Statistical Estimators Using Jointly Administrative and Survey Data to Produce French Structural Business Statistics

Open access

Abstract

Using as much administrative data as possible is a general trend among most national statistical institutes. Different kinds of administrative sources, from tax authorities or other administrative bodies, are very helpful material in the production of business statistics. However, these sources often have to be completed by information collected through statistical surveys. This article describes the way Insee has implemented such a strategy in order to produce French structural business statistics. The originality of the French procedure is that administrative and survey variables are used jointly for the same enterprises, unlike the majority of multisource systems, in which the two kinds of sources generally complement each other for different categories of units. The idea is to use, as much as possible, the richness of the administrative sources combined with the timeliness of a survey, even if the latter is conducted only on a sample of enterprises. One main issue is the classification of enterprises within the NACE nomenclature, which is a cornerstone variable in producing the breakdown of the results by industry. At a given date, two values of the corresponding code may coexist: the value of the register, not necessarily up to date, and the value resulting from the data collected via the survey, but only from a sample of enterprises. Using all this information together requires the implementation of specific statistical estimators combining some properties of the difference estimators with calibration techniques. This article presents these estimators, as well as their statistical properties, and compares them with those of other methods.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Béguin J.M. V. Hecquet and J. Lemasson. 2012. “France’s Economic Fabric More Concentrated Than it Seemed. New Definition and New Categories of Enterprises.” Insee-première 1399. Insee Paris. Available at http://www.insee.fr/en/ffc/ipweb/ip1399/ip1399.pdf (latest access October 2015).

  • Brion P. 2007. “Redesigning the French Structural Business Statistics Using More Administrative Data.” In Proceedings of the Third International Conference on Establishment Surveys June 18–21 2007 Montreal Canada. 533–541. Alexandria VA [CD-Rom]: American Statistical Association. Available at: https://www.amstat.org/meetings/ices/2007/Proceedings/ICES2007-000034.pdf (accessed October 2015).

  • Brion P. 2011. “Esane Le Dispositif Rénové de Production des Statistiques Structurelles D’entreprises.” Courrier des Statistiques n°130 Insee Paris. Available at http://www.insee.fr/fr/ffc/docs_ffc/cs130d.pdf (accessed October 2015).

  • Brion P. 2012a. “Calibrated Bayes an Alternative Inferential Paradigm for Official Statistics.” Journal of Official Statistics 28: 341–347.

  • Brion P. 2012b. “The New French System of Production of Structural Business Statistics.” In Proceedings of the Fourth International Conference on Establishment Surveys June 2012 Montreal Canada. Available at: http://www.amstat.org/meetings/ices/2012/papers/302161.pdf (accessed October 2015).

  • Chami S. 2010. “Reengineering French Structural Business Statistics: an Extended Use of Administrative Data.” In Proceedings of the Q2010 Conference May 4–6 2010 Helsinki. Available at: https://q2010.stat.fi/sessions/session-27 (accessed October 2015)

  • Costanzo L. 2011. “An Overview of the Use of Administrative Data for Business Statistics in Europe.” ESSnet Admin Data workpackage 1 Eurostat. Available at: http://essnet.admindata.eu/Document/GetFile?objectId=5358 (accessed October 2015)

  • Deroyon T. 2013. “Missing Data Treatment in Administrative Fiscal Sources for the French Structural Business Statistics Production System.” In Proceedings of the Third European Establishment Statistics Workshop September 9–11 2013 Nuremberg. Available at: http://enbes.wikispaces.com/file/view/Deroyon%202013.pdf/456103752/Deroyon%202013.pdf (accessed October 2015)

  • Deville J.C. and C.-E. Särndal. 1992. “Calibration Estimators in Survey Sampling.” Journal of the American Statistical Association 87: 376–382. Doi: http://dx.doi.org/10.2307/2290268.

  • ESSnet on Administrative Data. 2011. “Main Findings of the Information Collection on the Use of Administrative Data for Business Statistics in EU and EFTA Countries.” Deliverable 1.1 Eurostat. Available at: http://essnet.admindata.eu/WorkPackage/ShowAllDocuments?objectid=4251 (accessed October 2015)

  • Grandjean J.P. 1997. “The System of Enterprise Statistics.” Courrier des Statistiques. English series n°3 Insee Paris. Available at: http://www.epsilon.insee.fr/jspui/bitstream/1/14403/1/csa3.pdf (accessed October 2015)

  • Gros E. 2012a. “Esane ou les Malheurs de l’Estimateur Composite.” In Proceedings of the Journées de Méthodologie Statistique Insee Paris. Available at: http://jms.insee.fr/files/documents/2012/936_2-JMS2012_S23-2_GROS-ACTE.PDF (accessed October 2015).

  • Gros E. 2012b. “First Assessment of the Combined Use of Administrative and Survey Data in the New System of French Structural Business Statistics.” In Proceedings of the Fourth International Conference on Establishment Surveys June 2012 Montreal Canada. Available at: http://www.amstat.org/meetings/ices/2012/papers/301882.pdf (accessed October 2015)

  • Haag O. 2010. “Redesigning French Structural Business Statistics: Redesign of the Annual Survey.” In Proceedings of the Q2010 Conference May 4–6 2010 Helsinki. Available at: https://q2010.stat.fi/sessions/session-14 (accessed October 2015)

  • Kovar J. and P. Whitridge. 1995. “Imputation of Business Survey Data.” In Business survey methods edited by B.G. Cox D.A. Binder B.N. Chinnappa A. Christianson M.J. Colledge and P.S. Kott. New York: John Wiley.

  • Kroese A.H. and R.H. Renssen. 2000. “New Applications of Old Weighting Techniques – Constructing a Consistent Set of Estimates Based on Data from Different Sources.” In Proceedings of the Second International Conference on Establishment Surveys June 17–21 2000 Buffalo NY. 831–840. Available at: http://www.amstat.org/meetings/ices/2000/proceedings/INTRO.pdf (accessed October 2015)

  • Little R.J. 2012. Rejoinder to the Discussion of his Paper: “Calibrated Bayes an Alternative Inferential Paradigm for Official Statistics.” Journal of Official Statistics 28: 367–372.

  • Särndal C.-E. B. Swensson and J. Wretman. 1992. Model Assisted Survey Sampling. New York: Springer-Verlag.

Search
Journal information
Impact Factor


IMPACT FACTOR 2018: 0,837
5-year IMPACT FACTOR: 0,934

CiteScore 2018: 1.04

SCImago Journal Rank (SJR) 2018: 0.963
Source Normalized Impact per Paper (SNIP) 2018: 1.020

Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 355 229 1
PDF Downloads 155 97 1