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

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.

Podejście kalibracyjne w badaniach społeczno-ekonomicznych

“…Z kolei w Narodowym Spisie Powszechnym Ludności i Mieszkań 2011 około 1,5 mln osób odmówiło odpowiedzi na temat niepełnosprawności (GUS, 2012). Również w rejestrach administracyjnych, które na potrzeby statystyki publicznej przekształcane s ą w rejestry statystyczne, braki danych stanowi ą poważne źródło błędów (Groen, 2012;Laitila, A. Wallgren i B. Wallgren, 2011;. Na przykład, w rejestrze PESEL, będ ącym głównym źródłem informacji na temat wieku czy płci mieszkańców Polski, stan cywilny jest zmienn ą, dla której obserwuje się spory odsetek braków danych.…”

Business Data Collection: Toward Electronic Data Interchange. Experiences in Portugal, Canada, Sweden, and the Netherlands with EDI

“…As a result of the computerisation of registers, the exchange of large data files became feasible, and NSIs developed multi-source approaches to combine primary and secondary data, a process that is ongoing (Bakker and Daas 2012;Smith and Phipps 2014). All four countries have a long history of using alternative data sources in the production of national statistics (Erikson and Nordberg 2000;Snijkers et al 2011;Born 2016;Saraiva 2016b).…”

Estimating Classification Errors Under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC)

“…When using registers for research, we should be aware that they are collected for administrative purposes so they may not align conceptually with the target and can contain process delivered classification errors. These may be due to mistakes made when entering the data, delays in adding data to the register (Bakker 2009) or differences between the variables being measured in the register and the variable of interest (Groen 2012). This means that both registers and surveys may contain classification errors, although originating from different types of sources.…”