Data Set Description
The WageIndicator Survey is a continuous, multilingual, multi-country web-survey, counducted across 65 countries since 2000. The web-survey generates cross sectional and longitudinal data which might provide data especially about wages, benefits, working hours, working conditions and industrial relations.
The survey has detailed questions about earnings, benefits, working conditions, employment contracts and training, as well as questions about education, occupation, industry and household characteristics.
The WageIndicator Survey is a multilingual questionnaire and aims to collect information on wages and working conditions. As labour markets and wage setting processes vary across countries, country specific translations have been favoured over literal translations. The WageIndicator Survey includes regularly extra survey questions for project targeting specific countries, for specific groups or about specific events.
These projects usually address a specific audience (employees of a company, employees in an industry, readers of a magazine, members of a trade union or an occupational association, and alike). The data of the project questions are included in the dataset.
Non-Probability web based surveys are problematic because not every individual has the same probability of being selected into the survey. The probability of being selected depends on national or regional internet access rates and on numbers of visitors accessing the webiste. Data of such surveys form a convenience rather than a probability sample. Due to the non-probability based nature of the survey and its selectivity the obtained results cannot be generalized for the population of interest; i.e. the labor force.
Comparisons with representative studies found an underrepresentation of male labour force, part-timers, older age groups, and low educated persons.
Besides other strategies to reduce the bias the WageIndicators provides different weighting schemes in order to correct for selection bias.
The data is organised in annual releases. The data of the period 2000-2005 is released as one dataset. Each data release consists of a dataset with continuous variables and one with project variables. The continuous variables can be merged across years. All variable and value labels are in English. The data does not include the text variables and verbatims form open-ended survey questions, these are available in Excel-Format upon request.
The survey started in 2000 in the Netherlands. Since 2004, websites have been launched in many European countries, in North and South America and in countries in Asia. From 2008 on web sites have been launched in more African countries, as well as in Indonesia and in a number of post-Soviet countries.
For each country each, the questions have been translated. Multilingual countries employ multilingual questionnaires. Country-specific translations and locally accepted terminology have been favored over literal translations.
The target population of the WageIndicator is the labour force, that is, individuals in paid employment as well as job seekers. In addition to workers in formal dependent employment the survey aims to include apprentices, employers, own-account workers, freelancers, workers in family businesses, workers in the informal sector, unemployed workers, job seekers individuals who never had a job, as well as retired workers and housewifes school pupils or students with a job on the side and persons performing voluntary work.
The WageIndicator data is derived from a volunteer survey, inviting webvisitors to the national WageIndicator websites to complete the web-survey. Annually, the websites receive millions of web-visitors.
Scope of Data Set
Time Periods: 2000 - 2018
Tijdens, K.G. and P. Osse, WageIndicator continuous web-survey on work and wages. Amsterdam: University of Amsterdam/AIAS and Stichting Loonwijzer.
- Smyk, M., Tyrowicz, J., & Van der Velde, L. (2021). A cautionary note on the reliability of the online survey data: the case of wage indicator. Sociological Methods & Research, 50(1), 429-464.
- Andreadis, I., & Kartsounidou, E. (2020). The Impact of Splitting a Long Online Questionnaire on Data Quality. Survey Research Methods, 14(1), 31-42.
- Baiocco, S., Kilhoffer, Z., & Niang, Mouhamadou M. (2020). The Measurement of Skills Needs, Skills Transferability and Skills Imbalances with Data from International Surveys,Web Sources and Web-Based Surveys. Ingrid deliverable 12.1.
- Biavaschi, C., Burzyński, M., Elsner, B., & Machado, J. (2020). Taking the skill bias out of global migration. Journal of Development Economics, 142, 102317.
- de Pedraza, P., Guzi, M. & Tijdens, K. (2020). Life satisfaction of employees, labour market tightness and matching efficiency. International Journal of Manpower, Vol. ahead-of-print No. ahead-of-print.
