The Labour Force Survey (LFS) is generally considered as a very rich and reliable data source to capture labour force characteristics and working times. However, when it comes to the length, timing and scheduling of working times, there is an internationally growing concern about the validity and reliability of working time estimates of the LFS. These concerns rise partially because of its methodology of stylised estimate questions which are prone to memory decay and overestimations of work time durations, and partially because of flexibility, sovereignty, and weekly patterning of working times, which are important characteristics of the current labour market and by no means captured by simple estimates of weekly work time durations as done in the LFS. When it comes to measuring working times, the methodologies of time-diaries as used in Time-Use Surveys (TUS) and Work Grids (WG) can account for the weaknesses attributed to work time estimates of the LFS. Since respondents in TUS face a much shorter period of recall because of instantaneous registration of activities, report their activities in their natural temporal order, and need to confine to the maximum overall duration of 24 hours a day, memory decay and overestimations are largely ruled out. The strength of the WG is that respondents keep track of just one activity in a delineated grid for one whole week, compared to the two-day diaries of TUS.
This project will merge both the TUS and the WG with the LFS and thus create a situation of interoperability. By this, we understand the ability of these three datasets to ‘work’ together in a sense that the combined individual strengths of the different datasets, once merged, will fade out their weaknesses. This will allow a calibration of the LFS dataset based on the TUS and the WG. This merge will also enable us to test the quality of the data of the LFS in terms of reliability (the ability of a method to generate the same results for different samples with the same characteristics time after time) and validity (how capable the registration method is in measuring the variable of interest).