1. The Use of Administrative Sources for Statistical Purposes Matching and Integrating Data from Different Sources

2. What is Matching? Linking data from different sources Exact Matching - linking records from two or more sources, often using common identifiers Probabilistic Matching - determining the probability that records from different sources should match, using a combination of variables

3. Why Match? Combining data sets can give more information than is available from individual data sets Reduce response burden Build efficient sampling frames Impute missing data To allow data integration

4. Models for Data Integration Statistical registers Statistics from mixed source models –Split population model –Split data approach –Pre-filled questionnaires –Using administrative data for non- responders –Using administrative data for estimation Register-based statistical systems

5. Statistical Registers

6. Mixed Source Models Traditionally one statistical output was based on one statistical survey Very little integration or coherence Now there is a move towards more integrated statistical systems Outputs are based on several sources

7. Split Population Model One source of data for each unit Different sources for different parts of the population

8. Split Population Model

9. Split Data Approach Several sources of data for each unit

10. Pre-filled Questionnaires Survey questionnaires are pre-filled with data from other sources where possible Respondents check that the information is correct, rather than completing a blank questionnaire This reduces response burden...... but may introduce a bias!

11. Example

12. Using Administrative Data for Non-responders Administrative data are used directly to supply variables for units that do not respond to a statistical survey Often used for less important units, so that response-chasing resources can be focused on key units

13. Using Administrative Data for Estimation Administrative data are used as auxiliary variables to improve the accuracy of statistical estimation Often used to estimate for small sub- populations or small geographic areas

14. Register- based Statistical Systems

15. Matching Terminology

16. Matching Keys Data fields used for matching e.g. Reference Number Name Address Postcode/Zip Code/Area Code Birth/Death Date Classification (e.g. ISIC, ISCO) Other variables (age, occupation, etc.)

17. Distinguishing Power 1 This relates to the uniqueness of the matching key Some keys or values have higher distinguishing powers than others High - reference number, full name, full address Low - sex, age, city, nationality

18. Distinguishing Power 2 Can depend on level of detail –Born 1960, Paris –Born 23 June 1960, rue de l’Eglise, Montmartre, Paris Choose variables, or combinations of variables with the highest distinguishing power

19. Match A pair that represents the same entity in reality A  A

20. Non-match A pair that represents two different entities in reality AB 

21. Possible Match A pair for which there is not enough information to determine whether it is a match or a non-match Aa 

22. False Match A pair wrongly designated as a match in the matching process (false positive) AB =

23. False Non-match A pair which is a match in reality, but is designated as a non-match in the matching process (false negative) AA 

24. Matching Techniques

25. Clerical Matching Expensive Inconsistent Slow Intelligent

26. Automatic Matching Cheap Consistent Quick Limited intelligence

27. The Solution Use an automatic matching tool to find obvious matches and no-matches Refer possible matches to specialist staff Maximise automatic matching rates and minimise clerical intervention

28. How Automatic Matching Works

29. Standardisation Generally used for text variables Abbreviations and common terms are replaced with standard text Common variations of names are standardised Postal codes, dates of birth etc. are given a common format

30. Blocking If the file to be matched against is very large, it may be necessary to break it down into smaller blocks to save processing time –e.g. if the record to be matched is in a certain town, only match against other records from that town, rather than all records for the whole country

31. Blocking Blocking must be used carefully, or good matches will be missed Experiment with different blocking criteria on a small test data set Possible to have two or more passes with different blocking criteria to maximise matches

32. Parsing Names and words are broken down into matching keys e.g.Steven Vale  stafan val Stephen Vael  stafan val Improves success rates by allowing matching where variables are not identical

33. Scoring Matched pairs are given a score based on how closely the matching variables agree Scores determine matches, possible matches and non-matches

35. How to Determine X and Y Mathematical methods e.g. Fellegi / Sunter method Trial and Error Data contents and quality may change over time so periodic reviews are necessary

36. Enhancements Re-matching files at a later date reduces false non-matches (if at least one file is updated) Link to data cleaning software, e.g. address standardisation

37. Matching Software Commercial products e.g. Informatica, Trillium, Automatch In-house products –Jasper (Statistics Canada) –Relais (ISTAT) Open-source products e.g. FEBRL No “off the shelf” products - all require tuning to specific needs

38. Internet Applications Google (and other search engines) –www.google.com Cascot – an automatic coding tool based on text matching –http://www2.warwick.ac.uk/fac/soc/ier/publicati ons/software/cascot/choose_classificatio/ Address finders e.g. Postes Canada –http://www.postescanada.ca/tools/pcl/bin/adva nced-f.asp

39. Software Applications Trigram method applied in SAS code (freeware) for matching in the Eurostat business demography project Works by comparing groups of 3 letters, and counting matching groups

40. Trigram Method Match “Steven Vale” –Ste/tev/eve/ven/en /n V/ Va/Val/ale To “Stephen Vale” – Ste/tep/eph/phe/hen/en /n V/ Va/Val/ale – 6 matching trigrams And “Stephen Vael” – Ste/tep/eph/phe/hen/en /n V/ Va/Vae/ael – 4 matching trigrams Parsing would improve these scores

41. Matching in Practice

42. Matching Records Without a Common Identifier The UK Experience by Steven Vale (Eurostat / ONS) and Mike Villars (ONS)

43. The Challenge The UK statistical business register relies on several administrative sources It needs to match records from these different sources to avoid duplication There is no system of common business identification numbers in UK

44. The Solution Records are matched using business name, address and post code The matching software used is Identity Systems / SSA-NAME3 Matching is mainly automatic via batch processing, but a user interface also allows the possibility of clerical matching

45. Batch Processing 1 Name is compressed to form a namekey, the last word of the name is the major key Major keys are checked against those of existing records at decreasing levels of accuracy until possible matches are found The name, address and post codes of possible matches are compared, and a score out of 100 is calculated

46. Batch Processing 2 If the score is >79 it is considered to be a definite match If the score is between 60 and 79 it is considered a possible match, and is reported for clerical checking If the score is <60 it is considered a non-match

47. Clerical Processing Possible matches are checked and linked where appropriate using an on-line system Non-matches with >9 employment are checked - if no link is found they are sent a Business Register Survey questionnaire Samples of definite matches and smaller non-matches are checked periodically

48. Problems Encountered 1 “Trading as” or “T/A” in the name e.g. Mike Villars T/A Mike’s Coffee Bar, Bar would be the major key, but would give too many matches as there are thousands of bars in the UK. Solution - split the name so that the last word prior to “T/A” e.g. Villars is the major key, improving the quality of matches.

49. Problems Encountered 2 The number of small non-matched units grows over time leading to increasing duplication Checking these units is labour intensive Solutions –Fine tune matching parameters –Re-run batch processes –Use extra information e.g. legal form / company number where available

50. Future Developments Clean and correct addresses prior to matching using “QuickAddress” and the Post Office Address File Links to geographical referencing Business Index - plans to link registers of businesses across UK government departments Unique identifiers?