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Annual labour force surveys

Annual labour force surveys. Ralf Hussmanns Head, Methodology and Analysis Unit Bureau of Statistics International Labour Office. International recommendations on periodicity of labour force statistics (1).

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Annual labour force surveys

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  1. Annual labour force surveys Ralf Hussmanns Head, Methodology and Analysis Unit Bureau of Statistics International Labour Office

  2. International recommendations on periodicity of labour force statistics (1) • “Current statistics of the economically active population, employment, where relevant unemployment, and where possible visible underemployment, should be compiled at least once a year.” • ILO Recommendation No. 170 (Labour Statistics Recommendation), 1985, Paragraph 1. (1)

  3. International recommendations on periodicity of labour force statistics (2) • “The current statistics programme should encompass statistics of the currently active population and its components in such a way that trends and seasonal variations can be adequately monitored. As a minimum programme, countries should collect and compile statistics on the currently active population twice a year … ” • 13th ICLS (1982), Paragraph 2 (a)

  4. International recommendation on periodicity of statistics on the informal sector • “The data collection programme should provide both for (a) the current monitoring, if possible once a year, of the evolution of employment in the informal sector and (b) the in-depth examination, if possible every five years, of informal sector units with respect to their numbers and characteristics ...” • 15th ICLS (1993), Paragraph 21 (1)

  5. Annual labour force surveys • Periodic data collection (point in time estimates): • once a year • two, four or twelve times a year • Continuous data collection (annual, quarterly or monthly averages): • every week

  6. Continuous data collection (1) • Seasonal and other variations over time are captured and period effects eliminated through division of sample in monthly, fortnightly or weekly sub-samples and continuous data collection during the year (examples: Mauritius, South Africa). • Estimates reflect the average situation during a month, quarter or year, i.e. for many purposes (including national accounts) they are more useful than point-in-time estimates.

  7. Continuous data collection (2) • Flexibility in periodicity of data dissemination (example Colombia: dissemination of monthly, bi-monthly, quarterly, bi-annual and annual averages), • but inverse relationship between (i) level of geographic and other detail of estimates and (ii) frequency of data dissemination. • It becomes unnecessary to use concepts based on long reference periods (e.g. usual activity, annual income), which are prone to recall errors.

  8. Continuous data collection (3) • As data entry and processing can be carried out on a continuous basis, the time lag between data collection and dissemination can be much reduced (South Africa: one month) and the users’ demand for more timely statistics be satisfied. • Data quality is improved because field work is carried out by small teams of permanent interviewers and supervisors (reduced cost & improved quality/intensity of training, recruitment and supervision of field staff facilitated, including re-interview programme). • Additional topics can be included in the survey as modules attached to it from time to time.

  9. Enhanced flexibility to meet demands for additional data • Additional topics included in the survey should be somehow related to the core topics of the survey • to avoid that the survey will become an omnibus multi-purpose survey. • Not all additional topics require to be investigated in using the whole sample. • To reduce response burden and survey costs, sub-samples can be used to investigate various additional topics.

  10. Sub-samples • Defined by rotation groups • or determined by serial number of the interview • or representing all households interviewed during a specific period of time (month, quarter or year)

  11. Determining factors for type of sub-sample • Urgency of user needs for the information, time available for data collection. • Nature of the topic, especially its being subject or not to seasonal or other variations over the year. • Usefulness of inclusion of topic in repeated interviews of the same households. • Precision requirements for estimates. • Response burden of households. • Etc.

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