Measurement of socio-economic variables is often not sufficiently standardised and is expensive. Core socio-economic variables are mostly asked using open-ended questions requiring post-hoc office coding that is time consuming, and thus expensive, and is prone to coder-specific errors. Further challenges are associated with the lack of cross-national harmonisation.
For many socio-economic concepts, there are classifications allowing data producers to standardise coding across data products. For example, the International Labour Organization (ILO) has developed the International Standard Classification of Occupations (ISCO), and UNESCO Institute for Statistics the International Standard Classification of Education (ISCED). However, different data producers may implement the same standard classification differently, leading to inconsistent and thus incomparable data (see e.g. Ortmanns & Schneider 2016). Further, for some concepts several candidate classifications could be used for coding and some classifications are more detailed than others. When a detailed classification is used, data are usually further aggregated for statistical analysis.
Surveyscoding.org contains several databases using standard classifications to organise and code information and to enable input harmonisation at data collection stage resulting in more consistent data especially in case of cross-national data collection. Our services aim at developing solutions for fast high-quality cost-effective cross-national harmonised coding for the key socio-economic variables. The coding tools use a large multi-lingual dictionary containing tens of thousands of entries about job titles, industry names and fields of education. In addition, we provide country-specific, structured lists of educational qualifications and employment status categories. For all of these variables up-to-date code frames are provided using international standardised classification systems.
Ortmanns, V., & Schneider, S. L. (2016). Harmonization still failing? Inconsistency of education variables in cross-national public opinion surveys. International Journal of Public Opinion Research, 28(4), 562–582.
Schneider, S. L., Joye, D., Wolf, C., & Surveys, C. (2016). When Translation is not Enough: Background Variables in Comparative Surveys. In C. Wolf, D. Joye, T. W. Smith, & Y.-C. Fu (Eds.), The SAGE Handbook of Survey Methodology (pp. 288–307). Los Angeles.