1. Theme (ii): New Data Sources and Census
Alexander Kowarik and Thomas Deroyon

2. Introduction
Several countries make heavy use of administrative data in the production of their census, which poses additional challenges in terms of methodological problems and appropriate solutions.
This point in time is an ideal occasion for discussing results, ideas and tests, related to methodological developments that have been carried out in this area

3. Germany : Possible imputation procedures for the Census 2021
Presentations
Austria: Improving the Census Core Topic Occupation by using new administrative data sources
Germany : Possible imputation procedures for the Census 2021
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Israel: Editing and Imputing Income Data in Integrated Census: lessons learned from the 2008 Census – toward the Census 2020
USA: Creating an initial donor pool for new questions in the Census of Agriculture

4. S. Koenig: Improving the Census Core Topic Occupation by using new administrative data sources
Register-based census using only administrative data
Focus on the occupation code variable
Use of a large number of administrative files covering the whole population
Two major issues:
Coding of text information in each file
Combining information available in the different files sometimes on the same population

5. L. Spiess: Possible imputation procedures for the Census 2021
Census combining register with survey data
2011 Census used a combination of cold-deck / deductive / nearest- neighbour imputation methods
For 2021 Census, reflexion on new imputation methods allowing:
exact variance estimation
easy integration into FSO's workflow, especially with validation tools
Three possible imputation methods: 
Multiple imputation: exact variance calculation / time-consuming / need users specific tools
NIM Algorithm (Canceis): very well developped solution / black- box closed tool difficult to fit to specific needs
Nearest-neighbour imputation used for 2011 Census: less developped than Canceis / easy interaction with validation tools / possible enhancements to make it a generic editing tool

6. Combination of register and survey data
Y. Klejman: Editing and imputing income data in integrated Census: lessons learned from the 2008 Census – toward the 2020 Census
Combination of register and survey data
Information on income coming from administrative files
Need to develop specific E & I methods:
to deal with income dispersion and specific populations
while controlling income's distribution on the population
Three emphasized methodological questions:
Assessment of discrepancies between Census and administrative information on activity status
Income imputation for
non-covered subpopulations (mean imputation / random regression imputation)
Covered population (NIM algorithm)
Income editing
for Secrecy management for highest incomes
with trust in income values found in administrative data

7. Imputation in the Census of Agriculture (COA):
D. Miller: Creating an initial donor pool for new questions in the Census of Agriculture
Imputation in the Census of Agriculture (COA):
editing and imputations performed as data enter the system
combination of deductive / historic / nearest- neighbour imputations
donor pools initiated with former censuses and tests
adaptations of donor pools to new validated answers
New questions on decision-making added to the Census:
need to initialise a donor pool without any past information
use of fully conditional specification methods implemented in IVEware to initiate donor pools
validation by COA E & I team with members of methodology division and subject-matter experts

8. Discussion
Are Census datasets « special » so the treatment of E & I is different from surveys ?
How important is error estimation for Census?Is there a need for quantitative error measurement ? Is the imputation error a substantial part of the error ?