New Methods for Job and Occupation Classification

Research question/goal: 

Currently, most surveys ask for occupation with open-ended questions. The verbatim responses are coded afterwards into a classification with hundreds of categories and thousands of jobs, which is an error-prone, time-consuming and costly task. When textual answers have a low level of detail, exact coding may be impossible. The project investigates how to improve this process by asking response-dependent questions during the interview. Candidate job categories are predicted with a machine learning algorithm and the most relevant categories are provided to the interviewer. Using this job list, the interviewer can ask for more detailed information about the job. The proposed method is tested in a telephone survey conducted by the Institute for Employment Research (IAB). Administrative data are used to assess the relative quality resulting from traditional coding and interview coding. This project is carried out in cooperation with Arne Bethmann (IAB, University of Mannheim), Manfred Antoni (IAB), Markus Zielonka (LIfBi), Daniel Bela (LIfBi), and Knut Wenzig (DIW).

Current stage: 

A new instrument for the coding of occupation during the interview was tested and appears promising: 72.4 percent of the respondents actually selected a job description during the interview, making additional manual coding unnecessary. We find, however, that job titles are often ambiguous, reducing the validity of collected data. For this reason, we started to work on an auxiliary classification, which will describe occupational categories more precisely and allow simultaneous coding in national and international classifications. Preliminary findings of our project were presented at international conferences.

Fact sheet

2014 to 2017
Data Sources: 
ALWA and NEPS survey data, additional sources
Geographic Space: