Combined use of data from registers and sample surveys Eric Schulte Nordholt Statistics Netherlands Division Socio-economic and spatial statistics e. schultenordholt@cbs. nl Statistical Training Course on Use of Administrative Registers in Production of Statistics in Warsaw (October 2014)
Contents General • Social Statistics • System of social statistical datasets (SSD) • Group work on registers and surveys • The Dutch virtual census • Time for questions and discussion 2
Contents Social Statistics • Requirements for modern Social Statistics • Driving forces • Policy implications • Life cycle model • Relevant statistical information for policy and society • Strategy for data collection • Secondary data • How to get consistency of different data sources? • Prototype of a micro database • Conclusions 3
Requirements for modern Social Statistics Product quality (Eurostat Code of Practice): 1. Relevance 2. Accuracy 3. Timeliness and punctuality 4. Comparability and coherence 5. Accessibility and clarity 4
Driving Forces More coherence, more thematic publications, more detail (small areas, population groups) and more flexibility in the statistical output (will lead to a better product) ICT developments: more registers High nonresponse rates in social surveys To cut down processing costs: standardisation To lower response burden: less questions, EDI (or EDC) and diminish ‘irritation factor’ 5
Policy implications • From primary to secondary data collection – Wherever possible use data available in existing registers and other administrative sources – Primary data collection only, if no (timely) data available (or of bad quality) – Statistics Netherlands Act • From traditional to electronic data collection • Standardisation of statistical processes; multidata-source statistics; efficient sampling • Challenges must be faced while the available budget is constantly being reduced 6
Life cycle model (1) Labour market position Education - Working/non Income working - Occupation Health Consumption Demography - Economic activity - Year of birth Social - Nationality Demography capital Housing - Household composition Time use … Well-being - Etc. 7 Labour market position
Life cycle model (2) Labour market position Education Income Health Consumption Demography Housing Time use … Social capital Well-being 8
Life cycle model (3) me Ti Cases T+2 T Variables T+1 9
Life cycle model (4) 10
Life cycle model (5) Analysis possibilities: • State • Transitions between states • Duration time in a certain state e m Ti 11
Life cycle model (6) e T im 12
Relevant statistical information for policy and society • Domain specific • Transitions and durations within a domain • Relations between domains • Relations between transitions and durations between domains • Monitor information (long period) 13
Strategy for data collection (1) • Start with registers (e. g. population register, housing register, business register) • Add data from other administrative sources • Add data from business and household surveys • Match all these data at the micro level • Create a ‘data clearing house’ within the statistical office 14
Strategy for data collection (2) Variables Registers . . n All inhabitants Netherlands 1 Surveys 15
Strategy for data collection (3) Matching method for individual data RIN Longitudinal Administrative or Population Register survey data 16
Secondary data (1) Quality • Quality may be good for some basic registers, but not for all registers; monitoring quality is important • No sampling errors • No unit nonresponse • Many sources of non-sampling errors remain: – Item nonresponse – Measurement errors – Coverage errors 17
Secondary data (2) Challenges • Impact on the organisation, coordination, crossing departmental boundaries, change in culture • Influence of a statistical office on contents of registers is limited • Communication with register holders, e. g. about quality and changes • Quality control system (control surveys? ) • Comprehensive, standardised metadata system • Version control system for updates • Changing form surveys to registers without causing a trend break 18
How to get consistency of different data sources? • Harmonisation! (coverage, definitions, reference periods, etc. ) • Editing of all records at micro level by automated procedures • Only edit what needs to be edited (clear instructions are necessary!) • Make use of the technique of repeated weighting for survey data 19
Prototype of a micro database (1) X 1…XK Y 1…YM Z 1…ZR U 1…US LFS HS 20
Prototype of a micro database (2) Output inspired harmonisation: the one figure for one phenomenon idea Stat. Line: all statistical information on the web (via home page of Statistics Netherlands) http: //www. cbs. nl/en-GB/menu/home/default. htm 21
