Occupation is a key variable in socio-economic research and predominantly asked using an open response format, followed by field- or office-coding. Web-surveys are disadvantageous because unidentifiable and too aggregate responses can’t be corrected during survey completion. Two solutions can improve respondent’s self-identification, namely online recoding of text or a search tree with an occupational database. The latter is commonly used by online jobsites. Statistical agencies judge the measurement of occupation in web-surveys risky.

The paper uses the 2010q2 data for UK, Belgium and Netherlands (16,680 observations) from the continuous, multi-country WageIndicator web-survey on work and wages, employing a 3-tier search tree with a choice-set of approximately 1,600 occupational titles. This paper investigates:

• What are dropout rates during search tree completion?

• What is completion time for completed and not-completed search trees?

• How often do respondents use the open ended question following the search tree for further detailing their occupation?

• Are dropout rates during search tree completion explained by the length of search paths or individual characteristics?

• Does search tree completion time depends on characteristics related to the survey, the search tree, or individual education?

A new dataset was created, consisting of the survey data, the time stamps and data on the length of the search tree (words and characters). The findings show that drop-out rates for the search tree are approximately 10%, taken into account an overall drop-out rate of 50%. The base model reveals indeed that the more characters red, the higher the likelihood of drop-out, though the effect is larger and significant for the numbers of characters in the 1st compared to the 2nd tier. Drop-out chances in tier1 are lower for employees compared to employment status groups with slightly less pronounced occupations, such as unemployed, students or housewives. No significant relationship is found between the number of characters in tier1 and the time needed to complete tier1, but both the number of characters in tier2 and tier3 and the respective completion times relate positively. The text data was analysed separately, revealing that respondents tend to report a more disaggregated job title.