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The purpose of this paper is to propose a framework of an automatic bidirectional matching system that measures the degree of semantic similarity of job-seeker qualifications and skills, against the vacancy provided by employers or job-agents.

The paper presents a framework of bidirectional jobseeker-to-vacancy matching system. Using occupational data from various sources such as the WageIndicator web survey, International Standard Classification of Occupations, European Skills, Competences, Qualifications, and Occupations as well as vacancy data from various open access internet sources and job seekers information from social networking sites, the authors apply machine learning techniques for bidirectional matching of job vacancies and occupational standards to enhance the contents of job vacancies and job seekers profiles. The authors also apply bidirectional matching of job seeker profiles and vacancies, i.e., semantic matching vacancies to job seekers and vice versa in the individual level. Moreover, data from occupational standards and social networks were utilized to enhance the relevance (i.e. degree of similarity) of job vacancies and job seekers, respectively.

The paper provides empirical insights of increase in job vacancy advertisements on the selected jobs – Internet of Things – with respect to other job vacancies, and identifies the evolution of job profiles and its effect on job vacancies announcements in the era of Industry 4.0. In addition, the paper shows the gap between job seeker interests and available jobs in the selected job area.

Due to limited data about jobseekers, the research results may not guarantee high quality of recommendation and maturity of matching results. Therefore, further research is required to test if the proposed system works for other domains as well as more diverse data sets.

The paper demonstrates how online jobseeker-to-vacancy matching can be improved by use of semantic technology and the integration of occupational standards, web survey data, and social networking data into user profile collection and matching.

Di Tella et al. (2001) show that temporary fluctuations in life satisfaction (LS) are correlated with macroeconomic circumstances such as gross domestic product, unemployment and inflation. In this paper, we bring attention to labour market measures from search and matching models (Pissarides 2000).

Our analysis follows the two-stage estimation strategy used in Di Tella et al. (2001) to explore sectoral unemployment levels, labour market tightness and matching efficiency as LS determinants. In the first stage, we use a large sample of individual data collected from a continuous web survey during the 2007–2014 period in the Netherlands to obtain regression-adjusted measures of LS by quarter and economic sector. In the second-stage, we regress LS measures against the unemployment level, labour market tightness and matching efficiency.

Our results are threefold. First, the negative link between unemployment and an employee's LS is confirmed at the sectoral level. Second, labour market tightness, measured as the number of vacancies per job-seeker rather than the number of vacancies per unemployed, is shown to be relevant to the LS of workers. Third, labour market matching efficiency affects the LS of workers differently when they are less satisfied with their job and in temporary employment.

No evidence of this relationship has been documented before. Our results give support to government interventions aimed at activating demand for labour, improving the matching of job-seekers to vacant jobs and reducing information frictions by supporting match-making technologies.

The purpose of this paper is to overcome the problems that skill mismatch cannot be measured directly and that demand side data are lacking. It relates demand and supply side characteristics by aggregating data from jobs ads and jobholders into occupations. For these occupations skill mismatch is investigated by focussing on demand and supply ratios, attained vis-à-vis required skills and vacancies’ skill requirements in relation to the demand-supply ratios.

Vacancy data from the EURES job portal and jobholder data from WageIndicator web-survey were aggregated by ISCO 4-digit occupations and merged in a database with 279 occupations for Czech Republic, being the only European country with disaggregated occupational data, coded educational data, and sufficient numbers of observations.

One fourth of occupations are in excessive demand and one third in excessive supply. The workforce is overeducated compared to the vacancies’ requirements. A high demand correlates with lower educational requirements. At lower occupational skill levels requirements are more condensed, but attainments less so. At higher skill levels, requirements are less condensed, but attainments more so. Educational requirements are lower for high demand occupations.

Using educational levels is a limited proxy for multidimensional skills. Higher educated jobholders are overrepresented.

In Europe labour market mismatches worry policy makers and Public Employment Services alike.

The authors study is the first for Europe to explore such a granulated approach of skill mismatch.