Conclusions Social Statistics develop in the direction of a permanent virtual census to be able to produce: – More crosstables over different domains – More longitudinal information – More flexible policy relevant output 22
Contents System of social statistical datasets (SSD) • Introduction to Statistics Netherlands • Examples of registers • Definition and driving forces of the SSD • The scope of the SSD • Core and satellites • The process • Linking the sources • Micro integration • Estimation aspects • Statistical confidentiality • Conclusions 23
Introduction to Statistics Netherlands (1) The Central Statistical Office (CBS) • almost all official statistics in the Netherlands • no regional offices • two buildings: The Hague (in the West) 24
Introduction to Statistics Netherlands (2) and Heerlen (in the South); both have about 1000 employees Mission The mission of Statistics Netherlands is to publish reliable and coherent statistical information that meets the needs of society. Position of the Statistical Office Statistics Netherlands is since 2004 a semi-independent organisation (still government funding) with about 2000 employees 25
Examples of registers Three kinds of registers • Population Register (PR) • Job register • Self-employed register • Education register • Occupation register • Income register • Social security register • Unemployment register • Pension register • Other registers on persons, families and households • Housing register • Other registers on properties, buildings and dwellings • General business register • Other registers on enterprises and establishments Common identifier: (numerical) address 26
Definition and driving forces of the SSD Definition: set of integrated microdata files with coherent and detailed demographic and socio-economic data on persons, households, jobs and benefits No remaining internal conflicting information Driving forces: • Virtual Census of 2001 • Better products: more coherence and flexibility 27
The scope of the SSD All relevant variables in the life cycle • Demography • Health • Education • Labour market position • Income • Consumption • Housing • Time use • Etc. 28
ite e SSDcore satellite e llit tel a satellite sa e s llit te lite tel sa sa sate ll satellite Core and satellites (1) 29
Core and satellites (2) Core: • contains only integral register information • contains the most important demographic and socio-economic information • contains only information that is used in at least two satellites 30
Core and satellites (3) Satellites are produced in two steps: • Copying and derivation of the relevant information from the core SSD • Adding of the unique information on a specific theme from registers and surveys 31
Core and satellites (4) Examples of current SSD satellites: • Labour market • Social security • Income • Education • Health care • Justice and security • Ethnic minorities • Social cohesion The development of more SSD-satellites has been planned 32
The process Already discussed: – Specify the information needed – Collection of registers – Surveys only additional Still to discuss: – Linking the sources – Micro integration – Estimation aspects – Statistical confidentiality 33
Linking the sources (1) • The Population Register is the backbone of the system for persons • All other files are matched exactly to the Population Register, • such that the true matches are maximised (aim: no missed matches) and the false matches (mismatches) are minimised 34
Linking the sources (2) Matching variables: • Social security and fiscal (SOFI) number (effectiveness close to 100%), since 2007 Citizen Service Number • Other personal identifiers: sex, date of birth, and address (effectiveness close to 100%) • Number of mismatches very low (close to 0%) 35
Micro integration (1) The aim of micro integration is: – To check the linked data and modify incorrect records, – in such a way that the results that are to be published are of higher quality than the original sources 36
Micro integration (2) To fulfil this demand an integrated process of: • data editing, • derivation of statistical variables, • and imputation is executed 37
Micro integration (3) Constraints and limitations: - Only variables that are to be published are micro integrated - Identity rules are necessary, e. g. the same variable in two sources or a relationship between two or more variables in one or more sources - No mass imputation 38
Estimation aspects Surveys are samples from the population If surveys are enriched with register information, estimations of the register part of the enriched survey will lead to inconsistencies with the counts from the entire register Statistics Netherlands developed the method of repeated weighting to solve these inconsistencies (aim: numerically consistent estimations) 39
