New Sampling Design of INSEE’s Labour Force Survey

1. New Sampling Design of INSEE’s Labour Force Survey Sébastien Hallépée Vincent Loonis Manchi Luc INSEE, Unité de méthodes statistiques

2. Outline • Background • Sample redesign • Current sample design • Options for redesign • New sampling design and potential benefits • Concluding remarks

3. Background • In France, 2 series of unemployment data: • Annual unemployment rate estimated from LFS • only source of quarterly estimates of unemployment rate according to ILO definition • Monthly unemployment data derived by applying growth rates obtained from administrative data

4. Background (continued) • Annual average of unemployment rate for 2006: • Labour Force Survey: 9.8% • Monthly series (monthly admin data benchmarked on annual unemployment rate): 9.1%

5. Background (continued) • Beginning of 2007 • No publication of 2006 unemployment rate • INSEE launches a series of studies and plans a publication in the fall • September 2007 • Stop publication of monthly unemployment rates according to ILO definition • Admin data will be published monthly by National Employment Agency • INSEE will publish quarterly results issued from LFS (starting Dec. 2007) • December 2007 • Action plan for improving precision and quality of the survey • Redesign of LFS

6. Sample redesign • Increase sample size by 50% by introducing a new sample beginning in January 2009. • Control new sample phase-in with gradual end of current sample (exhausted in July 2010).

7. Current Sample Design • Sampling frame based on 1999 Census of population • Sample size = 54 000 dwellings quarterly • 1st stage with stratified, geographically defined PSUs • 2554 PSUs • 2nd stage with « Sectors » as SSUs • 1 sector per PSU (~ 120 and 240 dwellings) • 3rd stage with « Area » as tertiary sampling units • 1 area = 20 dwellings • An area remains in sample for 6 consecutive quarters • Each quarter, 1/6 of area sample replaced by another area in the same SECTOR

8. Current Data Collection • When an area enters the sample, the interviewer has to accomplish a field listing to capture new dwellings • 1st and 6th interviews: face-to-face • In between: by telephone

9. Limitations of Current Sample Design Ratio betwwen LFS estimates and admin data for the number of new dwellings 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 2003 T1 2003 T2 Urban Unit of Paris France métropolitaine 2003 T3 2003 T4 2004 T1 2004 T2 2004 T3 2004 T4 2005 T1 2005 T2 2005 T3 2005 T4 2006 T1 2006 T2 2006 T3 2006 T4 2007 T1 2007 T2 • LFS underestimates the number of new constructions

10. Redesign Constraints • Statistical constraints • Maintain area sampling • Improve sampling method • Improve update of new building constructions • Control transition between current sample and new sample • Coordination with other household surveys • Organizational constraints • Maintain interviewers’ work loads • Tight schedule

11. Options for New Sample Design • Tax files • Interesting statistical properties • Careful of quality of sectors • Homogenous auxiliary data • New Census annual surveys • Interesting properties for area formation • Problems relating to different rotating schemes between Census and LFS • Problems caused by different types of auxiliary information • After various studies  Tax files chosen

12. New Sampling Frame Features Tax files: • Contain data for all building premises subject to tax declaration • Year N data available on 1st quarter of year N+1 • Main features • Unique identification code, housing characteristics (address, occupant’s name, cadastral data …) • Comprehensive data base • No duplicate records • Very recent data • Many auxiliary data

13. Objectives of Sample Redesign • The sample redesign aims at improving: • Sampling design • Handling of non-response • Data collection

14. Improving Sampling Design • High quality of geographical information allows allocation of 32 000 000 dwellings to ~ 200 000 sectors with similar size (~ 120 main residences) • Reduce sampling design by 1 stage  direct sampling of sectors • Constant sample size of sectors  reduce variance • Balanced sampling can be used to select sectors based on large number of criteria, in particular: • Total income of a sector • Number of « HLM » (low-rent) dwellings • Number of collective dwellings …

15. Improving Sampling Design (continued) Sampling method for new building constructions : • Record matching of 2 consecutive year tax files based on unique dwelling ID allows identification of new building constructions • Procedure for updating sampled areas can be centralized, automated and carried out regularly

16. Improving Handling of Non-response • For all sampled dwellings, information less than 2-year old: • Category of dwellings (main, vacant, …) • Type of dwellings ( individual, collective, social …) • Household incomes within dwelling • Household status (single, married, # of people) • Dwelling size • All these auxiliary information are likely to improve non-response models. In current sample, only partial information available based on 1999 Census.

17. Improving Data Collection • In current sample, areas contain 20 dwellings (of any category). In future sample, 20 main residences  stabilize interviewers’ work load • Annual update of new dwelling identification (name and address) • Use of Internet sites to help locating new dwellings: • Google map, Mappy, Geoportail, … • Cadastral website

19. Concluding Remarks • Many benefits from new sampling design: • Better coverage • Better field operations • Improved sampling methodology • Improved estimation (handling of non-response, calibration)