrimarily Self-Employed %Job Change, Last Ten YearsIndustry 1 NameIndustry 1 EmplIndustry 1 %Industry 2 NameIndustry 2 EmplIndustry 2 %Industry 3 NameIndustry 3 EmplIndustry 3 %Industry 4 NameIndustry 4 EmplIndustry 4 %Industry 5 NameIndustry 5 EmplIndustry 5 %Industry 6 NameIndustry 6 EmplIndustry 6 %Industry 7 NameIndustry 7 EmplIndustry 7 %Industry 8 NameIndustry 8 EmplIndustry 8 %Industry 9 NameIndustry 9 EmplIndustry 9 %Industry 10 NameIndustry 10 EmplIndustry 10 %All Other IndustriesAll Other Industries EmplAll Other Industies %Agriculture, Food & Natural Resources EmplArchitecture and Construction EmplArts, A/V Technology & Communications EmplBusiness, Management & Administration EmplEducation & Training EmplFinance EmplGovernment & Public Administration EmplHealth Science EmplHospitality & Tourism EmplHuman Services EmplInformation Technology EmplLaw, Public Safety, Corrections & Security EmplManufacturing EmplMarketing, Sales & Service EmplScience, Technology, Engineering & Mathematics EmplTransportation, Distribution & Logistics EmplAgriculture, Food & Natural Resources %Architecture and Construction %Arts, A/V Technology & Communications %Business, Management & Administration %Education & Training %Finance %Government & Public Administration %Health Science %Hospitality & Tourism %Human Services %Information Technology %Law, Public Safety, Corrections & Security %Manufacturing %Marketing, Sales & Service %Science, Technology, Engineering & Mathematics %Transportation, Distribution & Logistics %COVID-19 Vulnerability IndexAverage Wages per WorkerAvg Wages Growth, Last Ten YearsUnemployment RateUnderemployment RatePrime-Age Labor Force Participation RateSkilled Career 1Skilled Career 1 EmplSkilled Career 1 Avg Ann WagesSkilled Career 2Skilled Career 2 EmplSkilled Career 2 Avg Ann WagesSkilled Career 3Skilled Career 3 EmplSkilled Career 3 Avg Ann WagesSkilled Career 4Skilled Career 4 EmplSkilled Career 4 Avg Ann WagesSkilled Career 5Skilled Career 5 EmplSkilled Career 5 Avg Ann WagesSkilled Career 6Skilled Career 6 EmplSkilled Career 6 Avg Ann WagesSkilled Career 7Skilled Career 7 EmplSkilled Career 7 Avg Ann WagesSkilled Career 8Skilled Career 8 EmplSkilled Career 8 Avg Ann WagesSkilled Career 9Skilled Career 9 EmplSkilled Career 9 Avg Ann WagesSkilled Career 10Skilled Career 10 EmplSkilled Career 10 Avg Ann Wages

Labor force participation rate is the percent of persons classified as employed or unemployed as a percent of the civilian noninstitutional population. The Bureau of Labor Statistics produces industry estimates of nonfarm payroll employment as part of the Current Population Survey. Employment data are seasonally adjusted to remove the effects of normal seasonal variation.

This study analyzes the employment effects of training in East Germany. We propose and apply an extension of the widely used conditional difference-in-differences estimator. Focusing on transition rates between nonemployment and employment, we take into account that employment is a state- and duration-dependent process. Our results show that using transition rates is more informative than using unconditional employment rates as commonly done in the literature. Moreover, the results indicate that due to the labor market turbulence during the East German transformation process the focus on labor market dynamics is important. Training as a first participation in a program of Active Labor Market Policies shows zero to positive effects both on re-employment probabilities and on probabilities of remaining employed with notable variation over the different start dates of the program.

Data Set Information:

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

There are four datasets: 1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] 2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). 4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM).

The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

Attribute Information:

Input variables:

bank client data:

1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown')

related with the last contact of the current campaign:

8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

other attributes:

12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

social and economic context attributes

16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric)

Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')

Relevant Papers:

S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimaraes, Portugal, October, 2011. EUROSIS. [bank.zip]