Statistical confidentiality IDs Variables Characteristics Administrative sources Identifiers (PINs, sex, date of birth, address) IDs Variables Household surveys PERSONS BACKBONE full range of all persons as from 1995 IDs in sources are replaced by random Record Identification Numbers (RINs) 40
Conclusions The SSD diminishes the administrative burden and increases: – The efficiency of statistics production – The accuracy of statistical outputs – The possibilities for social policy research Safeguarding confidentiality is vital for the process of record linkage 41
Group work on registers and surveys (1) Key question: which census variables are missing in all the registers? Consider the following thirteen census variables: 1. Sex 2. Age 3. Country of citizenship 4. Marital status 5. Household position 6. Religious denomination 7. Country of birth 8. Household size 42
Group work on registers and surveys (2) 9. Place of residence one year prior to the census 10. Economic status 11. Level of educational attainment 12. Occupation 13. Branch of current economic activity A. Discuss the situation in the countries represented in your group or select some countries for further discussion 43
Group work on registers and surveys (3) B. Are those missing variables available is any survey? Discuss where those surveys may be used (legal aspect and agreement with survey organiser) for producing official statistics C. Can the surveys and registers be linked? Is this exact matching or is statistical matching necessary? Are there other important issues that affect the overall situation? 44
Group work on registers and surveys (4) D. Possibilities and limitations for further development of combining registers and surveys. What is the policy in the NSIs for further development? What are the possibilities and limitations for such a development? E. Prepare a short presentation (5 minutes per group) 45
Contents The Dutch virtual census (1) • History of the Dutch Census • The Dutch Census of 2011 • Data sources • Combining sources: micro linkage • Combining sources: micro integration • Conditions facilitating use of administrative sources • Miscellaneous aspects • Census tables • Micro macro method • Result on 2011 economic activity 46
Contents The Dutch virtual census (2) • Comparison with other countries • Comparison with other years • Harmonisation • Microdata availability • Data integration activities between the 2001 Census and the 2011 Census • Preparing the 2011 Census • Conclusions 47
History of the Dutch Census (1) TRADITIONAL CENSUS Ministry of Home Affairs: 1829, 1839, 1849, 1859, 1869, 1879 and 1889 Statistics Netherlands: 1899, 1909, 1920, 1930, 1947, 1960 and 1971 Unwillingness (nonresponse) and reduction expenses no more traditional censuses 48
History of the Dutch Census (2) ALTERNATIVE: VIRTUAL CENSUS 1981 and 1991: limited virtual censuses based on Population Register and surveys development 90’s: more registers → integrated set of registers and surveys, SSD 2001 and 2011: complete virtual censuses based on the SSD with information at the municipality level 49
The Dutch Census of 2011 is based on the Social Statistical Database (SSD) which • is a set of integrated microdata files with coherent and detailed demographic and socio-economic data on persons, households, jobs and benefits • has no remaining internal conflicting information is part of the European Census • Eurostat: coordinator of EU, accession and EFTA countries in the European Census Rounds • Census Table Programme, every 10 years Social statistics in the Netherlands develop in the direction of a permanent Virtual Census to be able to produce: • More crosstables over different domains • More longitudinal information • More flexible policy relevant output 50
Data sources Registers: • Population Register (PR) → illegal people excluded, homeless counted at last known address • Jobs file, containing all employees • Self-employed file, containing all self-employed • Fiscal administration • Social Security administrations • Pensions and life insurance benefits • Housing registers Surveys: • Survey on Employment and Earnings (SEE) stopped • Labour Force Survey data around Census Day • Housing surveys no longer necessary for the Census 51
Combining sources: micro linkage • Linkage key: Registers Citizen Service Number, unique Surveys Sex, date of birth, address (postal code and house number) • Linkage key replaced by RIN-person • Linkage strategy Optimizing number of matches Minimizing number of mismatches and missed matches 52