The purpose of this paper is to explore the matching process before and after the Great Recession in the Netherlands. The Dutch case is interesting because it is characterised by increasing matching efficiency.

This paper uses data from 2001 to 2014 to study the Dutch labour market matching process accounting for the three labour market states and their heterogeneities.

The elasticity of hires with respect to the short-term employed was significant, positive and countercyclical, while elasticities relating to new entrants were procyclical. The matching function (MF) displays constant returns to scale (CRTS) when using an alternative labour supply (LS) measure that includes the short-term employed as jobseekers. The findings are at odds with the idea of mismatch and a shortage of skills. Search frictions for employers were lower and vacancies were filled faster. This can be related to the fact that in a loose labour market context with increasing short-term employment, employers increase their hiring of employed workers which generates negative externalities on unemployed.

The implications concern the specification of the MF and the CRTS assumption when using unemployment as a LS measure.

The purpose of this article is to evaluate a method for measuring work activities and skill requirements of 160 occupations in eight countries, used in EurOccupations, an EU‐FP6 project. Additionally, it aims to explore how the internet can be used for measuring work activities and skill requirements.

For the 160 occupations, work activities were described in approximately ten tasks. Occupational experts and jobholders were invited to rate these tasks and to indicate the skill requirements, using a multilingual web‐survey. Experts were recruited through the networks of the project partners and jobholders through frequently visited websites in the eight countries. The effectiveness of the drafting of tasks descriptions, the recruitment of raters, and the measurement of skill requirements is evaluated.

The project showed that tasks descriptions for a wide range of occupations and countries can be drafted relatively easy, using desk research. Conducting a web‐survey with a routing for 160 occupations and eight countries is viable. Recruiting experts used more resources than recruiting jobholders using the internet. Measuring skill requirements would need much more resources due to major variations within and across countries.

The article addresses a number of areas that are potentially worthy of further empirical investigations for a Europe‐wide library of occupational titles, work activities and skill requirements.

The paper outlines the potential of a future method for a European library of work activities and skill requirements for occupational titles, thereby facilitating European industrial training efforts.

Insight in the work activities and skill requirements of occupations will facilitate labour mobility and related training across EU member states.

This paper explores the potential for a Europe‐wide empirical underpinning of work activities and skill requirements, using a web‐survey and the internet.

The purpose of this paper is to fill several knowledge gaps regarding the contents of collective agreements, using a new online database. The authors analyse 249 collective agreements from 11 countries – Benin, Brazil, Ghana, Indonesia, Kenya, Madagascar, Peru, Senegal, Tanzania, Togo, Uganda. The authors research to what extent wage and other remuneration-related clauses, working hours, paid leave arrangements and work-family arrangements are included in collective agreements and whether bargaining topics cluster within agreements.

The authors use the web-based WageIndicator Collective Bargaining Agreement Database with uniformly coded agreements, that are both collected and made accessible online. The authors present a quantitative multi-country comparison of the inclusion and contents of the clauses in the agreements.

The authors find that 98 per cent of the collective agreements include clauses on wages, but that only few agreements specify wage levels. Up to 71 per cent have clauses on social security, 89 per cent on working hours and 84 per cent of work-family arrangements. The authors also find that collective agreements including one of these four clauses, are also more likely to include the other three and conclude that no trade off exists between their inclusion on the bargaining agenda.

Being one of the first multi-country analyses of collective agreements, the analysis is primarily explorative, aiming to establish a factual baseline with regard to the contents of collective agreements.

This study is unique because of its focus on the content of collective bargaining agreements. The authors are the first to be able to show empirically which clauses are included in existing collective agreements in developing countries.

Do similar job titles refer to the same work activities, as assumed often, but hardly empirically tested? The purpose of this paper is to analyze the similarity of 160 occupations within and across eight European countries using interrater agreement statistics (rWG).

