15 may 2008 The validation of the french population census Olivier Lefebvre, Insee

15 may 2008

The validation of the french population census

Olivier Lefebvre, Insee

Page 2

The validation of the french population census 15 may 2008

The method of the new census

›Two major principles–Data collection in rotation (five years cycle)

–Sample surveys for large communes (more than 10 000 inhabitants)

› The sampling base: the located buildings register (répertoire d’immeubles localisés - RIL)

› Special case of persons not living in ordinary dwellings

Page 3


The estimation mechanism

›Municipalities with 10 000 inhabitants or more :–Sample surveyed at 8 % every year, i.e. 40 % over 5

years

–Mobile mean over 5 years (number of persons by dwelling)

–Fitted over the number of dwellings of the median year (given by the RIL)

Page 4


The same year of reference

Municipalities less than 10 000 inhabitants

A - 4 A-3 A-2 A-1 A

Census

Census

Census

Census

Census

Page 5


The estimation mechanism

›Municipalities with less than 10 000 inhab. :

–Exhaustive census every 5 years

–Population updated by :‐Extrapolation taking into account administrative sources (Housing Tax files)

‐ Interpolation with last census (1999)

Page 6


Quality process and validation

Page 7


The figures of population : a synthesis of …

› Results of annual surveys– quality of data collection– quality of data capture– quality of data processing

› Number of dwellings contained in the RIL› Evolution of dwellings in the local tax register› Population of institutionnal households

› Each of these « sources » must be validated

Page 8


The validation of the RIL

›The « quality » survey of the RIL› An annual survey to assess

– The surpluses and the deficits– The surplus-deficit balance– The proportion of communes for which the quality is deemed

mediocre› The sample : 340 000 addresses and 900 000 dwellings› The results

– 1% of dwellings in deficit, 1% in surplus– A level of quality higher than that of usual geographical

registers – A regular improvement process

Page 9


Data collection

› The control procedures during the data collection› The follow-up and control upon the initiative of the

communes› The controls at the Insee after the data collection

(reception-control, in office control, controls in the field)

Page 10


The control procedure during data collection

› A precise data collection protocol

›Checked by the municipal officer

› And by the supervisor

Page 11


The controls at the Insee after data collection

Successive « sorting » › The step of reception-registering of the questionnaires

– Establishment of quality indicators

– Communes with insufficient scores subject to in-depth control (approx. 15% of the municipalities representing 30% of the population)

› Desk controls– Verification with the help of the housing Tax files

– On the exhaustiveness of the dwellings, the FLNE, the structure of main/not main residence

› In field controls (8% of municipalities, 1 dwelling out of 200)

Page 12


The in-field controls

› They are done by Insee agents› They relate to the communes whose quality is presumed

insufficient› Most often, they confirm the data collection› The errors detected are corrected

N.B. these controls are not representative, therefore their results cannot be extrapolated

Page 13


Data capture

›Quality criteria specifically defined in the specifications– securing of transports and data– control of discrepancies with previous counts – maximum rate of error by category of variables

›… and quality evaluated by an independent control process (sample that has been captured twice and arbitration)

Page 14


Data processing

› Encoding–Quality control work station for :

‐Assessing the quality of the automatic coding and manual correction

‐Estimating a « threshold » of percentage of bulletins « non codable »

–The contributions of this approach :‐adding to the expert system files

‐ the improvement of training and protocols (precisions added in codification rules)

‐A finer management of quality, without going in for « excess quality »

Page 15


Data processing (2)

›Data editing and imputation–Anayse the share of non responses or incoherent

responses

–Imputation of missing data or data presumed non-coherent (hot-deck procedure)

–They are fine-tuned year after year, in view of the quality of the variables produced

Page 16


Quality of extrapolations in municipalities more than 10 000 inhabitants

Page 17


The validation in « big » municipalities

Estimations successives de la population (intervalles de confiance à 95%)

128000130000132000134000136000138000

RP 1999 EAR2004 cumul2004-2005

cumul2004-2006

cumul2004-2007

An analysis of the evolutions

Page 18


The validation in « big » municipalities (2)

number of dwellings

number of principal

résidences

rate of principal

résidences

size of households

population of households

RP 1990 55431 50783 91,61 2,53 128564RP 1999 63201 57593 91,13 2,29 1320372004 survey 63772 59806 93,78 2,15 1288632 surveys : 2004 & 2006 66105 61835 93,54 2,13 1315563 surveys 2004-2006 66387 62205 93,70 2,12 1318194 surveys 2004-2007 66836 62577 93,63 2,12 132533

The contributions to the evolutions

Page 19


Any questions ?

Page 20


CENSUS STEPS

Located

buildings register

(R.I.L)

Data Collection Sampling

Data Collection preparation

Households Communities

Communities register Receipt, Checking

Restitution to the mayors Dissemination

Preparation of data entry

Validation

of the data

Estimation of datas :

population and detailed datas

Imputation of missing datas

Coding

Automatic then manual

recovery

Data entry

(external service provider)

Addition of the weights and updating of geography

Documents

15 may 2008 The validation of the french population census Olivier Lefebvre, Insee