Philippe BrionJohara KhélifInsee

28/04/2014

Questions raised by the implementation of the data editing device for French structural business statistics

Work session on statistical data editing, Paris, April 2014 (topic iii)

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Outlines of the presentation

A new production process for French Structural Business Statistics has been working for 5 years

What have we learnt from it ?

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1. A few elements on the production process

The Esane process was put into place in 2009– Produces Structural business statistics every year ;– Is the main yearly statistical source on firms for Eurostat

and French National Accounts ;– The old system used two different sources : a survey and

administrative data (tax declarations) ; A new system under constraint

– A change under constraint : budget and staff were called to diminish ;

– Methodology : keep high standards on statistical quality Insee’s answer through the Esane process

– Produce only one set of statistics, instead of two before ;– Put selective editing methods into place.

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2. From theory to practice (1/2) In theory, the data editing system was based on the

following idea :– Implementation of local scores reflecting the “influence” of a

firm on the final result (based on temporal and contemporary drop-out scores) ;

– Synthetic global priority scores pointing problematic firms out.

In practice, three main problems– The scores were sometimes unsatisfactory (unstable, not

adapted to some variables, problem with measuring the potential error) ;– The method led to too much work for the staff (thresholds

calculated « a priori » had to be adjusted) ;– The first intended calendar was unrealistic.

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2. From theory to practice (2/2) Since 2009, the methods have been fine-tuned

– The computation of local score aggregates is more robust (based on a yearly median growth rate estimate for each sector) ;

– Computation of the global score, taking into account the relative importance of the results of each local score

– But the data editing method still lacks efficiency for some variables

The calendar for the production process has been reviewed– Instead of publishing one final result in December of year N+1…

– …We publish Semi-final results in December N+1 by implementing only part of the

administrative data (75% of the firms, 85% of the final value added) ; Final results in June N+2 after implementing all the data.

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3. Lessons learnt (1)

Lesson one : Selective editing is better when there is feedback from the producers

– we changed our methods thanks to feedback from the editing staff;

– We plan on keeping very close to them :

Yearly reviews on Esane process’ help us improve; The editing staff is more satisfied than in the beginning

– Working closely prevents the “black box” effect

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3. Lessons learnt (2)

Lesson two : feedback from the users is also helpful

– It helped us change our calendar– Led us to invest on output editing…– Helped us to improve our editing methods

Lesson three : we need to keep working on the methods

– Keep robust output editing methods (as a safeguard)– Having a continuous improvement of the methods through

the use of metadata– But we don’t think it would be efficient to have globally

standardized selective editing tools for all Insee Could be useful for structural firm surveys And administrative data.

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Thank you for your attention !

Contact M. Philippe BrionCourriel : philippe.brion@insee.fr

Insee18 bd Adolphe-Pinard75675 Paris Cedex 14

www.insee.fr

Informations statistiques :www.insee.fr / Contacter l’Insee09 72 72 4000(coût d’un appel local)du lundi au vendredi de 9h00 à 17h00

Questions raised by the implementation of the data editing device for French structural business statistics