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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 investigate the extent to which the authors can use internet search data in order to capture the impact of the 2008 Financial and Economic Crisis on well-being.

The authors look at the G8 countries with a special focus on USA and Germany and investigate whether internet searches reflect the “malaise” caused by the crisis. The authors focus on searches that contain the word “symptoms” and are thought to proxy self-diagnosis and those that contain “side effects” and are thought to proxy treatment.

The authors find that “malaise” searches spike in a fashion coincident with the crisis and its contagion timeline across the G8 countries. The authors show that results based on search recover previously known stylized facts from the economics of health, well-being and the business cycle.

Internet penetration is high across the G8 countries. The authors nonetheless cannot get a good handle on the part of the population, which is not online. Moreover the authors cannot get a good grip on all confounding factors. More research would be necessary with access to search microdata.

The authors propose global proxies for diagnosis and treatment based on the “search buzz” for symptoms and side effects. The authors can thus capture trends on a global scale. This approach will become increasingly important.

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.

The purpose of this paper is to improve the forecast of tourism inflows into Spain by use of Google – indices on internet searches measuring the relative popularity of keywords associated with travelling to Spain.

Two models are estimated for each of the three countries with the largest tourist flows into Spain (Germany, UK and France): a conventional model, the best ARIMA model estimated by TRAMO (model 0) and a model augmented with the Google-index relating to searches made from each country (model 1). The overall performance of both models is compared.

The improvement in forecasting provided by the short-term models that include the G-indicator is quite substantial up to 2012, reducing out of sample mean square errors by 42 per cent, although their performance worsens in the following years.

Deeper study and conceptualization of sources of error in Google trends and data quality is necessary.

The paper illustrates that while this new tool can be a powerful instrument for policy makers as a valuable and timely complement for traditional statistics, further research and better access to data is needed to better understand how internet consumers’ search activities translate (or not) into actual economic outcomes.

The purpose of this paper is to explore how both gender and facial attractiveness affect job candidates’ chances of obtaining interviews in China’s dynamic internet job board labor market. It examines how discrimination based on these attributes varies over occupation, location, and firms’ ownership type and size.

The authors carry out a resume audit (correspondence) study. Resumes of fictitious applicants are first carefully crafted to make candidates appear equally productive in terms of their work histories and educational backgrounds. The authors control gender and facial attractiveness. The authors establish the facial attractiveness of candidate photos via an online survey. In total, 24,192 applications are submitted to 12,096 job postings across four occupations in six Chinese cities. Callbacks are carefully tracked and recorded. Discrimination is estimated by calculating the differences in the rates of callbacks for interviews received by individuals whose applications vary only in terms of facial attractiveness and gender. The authors reuse the same resumes repeatedly through this project such that names and photos of each of the candidates: attractive man, attractive woman, unattractive man, and unattractive woman is attached to each resume hundreds of times for each occupation in each city.

The authors find sizable differences in the interview callback rates of attractive and unattractive job candidates. Job candidates with unattractive faces need to put in 33 percent more applications than their attractive counterparts to obtain the same number of interview callbacks. Women are preferred to men in three of the four occupations. Women, on average need put in only 91 percent as many applications as men to obtain the same number of interview callbacks.

The analysis of this paper focusses on only four different occupations. Its scope is also limited to exploring only the first part of the hiring process – obtaining a job interview. Furthermore, its fictitious applicants are all young people, approximately 25 years old. It would be useful to explore how gender and facial attractiveness affect candidates’ chances of landing a job after getting an interview.

This paper contributes to and expands the literature on hiring through China’s internet job boards. It also contributes to the literature on the role of facial attractiveness in hiring.

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.

Internet data hold many promises for demographic research, but come with severe drawbacks due to several types of bias. The purpose of this paper is to review the literature that uses internet data for demographic studies and presents a general framework for addressing the problem of selection bias in non-representative samples.

The authors propose two main approaches to reduce bias. When ground truth data are available, the authors suggest a method that relies on calibration of the online data against reliable official statistics. When no ground truth data are available, the authors propose a difference in differences approach to evaluate relative trends.

The authors offer a generalization of existing techniques. Although there is not a definite answer to the question of whether statistical inference can be made from non-representative samples, the authors show that, when certain assumptions are met, the authors can extract signal from noisy and biased data.

The methods are sensitive to a number of assumptions. These include some regularities in the way the bias changes across different locations, different demographic groups and between time steps. The assumptions that we discuss might not always hold. In particular, the scenario where bias varies in an unpredictable manner and, at the same time, there is no “ground truth” available to continuously calibrate the model, remains challenging and beyond the scope of this paper.

The paper combines a critical review of existing substantive and methodological literature with a generalization of prior techniques. It intends to provide a fresh perspective on the issue and to stimulate the methodological discussion among social scientists.