Automatic coding of occupations

Abstract

"Occupational coding in Germany is mostly done using dictionary approaches with subsequent manual revision of cases which could not be coded. Since manual coding is expensive, it is desirable to assign a higher number of codes automatically. At the same time the quality of the automatic coding must at least reach that of the manual coding. As a possible solution we employ different machine learning algorithms for the task using a substantial amount of manually coded occupations available from recent studies as training data. We assess the feasibility of these methods by evaluating performance and quality of the algorithms." (Author's abstract, IAB-Doku) ((en))

Cite article

Bethmann, A., Schierholz, M., Wenzig, K. & Zielonka, M. (2014): Automatic coding of occupations. Using machine learning algorithms for occupation coding in several German panel surveys. In: Statistics Canada (Hrsg.) (2014): Beyond traditional survey taking : adapting to a changing world. Proceedings of Statistics Canada Symposium 2014, p. 1-6.