Comments on Integrating designs for economic variables Luxembourg Income Study (LIS) asbl 17, rue des Pommiers L-2343 Luxembourg –City Tél : +(352) 26 00 30 20 Email : LISasbl@lisproject.org Fax: +(352) 26 00 30 30 Web : www.lisproject.org Comments on Integrating designs for economic variables Workshop « Integrating European Census Microdata II » Paris, 7-10 June 2006 Teresa Munzi Luxembourg Income Study

Luxembourg Income Study Since 1983 Promote comparative research on income inequality, poverty, labour market outcomes, and social policy Harmonizing social and economic household survey data Disseminating the data to social science researchers through a remote access system based on the internet

LIS / IPUMS Similarly to IPUMS: confronted to the task of harmonising data from various countries  different institutional/societal setups from various existing surveys  output harmonisation over time  changes in original surveys household/individual level data  confidentiality issues some overlap in the contents  economic variables (labour market data) Differently from IPUMS NOT based on censuses, but a variety of household surveys LIS harmonises data, while IPUMS standardises it

Labour Market variables Very difficult to create comparable variables Many international guidelines and recommendations, but those are usually only applicable to data from LFS  different definitions Rigid routing of the labour market questions  different universes

Activity status Universe Concept Reference period

Activity status: definition and reference period Any work Main activity At present (or at a given date / week) IPUMS LIS I LIS II Over a longer reference period LIS III

Activity status: some unclear cases Sometimes it can be difficult to assign a specific national code (or category) to the employed, unemployed or inactive population Persons on leave (maternity, sickness, layoff) Persons in occupational training / trainees / apprentices Conscripts (military or civil service) Mixed categories Two different approaches for these unclear categories Follow the routing of the questionnaire  comparability is harmed Follow some pre-determined rules  inconsistent data

Activity status: some guidelines Adopt one clear definition, and try to stick to it as much as possible Use the documentation to warn against deviations from the ideal definition Semi-standardisation is OK, but also keep as much detail as possible in further digits (so that the users have the flexibility of treating specific categories differently) Among the employed, if possible distinguish the different extent of work (regularly employed versus marginal/occasional employed) The category of unemployed should not coincide with the ILO unemployed

Time worked Definition Weekly hours “Usual” hours in the sense of regular and not actual during a given reference week, nor contractual (if different because of overtime) Time worked at all jobs (NOT FT/PT modality of main job) Includes paid work and unpaid family work, but excludes unpaid voluntary work

Time worked Universe and importance of getting the zeros right Ideal case: universe = all persons who are employed Not in the universe = zero working hours (it has a meaning!!!) But can differ considerably (only those who are mainly employed, only those who have a regular schedule, only employees, etc.)  An effort should be made to set to zero only the true zeros Top-coding Top-coding can vary a lot Some basic top-coding (99?) should be applied to datasets with very high numbers Importance of warning users for datasets that applied a stringent top-coding policy on hours

Job characteristics: Status in employment Occupation Industry Place of work Differently from the activity status and time worked variables, these variables refer to a job and not a person: main job No correspondence between the persons employed according to activity status and universe of job characteristics variables If standard international classifications are not available, keep the detailed national ones

Some generic recommendations LIS Golden rules: Maximise comparability by setting clear definitions for each variable and trying to stick to them as much as possible Document very well any deviation from the general rule/definition Allow for maximum flexibility to the users by keeping all the available detail necessary for any other concept Enhance user-friendliness by creating some semi-standardised codes, but keep the country-specific detail in the further digits