The University of Auckland New Zealand Data Quality of the 2018 Barry Milne New Zealand Census Tuesday, 3 March Tuesday,March 32020 COMPASS Seminar The University of Auckland New Zealand Outline Some recommendations that (I think) should be taken on board on taken be (I should that think) recommendations Some Census of the users for Guidelines implications? quality data the are What undertaken? were fixes What 2018? Census with happened What to the Census Background 4. 3. 2. 1. Why didit happen? Poor/very poor quality variablesqualityPoor/very poor data sources Use ofalternative implicationsElectoral counts Population 2 The University of Auckland New Zealand Background New Zealand Census of Population and Dwellings Dwellings and of Population Census Zealand New http://archive.stats.govt.nz/Census/2013 Undertaken in and the groups population key populationZealandNew Detailed a setpointtime(by inage,sex, ethnicity, region, community) Official The on first heldyear in ofCensusSince1966, Tuesday March • • • • The The 1946 Census was brought forward to September 1945 No census during the No census during the most recent census was undertaken on March 6,2018 recenton March most was census undertaken Christchurch Christchurch count of how many people and dwellings there are thecountry there in at dwellingsandcount of howmanypeople social, cultural andcultural socio social, since 1851,andsince earthquakes caused the 2011 Census to be re Second Second Great Depression (1931 - census/info World World War (1941 - about every - the - economic information aboutthe total information economic - census/intro five years since 1881,withsince five years ) - to ) - nz - census/history/history - run in 2013 - summary.aspx exceptions 3 The University of Auckland New Zealand Background dollars in the the in dollars “every for important is Census Many other thingsbeside…Many other A Statistical benchmarks Academic Allocating Central Electorates data to socialframe for selectsamples dollar invested in the census generates a net benefit of five five of benefit net a generates census in the invested dollar and local government policy making andmaking policy localgovernment and resources from centralto local government resources from and market market and and electoraland economy” economy” research (Bakker, (Bakker, boundaries 2014 surveys , Valuing the the , Valuing monitoring areas census, p census, . 5) 4 The University of Auckland New Zealand Background Census 2018 Census as equity have ‘to and terms’ own their ‘on development responsibilities Stats NZidentify statistics official Obligations ‘Digital first’ first’ census ‘Digital Paper questionnaires made questionnairesPaper under Te under – access access Tiriti o Waitangi relating to the production of of production to the relating o Waitangi available codes mailed. to support Māori well Māori to support as aback - up upon up request - being and and being citizens’ . 5 The University of Auckland New Zealand What happened? 6 The University of Auckland New Zealand What happened? Percent 100 90 10 20 30 40 50 60 70 80 0 Regional Council 98 – 100% Territorial Authority 95 – < 98% Statistical Area Level 2 90 – < 95% 75 – < 90% Statistical Area Level 1 < 75% 7 The University of Auckland New Zealand Waima Forest Waima Ngapuhi Ngapuna Ferguson CentralOtara East Eden North Mount Wiri SA2 Clendon ParkClendon North Queen Street Grange North Hokianga Rowandale West EastOtara PaBridge West Mangere Otangarei Central Queenstown Fordlands Burbank Harania North South Otara Panmure Flaxmere West West Otara What happened? West - Glen Innes Innes Glen Industrial Percent 59.8 59.2 59.1 58.9 58.9 58.9 58.7 58.6 58.5 58.1 58.0 58.0 57.3 57.1 57.0 56.6 56.5 55.9 55.6 55.5 55.0 54.9 52.3 46.9 Territorial Territorial Orakei Authority/Local Albert /Maungakiekie Queenstown Mangere Mangere Otara Otara Otara Otara Otara Otara - Eden/Waitemata ------Papatoetoe Papatoetoe Papatoetoe Papatoetoe Papatoetoe Papatoetoe Whangarei Whangarei Waitemata Waitemata Manurewa Manurewa Manurewa Manurewa Far North North Far North Far North Far - - Hastings Hastings Otahuhu Otahuhu - Rotorua Rotorua Tamaki - Board Lakes Lakes Bay of Bayof Plenty Bayof Plenty Hawke's Hawke's Bay Hawke's Bay Northland Northland Northland Northland Auckland Auckland Auckland Auckland Auckland Auckland Auckland Auckland Auckland Auckland Auckland Auckland Auckland Auckland Auckland Region Otago • • • • District) (3 from 4 from NorthlandFar North Otahuhu Manurewa boards 10 from the SouthAuckland 26% ofallSA2sonlycontains which inAuckland,15/24 (62.5%) completionCensus <60% SA2areas had 1% (n=24) of Otara (2 (4), (4), and Mangere ) - Papatoetoe (6), - 8 The University of Auckland New Zealand Why didit happen? New Zealand’s 2018 2018 Zealand’s New with associated Factors to start over be savedA form couldn’t their respectiveform to complete household T paperor took to for oftenwenttime