Using a multilingual web survey, experts and jobholders in the eight countries rated the frequency of ten tasks per occupation they had knowledge of (n=4,197 ratings). Three hypotheses are investigated: first, interrater agreements of occupations are similar regardless the country; second, interrater agreements of occupations are similar within countries; and third, experts and jobholders are similar in their ratings.

Half of the occupations reveal no agreement across ratings, one-third shows a weak/moderate agreement and one in ten shows a strong agreement. H1 is rejected for task frequency but not for task importance. Within-country similarity of occupations is larger than across-country similarity. H2 is supported for two countries and rejected for two other countries. H3 is not supported. Jobholders demonstrate higher agreement than experts.

An empirical testing of occupation-specific tasks for a wide range of occupations across Europe seems a viable approach.

The paper contributes to the challenges related to labor market mobility across borders.

Work tasks for a wide range of occupations and countries, using job-specific work activities in combination with web surveys and the internet for recruitment of jobholders, have not been used before.

The purpose of this paper is to examine gender differences in promotion probabilities of the academic staff of a large university in The Netherlands, taking into account the sex segregated context of the faculty.

The study uses records of the university's personnel information system from 1990 to 2006, covering the data of 1,792 employees in the academic ranks who have entered since 1990. Cox regression models are used to test three hypotheses.

The findings show that women have lower promotion probabilities than men. The gender differences are primarily explained by differences in years of service and external mobility, and not by the sex segregated context of the faculty. A higher share of women decreases the odds of being promoted for both men and women. Gender differences in working hours do not explain the gender differences in promotion probabilities.

The paper adds to the existing literature because event history analyses have hardly been applied to personnel records for investigating the impact of the sex segregated context on promotion probabilities.

The purpose of this paper is to recommend the use of internet data for social sciences with a special focus on human resources issues. It discusses the potentials and challenges of internet data for social sciences. The authors present a selection of the relevant literature to establish the wide spectrum of topics, which can be reached with this type of data, and link them to the papers in this International Journal of Manpower special issue.

Internet data are increasingly representing a large part of everyday life, which cannot be measured otherwise. The information is timely, perhaps even daily following the factual process. It typically involves large numbers of observations and allows for flexible conceptual forms and experimental settings.

Internet data can successfully be applied to a very wide range of human resource issues including forecasting (e.g. of unemployment, consumption goods, tourism, festival winners and the like), nowcasting (obtaining relevant information much earlier than through traditional data collection techniques), detecting health issues and well-being (e.g. flu, malaise and ill-being during economic crises), documenting the matching process in various parts of individual life (e.g. jobs, partnership, shopping), and measuring complex processes where traditional data have known deficits (e.g. international migration, collective bargaining agreements in developing countries). Major problems in data analysis are still unsolved and more research on data reliability is needed.

The data in the reviewed literature are unexplored and underused and the methods available are confronted with known and new challenges. Current research is highly original but also exploratory and premature.

The paper reviews the current attempts in the literature to incorporate internet data into the mainstream of scholarly empirical research and guides the reader through this Special Issue. The authors provide some insights and a brief overview of the current state of research.

The purpose of this paper is to explore the role of work conditions and job characteristics with respect to three subjective well-being (SWB) indicators: life satisfaction, job satisfaction and satisfaction with work-life balance. From a methodological point of view, the paper shows how social sciences can benefit from the use of voluntary web survey data.

The paper makes use of a large sample of individual data obtained from voluntary web surveys collected as part of the WageIndicator project. The sample includes extensive information on the quality of working conditions together with different well-being indicators. The propensity score adjustment weights are used to improve the sample performance.

The results shed light on the importance of certain job characteristics not only in determining job satisfaction, but also in other SWB domains. The findings support the theory of spillover perspectives, according to which satisfaction in one domain affects other domains.

As a voluntary web-survey, WageIndicator is affected by selection bias. The validity of the sample can be improved by weighting, but this adjustment should be made and tested on a country-by-country basis.

The paper provides analysis of the quality of a web survey not commonly used in happiness research. The subsequent presentation of the effects of working conditions on several satisfaction domains represents a contribution to the literature.