Combining sources: micro integration • Collecting data from several sources more comprehensive and coherent information on aspects of a person’s life • Compare sources - coverage - conflicting information (reliability of sources) • Integration rules - checks - adjustments - imputations • Optimal use of information quality improves • Example: job period vs. benefit period 53
Conditions facilitating use of administrative sources • Legal base (Statistics Act) • Public approval (‘Big Brother is watching you’) • Cooperation among authorities (mainly government organisations) • Comprehensive and reliable register system (administrative versus statistical quality) • Unified identification system (preferably unique ID-numbers) 54
Miscellaneous aspects (1) • Stable identifiers • Stability of registers • Only edit what needs to be edited (by automated procedures) • Dates of real events versus dates of registration • Derived variables (example: current activity status) • Impact on the organisation (change of culture) • Communication with register holders 55
Miscellaneous aspects (2) Output inspired harmonisation (coverage, definitions, reference periods): the one figure for one phenomenon idea Stat. Line: all statistical information on the web (via home page of Statistics Netherlands) http: //www. cbs. nl/en-GB/menu/home/default. htm 56
Census tables (1) Preliminary work before tabulating Census Programme definitions: not always clear and unambiguous, e. g. economic activity Priority rules • (characteristics of) main job (highest wage) • employee or employer • job or (partially) unemployed • job or attending education • job or retired • engaged in family duties or retired • age restrictions Tabulating register variables: Simply straightforward counting from SSD register data 57
Census tables (2) Tabulating survey (and register) variables Mass imputation? • Pro’s: reproducible results • Con’s: danger of oddities in estimates (e. g. highly educated baby) Traditional Weighting? • Pro’s: simple, reproducible results (if same microdata and weights) • Con’s: no overall numerical consistency between survey and register estimates Demand for overall numerical consistency • one figure for one phenomenon idea • all tables based on different sources (e. g. surveys) should be mutually consistent 58
Census tables (3) Ethnicity: register Education: survey 1 and survey 2 Employment status: survey 2 Estimate: T 1: educ x ethnic and T 2: educ x employ ethnic 1. . . k Register educ x ethnic not. NL NL Total educ. Lo 20 29 9 42 29 71 Survey 2 51 Total Survey 1 employ 1. . . m 49 educ. Hi educ. Lo. . . Hi 100 employ x educ ethnic Total not. NL 30 NL employed nonemployed Total educ. Lo 70 32 20 52 educ. Hi 28 20 48 Total 60 40 59 100
Census tables (4) Repeated Weighting (RW) : tool to achieve numerical consistency (VRD-software) Basic principles of RW: • estimate table on most reliable source (mostly source with most records, e. g. register) • estimate tables by calibrating on common margins of the current table and tables already estimated (auxiliary information) • repeatedly use of regression estimator: - initial weights (e. g. survey weights) calibrated as minimal as possible - lower variances - no excessive increase of (non-response) bias (as long as cell size>>0) • each table has its own set of weights 60
Census tables (5) Calibrate on ethnic, then on educ x ethnic 1. . . k educ. Lo. . . Hi Register Survey 1 employ 1. . . m 2 educ x ethnic not. NL NL Total educ. Lo 20 30 50 educ. Hi 10 40 50 Total 30 70 Survey 2 100 employ x educ Total not. NL 30 70 nonemployed 31 19 50 educ. Hi NL employed educ. Lo 1 ethnic 3 Total 30 20 50 Total 61 39 100 61
Micro macro method (1) Repeated Weighting works nicely, but in the 2011 Census a new requirement was introduced: hypercubes (= high dimensional tables) Problem: Very detailed tables contain many sample zeros that RW cannot handle Solution 1: estimate subhypercubes Solution 2: micro macro method (an IPF method) was introduced to estimate the interior of subhypercubes containing LFS variables 62
Micro macro method (2) Results of the micro macro method are published if two conditions are fullfilled: 1. table margins estimated with RW are small enough 2. number of records in estmated cells are large enough Criteria: 1. estimated relative inaccuracy of at most 20 percent (i. e. the estimated margins amount to 40 percent at most) which corresponds to a threshold of 25 persons 2. only table cells based on 5 or more persons are published 63
Result on 2011 economic activity 8. 9% 4. 1% Employed 16. 6% Unemployed 49. 1% Under 15 years Pension or capital income recipients Students (not economically active) Homemakers and others 17. 5% 3. 8% 64