- Fabo, B., & Kahanec, M. (2020). The role of computer skills on the occupation level. European Journal of Business Science and Technology, 87.
- Fabo, B. (2020). The English and Russian Language Proficiency Premium in the post-Maidan Ukraine–an Analysis of Web Survey Data. CELSI Discussion Paper No. 57.
- Smith, T. W. (2020). Optimizing Questionnaire Design in Cross‐National and Cross‐Cultural Surveys. Advances in Questionnaire Design, Development, Evaluation and Testing, 471-492.
- van Klaveren, M., & Tijdens, K. (2020). Social aspects of multinationals in the Netherlands. University of Amsterdam.
- Vontroba, J., Balcar, J., & Šimek, M. (2020). Commuting pays off: Evidence on wage returns to inter-urban and intra-urban commuting. Moravian Geographical Reports, 28(2) 112, 28(2): 112–123
- de Coning, J. A., Rothmann, S., & Stander, M. W. (2019). Do wage and wage satisfaction compensate for the effects of a dissatisfying job on life satisfaction?. SA Journal of Industrial Psychology, 45(1), 1-11.
- de Pedraza, P., Visintin, S., Tijdens, K., & Kismihók, G. (2019). Survey vs scraped data: comparing time series properties of web and survey vacancy data. IZA Journal of Labor Economics, 8(1), 20190004.
- Shannon, G., Jansen, M., Williams, K., Cáceres, C., Motta, A., Odhiambo, A., ... & Mannell, J. (2019). Gender equality in science, medicine, and global health: where are we at and why does it matter?. The Lancet, 393(10171), 560-569.
- Chala, S. A., Ansari, F., Fathi, M., & Tijdens, K. (2018). Semantic matching of job seeker to vacancy: a bidirectional approach. International Journal of Manpower.
- Fabo, B., & Kahanec, M. (2018). Can a voluntary web survey be useful beyond explorative research?. International Journal of Social Research Methodology, 21(5), 591-601.
- Fabo, B., Beblavý, M., & Lenaerts, K. (2017). The importance of foreign language skills in the labour markets of Central and Eastern Europe: assessment based on data from online job portals. Empirica, 44(3), 487-508.
- de Vries, D. H., Steinmetz, S., & Tijdens, K. G. (2016). Does migration ‘pay off’for foreign-born migrant health workers? An exploratory analysis using the global WageIndicator dataset. Human resources for health, 14(1), 40.
- Tijdens, K., & Steinmetz, S. (2016). Is the web a promising tool for data collection in developing countries? An analysis of the sample bias of 10 web and face-to-face surveys from Africa, Asia, and South America. International Journal of Social Research Methodology, 19(4), 461-479.
- Askitas, N., Zimmermann, K. F., Guzi, M., & de Pedraza García, P. (2015). A web survey analysis of subjective well-being. International Journal of Manpower.
- Kureková, L. M., Beblavý, M., & Thum-Thysen, A. (2015). Using online vacancies and web surveys to analyse the labour market: a methodological inquiry. IZA Journal of Labor Economics, 4(1), 18.
- Tijdens, K. (2015). Self-identification of occupation in web surveys: requirements for search trees and look-up tables. Survey Methods: Insights from the Field, 11.
- Visintin, S., Tijdens, K., & van Klaveren, M. (2015). Skill mismatch among migrant workers: evidence from a large multi-country dataset. IZA Journal of Migration, 4(1), 14.
- Steinmetz, S., de Vries, D. H., & Tijdens, K. G. (2014). Should I stay or should I go? The impact of working time and wages on retention in the health workforce. Human resources for health, 12(1), 23.
- Tijdens, K. (2014). Dropout rates and response times of an occupation search tree in a web survey. Journal of Official Statistics, 30(1), 23-43.
- Tijdens, K., De Vries, D. H., & Steinmetz, S. (2013). Health workforce remuneration: comparing wage levels, ranking, and dispersion of 16 occupational groups in 20 countries. Human resources for health, 11(1), 11.
