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

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

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 ************************** Break ************************** 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

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

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

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

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 2017 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

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 ?