Comparison with other countries Traditional Census (complete enumeration): Most countries in the world (including the UK and the US) Traditional Census (partial enumeration) and Registers: Some countries (e. g. Germany, Poland Switzerland) Rolling Census: France Fully or largely register-based (Virtual) Census: Five Nordic countries (Iceland, Norway, Sweden, Finland Denmark), the Netherlands, Belgium, Austria and Slovenia 65
Comparison with other years Inhabitants and household size Number of inhabitants (x mln) / Mean houshold size 18 16 14 12 10 8 6 4 2 0 1829 1839 1849 1859 1879 1889 1899 1909 1920 1930 1947 1960 1971 1981 1991 2001 2011 Census year Number of inhabitants Mean household size 66
Harmonisation (1) More information about the Dutch traditional Censuses (including those of 1960 and 1971): http: //www. volkstellingen. nl/en/ For 1960 and 1971 the same variables as for 2001 • if not available: constructed based on existing variables in Census data Variables not internationally harmonised (e. g. sex, age, marital status, household position, country of birth, economic status, household size and country of citizenship) • same classification and priority rules as for 2001 67
Harmonisation (2) Household size and country of citizenship: • missing for 1960 Religious denomination (philosophy of life): • only for 1960 and 1971 Place of residence one year prior to the census: • only for 2001 International classifications • Branch of current economic activity: ISIC / NACE • Occupation: ISCO • Level of educational attainment: ISCED 68
Harmonisation (3) 1960 1971 2001 Sex X X X Age X X X Country of citizenship Marital status X X X Household position X X X Religious denomination X X Country of birth X X X Household size Place of residence one year prior to the census X Economic status X X X Level of educational attainment X X X Occupation X X X Branch of current economic activity X X X 69
Microdata availability One percent samples for three years (1960, 1971 and 2001) IPUMS (Integrated Public Use Microdata Series): http: //www. ipums. org/international/index. html Weighting to population totals Protecting according to rules for public use files Microdata sets for all three years available for research! DANS (Data Archiving and Networked Services): http: //www. dans. knaw. nl/en/ 70
Data integration activities between the 2001 Census and the 2011 Census (1) • Tables (http: //www. cbs. nl/nl. NL/menu/themas/dossiers/his torischereeksen/publicaties/volkstelli ng-2001/2003 -volkstellingexcel. htm) • Book and extra chapter (http: //www. cbs. nl/nl. NL/menu/themas/dossiers/his torischereeksen/publicaties/volkstelli ng-2001/2001 -b 57 -pub. htm) 71
Data integration activities between the 2001 Census and the 2011 Census (2) • Integrated Public Use Microdata Series (https: //international. ipums. org/international) • Lectures (Conferences, Universities, Research institutes, Statististical offices) • ESTP-course Registers in Statistics (Oslo) • International Statistical Seminar Eustat in Bilbao (http: //www. eustat. es/prodserv/seminario_i. html) • Digitalizing (http: //www. volkstellingen. nl/en/) • Recommendations and register-based statistics • CENEX on ISAD (http: //cenex-isad. istat. it) • European census regulations 72
Preparing the 2011 Census • Sources (the PR as backbone of the census, changes in contents and quality of registers, remaining information from LFS) • Estimation method (repeated weighting, new version of the software, fall-back option of weighting to PR, zero cells problem) • Statistical Disclosure Control of the hypercubes (Workshop on SDC of Census Data in April 2012) • Tabular data in SDMX format and the Census Hub 73
Conclusions (1) • A Dutch Virtual Census: yes, we can! • Micro integration remains important • Repeated weighting was a success Advantages: • Relatively cheap (small cost per inhabitant) • Quick (short production time) Disadvantages: • Dependent on register holders (statistics is not their priority), timeliness of registers, concepts and population of registers may differ from what is needed (keep good relations with the register holders!) • Publication of small subpopulations sometimes difficult or even impossible because of limited information 74
Conclusions (2) Other aspects: • Less attention for the results of a virtual census than for a traditional one • Difficult to keep knowledge and software up-todate (Census is running every ten years) • Enormous international interest in virtual censuses • A lot of interesting census work in the coming years! 75
Time for questions and discussion 76
Скачать презентацию Combined use of data from registers and sample
6b8343c8461253eb871e74f5c5c77266.ppt