- Steinmetz, S., Lars, K., De Pedraza, P., Reips, U. D., Tijdens, K., Lozar Manfreda, K., & Bernardo, W. (2012). WEBDATANET: a Network on Web-based Data Collection, Methodological Challenges, Solutions, and Implementation. International Journal of Internet Science, 7(1), 78-89.
- Tijdens, K. G., De Ruijter, J., & De Ruijter, E. (2012). Measuring work activities and skill requirements of occupations. European Journal of Training and Development.
- Garzarelli, G., Keeton, L., Schoer, V., & Sitoe, A. (2011). Workers’ Compensation, minimum wages and moral hazard scope: stylized considerations on a South African case. Occupational Health Southern Africa, 17(6), 34-39.
- Williams, C. C., & Gurtoo, A. (2011). Evaluating women entrepreneurs in the informal sector: some evidence from India. Journal of Developmental Entrepreneurship, 16(03), 351-369.
- Athanasou, J. A. (2010). Decent work and its implications for careers. Australian Journal of Career Development, 19(1), 36-44.
- de Pedraza, P., Tijdens, K., de Bustillo, R. M., & Steinmetz, S. (2010). A Spanish continuous volunteer web survey: sample bias, weighting and efficiency. Revista Española de Investigaciones Sociológicas (Reis), 131(1), 109-130.
- Muñoz de Bustillo, R., & De Pedraza, P. (2010). Determinants of job insecurity in five European countries. European Journal of Industrial Relations, 16(1), 5-20.
- Kahanec, M. (2009). The Decade of Roma Inclusion: A unifying framework of progress measurement and options for data collection. Institute for the Study of Labor (IZA) Research Report, (21).
- Wetzels, C. Are workers in the cultural industries paid differently? Journal of Cultural Economics, Springer US, 2008, 32, 59-77
- Freeman, R. B. (2005). From the Webbs to the web: The contribution of the internet to reviving union fortunes (No. w11298). National Bureau of Economic Research.
- Tijdens, K. (2002). The impact of a career break on a woman's wage. Transfer: European Review of Labour and Research, 8(1), 123-127.
IZA Discussion Paper(s)
- Life Satisfaction of Employees, Labour Market Tightness and Matching Efficiency
- A Cautionary Note on the Reliability of the Online Survey Data: The Case of Wage Indicator
- The Gain from the Drain: Skill-biased Migration and Global Welfare
- Minimum Wage Violation in Central and Eastern Europe
- The Internet as a Data Source for Advancement in Social Sciences
- Using Internet Data to Analyse the Labour Market: A Methodological Enquiry
- A Web Survey Analysis of the Subjective Well-being of Spanish Workers
- Are Workers in the Cultural Industries Paid Differently?
- Amsterdam Institute for Advanced labour Studies, AIAS
Cross section survey dataRight:
Access to the data is provided to non-for-profit research, replication and teaching purposes. The data is available from the International Data Service Center (IDSC) of IZA.
Please contact IDSC for any access requests.
ANGOLA ARGENTINA ARMENIA AZERBAIJAN BELARUS BELGIUM BOTSWANA BRAZIL CHILE CHINA CONGO, THE DEMOCRATIC REPUBLIC OF THE CZECH REPUBLIC DENMARK EGYPT EL SALVADOR FINLAND FRANCE GEORGIA GERMANY GUATEMALA HUNGARY INDIA INDONESIA ITALY KAZAKHSTAN KOREA, REPUBLIC OF KYRGYZSTAN LATVIA LITHUANIA MALAWI MEXICO MOZAMBIQUE NAMIBIA NETHERLANDS PARAGUAY POLAND RUSSIAN FEDERATION SLOVAKIA SOUTH AFRICA SPAIN SWEDEN TURKMENISTAN UKRAINE UNITED KINGDOM UNITED STATES UZBEKISTAN ZAMBIA ZIMBABWE