Requestsforms a long unheeded, The staff fieldin time. Notenoughemployed he importance of paper inwasforms underestimated importance thismodel same online access codeaccessonline again Census; Jack Jack Census; low response response low – if not completed in a session the respondent had inthe respondentif session a not completed was required for each individual within the withinrequiredwasfor each individual and rates (Independent (Independent rates Graziadei , 2019 ): Review of Review . arrive 9 The University of Auckland New Zealand Why didit happen? New Zealand’s 2018 Census; Jack and and Jack Census; 2018 Zealand’s New of Review (Independent rates response withlow associated Factors substantially more ofthese more than previous substantially there was meaning It responses, not to followup partial was decided nonStrategiesinfor put place communities engageenough strategies didn’tand engagement Communication - private dwellings didn’t work didn’t dwellingsprivate Censuses Graziadei , 2019 ): 10 The University of Auckland New Zealand Fixes Census 2018 External Data Quality Panel set up to advise on advise to up set Panel Quality Data External 2018 Census any Treaty partners data sources that affect administrativeof the the qualitydata approaches basea strong evidenceresearch and sound are on census based whether August 2018 Donna Cook,Ian Cope,Len Bedford, AlisonReid,Dick quality issues peopleissuesquality issues that may affect the usefulness of the data for Māori and iwi as iwi and Māori of thedatafor that affect may usefulness issues the Cormack, Thomas Lumley, Barry Cormack, ThomasMilne Lumley, the methodologies used to produce quality information from from informationqualitythe to produce used the methodologies to data processing and methodology, and increased use of and increased and methodology,to data processing – February 2020 need to consider when using 2018using to considerwhen Tahu Kukutai Census , 11 The University of Auckland New Zealand Fixes • • • • • people who didn’t fillthe out didn’twho people FIX 1: the Use for datasets to linked)(allowsspinebe informationIDI identifiable forpeoplein IDI ( Behind (subset of IDI spine) pretty sure are IDI ERP of NZever beena residenthave IDI spine:list to of whoarelikelypeople for availableand de level, at the linkedindividual of administrativeIDI: Collection - Sure: List of people weListof canbe Sure:people not IDI ERP currently available for research)is for available research - Sure residentNZin census to get the - data sets identified, . 12 The University of Auckland New Zealand Fixes placed in correct locations correct in placed Data Force. of Ministry prisoner; Fix name (using Fix Add people AND grab characteristics aboutthose people ANDgrab characteristics Add people to be missed; 1.2% estimated 97.7% linked; 4,700/9,700 • 2: Corrections gave gave 2: Corrections Census 1: Link Adding to households; adding entirely new households for Census non Census for , date of date , prisoners; 2018 records records 2018 Defence birth, meshblock) birth, 800/3,200 Stats NZ . - responders identified from from identified responders did the same for those in NZ in those for same the did of those NZin linked linked unit - record data files files data record to < 1% estimated to to 1% estimated beincorrect people in the the in people Defence Force for every for every IDI and IDI and IDI spine IDI spine Defence 13 The University of Auckland New Zealand Fixes 14 The University of Auckland New Zealand Fixes to the Census dataset from from dataset Census to the However 2.0 2013 in percent to 2.4 compared population, Zealand New The &IDI Census 6 population Zealand New estimated ofthe percent 98.6 to cover The March 2018 of 2018 March percent in2006 percent under is estimated 4,699,800 of population resident usual Census final , the 2018 result is obtained only only is obtained result 2018 the , - count of 68,800 represents 1.4 percent of the estimated estimated of the percent 1.4 represents 68,800 of count - ERP 4,768,600 (using ‘dual system estimation’ based on on based system estimation’ ‘dual (using 4,768,600 - . Sure). administrative administrative after after data. 524,900 were added added were 524,900 and at 15 The University of Auckland New Zealand Fixes 16 The University of Auckland New Zealand Fixes data sovereignty sovereignty data Māori and data), of Māori use the trusted for mandate (collective Mitigations non EDQPendorsed No comprehensive to file get acensus workedZealanders that counts most Mitigation New wasresponses individual17% missingA censuswith gauge the acceptability of the census revised the acceptabilitygauge with thegroupsincluding mostaffected by the use ofalternativedata, to - response response raise raise questions around around questions the statistical approaches used to to used approaches statistical the and open public consultation withZealanders,Newconsultation publicopen and social licence, licence, social approach not cultural cultural mitigate an option licence 17 The University of Auckland New Zealand Legality Legality and licence (i.e., tacit approval from the New Zealand public Zealand New the from tacit approval (i.e., the Does Was The individual and dwellings censusforms and dwellingsThedidnotcontain individual this information Privacy, 2017, p.13). (Simplyadds and value” this islegitimate that the IDIwith integrationexplainand andaddresses use ofnames and SNZ… noticeto “shouldprovideclear about thepublic… the retention and Unclear… to StatsYes, accordingadvicelegalNZ’s extensive use of administrative data censusmitigationfor use of administrativeextensive of levelslower giventhat important, the trust Māori of MāoriisespeciallyRetaining the data linkage legal linkage data the linking of of linking institutional trust, trust, institutional admin data data admin ? but are among those most those most by the but are among impacted to census data enjoys social social enjoys data to census )? . licence have 18 The University of Auckland New Zealand Legality Legality and licence iwi and Māori Treaty partners have partners Treaty Māori iwi and data, Māori of use trusted the for mandate was cultural there whether clear Also not individuals the from obtained in those and prisoners from Consent understand did ifthey data to provide willingness their to the linked are sets that data Not clear that people in New Zealand understand the extent of of extent the understand Zealand New in people that concerned census, census, . defence nor that it would not affect affect not it would that nor licence based on the trust that that trust the on based force was was force : collective : collective not not 19 The University of Auckland New Zealand Fixes A very different Census data file data Census different A very ‘ was question to a but response completed was Census when Also used variable) the on (depending used were sources other three to up 2018, Census from available wasn’t data Where Fix 3: https ‘not stated’, ‘response outside scope’, ‘response unidentifiable‘responsescope’, outside‘notstated’, ‘response Imputation data Administrative census 2013 data from data from a mix sources: of ‘ refused to refusedanswer’, :// www.stats.govt.nz/reports/2018 Not elsewhere Not elsewhere - ‘ census don’tknow’ - external - data included’ - quality - panel - data - sources - for - key - 2018 - census - individual ’, - variables 20 The University of Auckland New Zealand 1. Population 1. Population counts Data quality implications population? population? ofthe count accurate an provide file Census final the Did also appear to be accurate. appear also and sex by age Distributions to TALB area. down counts accurate and undercount, small IDI using estimation system dual yet, but available not results Yes. - ERP Post - Sure suggests only a Sureonly suggests - enumeration survey 21 The University of Auckland New Zealand 2 Data quality implications . Electoral allocations Some background… Some determined accurately to be of electorates number the for allow file Census 2018 the Did so The minus The count Māori Yes All electorates Allelectorates must have roughly the same population, North Island GEP/South MEP/South Island quota = South number number of South Island general electorates is fixed at General electoral population (GEP) multiplied by electoral electoral population (MEP) the Island MEP. GEP/16 = the percent of enrolled Māori voters choosing the Māori Island quota = South Number Number of Māori electorates Island = electoral Number Number of quota = the census usually resident population count Māori Māori descent usually resident population General General electorates in the North Island ± 5% 16 (Electoral 16 (Electoral Act, 1993 roll roll (52%). ), 22 The University of Auckland New Zealand • • • 2. Electoral allocations Data quality implications electorates electorates to needed for the number of Māori population change be would Unrealistic assumptions about 7 MĀORI ELECTORATES THE THRESHOLD REGARDLESS OF THE use 0.5 for Census 2018. certain; 0.0 = a guess). Stats NZ Census file (1.0 = absolutely enough to add that person to the of a person’s location was accurate for determining whether knowledge Stats NZ used a threshold (alpha) RESULT ISRESULT ALWAYS not CHOSEN, be 7. boundaries. forelectoralCensus of 2018 Sensitivity analysis Data(2019).Loves Dot Unpublished report provided toprovidedStatistics report NZ. Unpublished 23 The University of Auckland New Zealand • • • 2. Electoral allocations Data quality implications alpha alpha this would include the selection of exercise a degree of discretion; Government Statistician to The Electoral Act 1993 enables the (as there was at the 2017 election). thresholds 49 electorates. Only strict Most thresholds (<=0.6) suggest Here the threshold matters a little. electorates? What about North Island general (and (and 0.5 seems reasonable suggest suggest 48 electorates electorates ). boundaries. forelectoralCensus of 2018 Sensitivity analysis Data(2019).Loves Dot Unpublished report provided toprovidedStatistics report NZ. Unpublished 24 The University of Auckland New Zealand 3. Alternative data sources Data quality implications has improved the quality of the Census results results Census of the quality the improved has T he But thereare issues… as ifcensus complete asmore but better nothing,not data asgood sources Use ofalternativethandoing (a goodthing) reduced undercountCensus • use of administrative ofadministrative use administrative data is not yet possible.” 2015 Cabinet Paper Census Census Transformation data, Census 2013, and imputation imputation and 2013, Census data, - Promising Future: “ a census based on data data 25 The University of Auckland New Zealand 3. Alternative data Data quality implications Weekly Weekly Sector Income, Address, data Admin >10% Admin (6/3/2018) Census with contemporaneous be not may data Admin the Census Taxable the first givento thevaluetime thatstudentitself havedefaulted might February2017,which an 2017,IDI from enrolmentin Decemberin A value of ethnicity from education data might have been supplied to the suppliedbeendata of ethnicity value educationmight have from data data Rent Paid by by Paid Rent income may may from from IRD is not measure measure not : Sector of of : Sector Households, Age, Age, Households, sources: of not the same as personal incomenot the same as personal Landlord, Usual Usual Landlord, Ownership, Industry, Workplace Workplace Industry, Ownership, exactly the exactly Admin data Admin Sex same thing same Residence Residence Address, Address, reported at reported enrolled. 26 The University of Auckland New Zealand 3. Alternative data Data quality implications qualification school secondary NZ, highest in arrival since years smoking, ethnicity, spoken, languages religion, descent, Māori place, birth ago, years 5 residence Usual census: of2013 use >7% time over little very or change change not do which variables for used Census 2013 are of interestare of to researchers. of thosethat Sometime analysis do for variablebe underestimated will for of changeDegreevariables change over time change sources: do change (ethnicity, smoking, religion)(ethnicity, smoking,change Census 2013Census that 27 The University of Auckland New Zealand 3. Alternative data Data quality implications main main and work occupation, imputation: >15% variables. to system imputation CANCEIS find potential donors who are good matches on a set of a set on matches good are who donors potential find bivariate associationsbivariate Accuracy may lowat levelthis willaffect beand the individual estimates Unbiased • • May decrease estimates of association with variable not included in the imputation model May increase estimates of association with variable included in the imputation model means of travel to work, main means of travel to education travel of means main to work, of travel means Closest match is chosen. is match Closest ( , so should produce accurate counts produce so should , CANadian searches records that are near near are that records searches sources: Census Edit and Imputation System) System) Imputation Edit and Census Imputation labour force force neighbours status, matching matching 28 The University of Auckland New Zealand 4 Data quality implications http https . Poor/very poorquality variables Metric 3 3 Metric 2 Metric 1 Metric on: based poor), very poor, moderate, high, high, (very scale point five a using variables of quality the Stats NZ assessed ://datainfoplus.stats.govt.nz/Item/nz.govt.stats/ca28210f :// comparability with other sources comparability with the expected trends quality rating ( A score ( Including Including aspects such as coding, www.stats.govt.nz/methods/data – – – 0 – data quality data coherence and consistency coverage and sources data 1 ) is given based on the contribution of each data source, weighted by a 0 – 1 ) give to each data source. - quality level of detail/classification, - assurance - for - 3fd6 - 2018 - 415c - census accuracy accuracy - a162 - ecc07b4a28b0 of responses 29 The University of Auckland New Zealand 4. Poor/verypoor quality variables Data quality implications https subgroups and at lower levels classifications of levels at lower and subgroups NZ for Stats than quality lower as variables rate to EDQPtended to: according variables Census assess to Stats Canada by used framework quality the EDQPadapted :// www.stats.govt.nz/reports/2018 Contemporaneity Comparability Consistency Coverage • • • • Were Were 2018 census How does methods?collection consistent classification a Was For the overall overall the all data sources used for the variable obtained at the same the at obtained variable the for used sources all data population, and by ethnic ( ethnic group by and population, compare - census used, and was data used, and - to recent to external - Censuses and other measures of the same variable? variable? the same of measures andother Censuses data - individual variables individual quality collection collection online across data and paper consistent - panel - assessment time? only) only) and - of - variables regions regions 30 The University of Auckland New Zealand 4. Data quality implications Count of variables 10 15 20 25 Poor/very 0 5 poor quality variables Very high 2013 High Moderate Poor Very poor 2018 31 The University of Auckland New Zealand 4. Poor/verypoor quality variables Data quality implications missing iwi data in Census in Census data iwi missing to address way reliable or robust a be to appear not does There household the from absentees and iwiaffiliation are These of overall data having Stats NZ as by rated been have that variables two individual/personal only are There identification. interis verysignificantAt there the aggregatelevel, than the dataIwi administrativeare iwi Significant response 2018to replacetheir missing response census for for the firsttime. census (Ministry census changes to the iwi classification into the iwiclassificationchanges It would bewould difficultto justify the use2013 ofan individual’s For these, of Education, sparse.Data 2018 no prior census data existsno prior Corrections, that do existareofpoorer quality 2017 classified a classified2017 very poor very - censal NZ Police . . change in iwiinchange quality. quality. ) number of number . 32 The University of Auckland New Zealand 4. Poor/verypoor quality variables Data quality implications • Family• Child Family in Dependent Youngest of Age • FamilyChild in Youngest of Age • in Adult FamilyChildren of Number • in FamilyDependent Children of Number • Indicator Person Young Dependent • 18 Child Under Dependent • in Individual’s Nucleus • Role Family Individual’s Nucleus Family of Identification • Household 15 in Under Aged Usual Residents of Number • in Usual Household OverResidents of Aged 15and • Number Household in Usual Residents of Number • FamilyChildren in of Number • People in Familyof Number • quality, though Stats NZ are reviewing these ratings: these reviewing NZare Stats though quality, rated currently are variables household and family 29 Type • Sex of Sole Sole Parent of Sex • Same in Partner Younger of Age • in Same Partner Older of Age • in Opposite Partner Female of Age • Opposite in Partner Male of Age • Couple of Type• Child Status Dependencyby Composition Household • Child Household in Dependent Youngest of Age • Household Child in Youngest of Age • in Household Dependent Children of Number • Composition Household • TypeChild byDependency• FamilyStatus TypeFamily Extended • Family• Type Children of Number by • Family Typewith Type Couple of - Sex Couple Couple Sex very poorvery - - Sex Couple Couple Sex Sex Couple Couple Sex - Sex Couple Couple Sex 33 The University of Auckland New Zealand 4. Poor/verypoor quality variables Data quality implications producing census producing Lack Māori and Pacific Māori and of number a disproportionate ~357,000 of coverage of families in admin data means the potential for for potential the means data admin in of families coverage of people (from (from people - populations are populations type information on families is currently is currently families on information type admin these meshblocksare for data) high not able to be placed intoto beable placed in areas where where areas in a dwelling minimal 34 The University of Auckland New Zealand 4. Poor/verypoor quality variables Data quality implications complex processing and coding of household and family family and of household coding and processing complex Problems Chose not to not to staff Chosededicate issues to family coding Tootime to fix hard given • • • • • • • Fewer Fewer households to manual coding (3% vs 18% previously) Major There Some Underage partners in opposite sex couples Potential undercount of children under 5 years old A large decrease in one – Implausibly large increases in the partner increases of the in age same older large Implausibly increase increase in the number of households c is an very old “children”, very young “parents”, and very young people livingalone in how how in overcount Stats Stats NZ’s in same - parent families constraints - sex couples new processing system handled the the handled system processing new omprising omprising - sex couples sex couples a couple and other person(s data 35 ) The University of Auckland New Zealand 4. Poor/verypoor quality variables Data quality implications overall overall having Stats NZ as by rated been have variables following The Unpaid activities;Unpaid status in relationship; current status, andrelationship partnership registered status: LegallyRelationship Qualifications:Post of rooms Number home ownership Individual Activitylimitations Years at usualresidence five years ago residence Usual oneago year residence Usual poor quality data quality ; ; - schoolqualificationfieldof study . : ; ; ; ; 36 The University of Auckland New Zealand 4. Poor/verypoor quality variables Data quality implications levels of some classifications, and for some ethnic groups ethnic some for and classifications, some of levels some for poor or very poor to be data EDQPassess In addition, Poor Verypoor • • • • • Occupation Occupation (overall) Hours worked in employment for Pacific (nearly 40% imputation) Smoking for Māori, Pacific and MELAA (through over “Te reo” under the language classification (and perhaps other Level 4 languages) African (MELAA)” ethnicities Level 4 of the ethnicity classification for 45 “Middle Eastern, Latin American and - reliance reliance on Census 2013 data) 37 The University of Auckland New Zealand 4. Poor/verypoor quality variables Data quality implications EDQP work closely withStats NZ workcloselythe to understand to are able who environmentsin controlledworking individualsaccredited Access to data rated as as officialnot and be released that suchdatashould mislead D poor Very ata believe rated rated quality variables should not be released.not be shouldvariables quality overall as overall being of being poor quality overall should be restricted be restricted to should overallquality poor quality overall has the potential to has thepotentialoverallquality quality of thedata statistics. . 38 The University of Auckland New Zealand Guidelines Guidelines for use of 2018 Census data Census 2018 of for use Guidelines Analyses may Analyses and the useCheck of the EDQP’sRead so and other by (and regionis differential ethnicity Dataquality comparing across censuses. comparing to for needsaccounted2018 be whenin Census information’Less ‘no • • can Sensitivity analyses Sensitivity analyses should test if imputation impacts results caution is advised when is advisedcaution for for areas. small be considered. be alternative data sources alternative affected affected by assessments using using missing undertaking comparisons. undertaking high levels of levelshigh - data and the relevantand techniques techniques , overall, by overall,, imputation (e.g . Stats NZ multiple . subpopulation, imputation) DataInfo factors) + page 39 The University of Auckland New Zealand R R R 2b R R (selected) Recommendations for Stats NZ 6 3 2a. 1 . . . waysaffiliationto measureiwiso thatthe census isnot thesource only measureaffiliation,iwiandtake should responsibility,inwith partnershipiwi,for investigating alternative Stats considerwhetherarechangesfor neededthe 2023Census missingresponses2018,ina focus with on the differentialfor impactsdifferentpopulation groups,and Stats Stats andnewin migrants, decision Stats sovereigntyfor thespecifically 2023Census. the extensiveuseadministrativedata of in futurecensuses andissuesof and sociallicensedata Māori involvementof inMāori futurecensuses. This includesStats NZactivelyaddressingtheof acceptability developand implementdecision Stats NZ should NZ should NZ should NZ should NZ should ensure ensuredatacollection futurein censusesis comprehensive enoughto accurately review ensureindividualcensusresponses from prisoners are in obtained the 2023Census ensure the extenttowhichthe waytheforms were online administeredto contributed there t here - making onabout data them,including makingof thein useadmindata the census is is - making andmaking governancemechanisms, to ensure meaningful a realfor memberscommunities, voiceofallespecially Pacific peoples genuinepartnershipMāoriwith communities, . . organisations andto iwi 40 . . The University of Auckland New Zealand (selected) Recommendations for Stats NZ R 22. R 21. R 19. R 17. R 12. Boardareas andethnicgroupsthat hadresponselow rates in2018. Statsshould NZ (>94 percent)in Māori from the 2023Census partnershipwithto Māoridevelop acensusdelivery modelthatwillachieve a very high response Statsshould NZ whereprojectproposalsconsideredarebyStats NZ ona case Statsshould NZ familiesandhouseholds data, andother complex variables,andnot divert this other team to tasks. Statsshould NZ betweenvariables sizesethnicgroups of andareas(e.g. small SA2s), andthe impact onassociations bivariate acrossAnalysesshould variables.focuson … estimates ofinter (previouscensusdata,data arange from of adminsources, imputeddata)the qualityof on data Stats NZshould set responserate targets particular for andTerritorial Authority AucklandLocal havean makeonlyrated as data being of supporta dedicatedteam for the 2023Censusto undertake post systematicallyinvestigatethe impact ofthe use ofalternativedata sources . organisational commitment to, commitmentandfocus on,achieving effective . poor or verypoor - by - censal - case basis case qualityoverallavailable change,the impacton the - processingfor 41 The University of Auckland New Zealand QUESTIONS? 42