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STS Compilation with Multiple Data Sources

UNECE Workshop on Short-Term Statistics (STS) and Seasonal Adjustment 14 – 17 March 2011, Astana, Kazakhstan. STS Compilation with Multiple Data Sources. Anu Peltola Economic Statistics Section, UNECE. Overview. Data collection Sampling Administrative data

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STS Compilation with Multiple Data Sources

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  1. UNECE Workshop on Short-Term Statistics (STS) and Seasonal Adjustment 14 – 17 March 2011, Astana, Kazakhstan STS Compilation with Multiple Data Sources Anu PeltolaEconomic Statistics Section, UNECE

  2. Overview • Data collection • Sampling • Administrative data • Combining multiple data sources • Compilation of results • Data editing • Non-response and weighting • Treatment of non-comparable changes • Publication • Improvement

  3. Act Plan Check Do Quality Continuous improvement Time Theoretical Concept – A Key to Good Quality • Define the purpose of an indicator • Links to the real world • What should it describe? • Who are the users/uses (internal/external)? • Possible data sources • Links to other statistics • Differences in concepts, scope, methods • Goal variables – national accounts/SBS • Regular benchmarking • Follow-up of differences By Deming

  4. Check for the most important observations Correction of systematic errors in data Index calculation Collection of data Publication Production Process • Bring the collected data to the level of the intended statistical output!

  5. Data CollectionStatistical Units • Corner stones of business statistics • Legal unit -> enterprise (services) -> enterprise groups • Establishment (for industry/construction) • Business registers are fundamentally important • Bridge between administrative and statistical units • Definition of the economic activity class (ISIC/NACE) • Improve its comprehensiveness – use as a frame • Examine opportunities to use administrative data • Interactive: update with information from STS UN: International recommendations for the Index of Industrial Production & EC: STS Metholodological manual

  6. System of Statistics Source: Statistics Finland, Strategy for economic statistics

  7. Data CollectionQuestionnaire Design • Give clear instructions • Explain the concepts to the respondents • Revisions to earlier months • Aim to pre-fill the questionnaire with data given earlier • Leave space for reporting revisions • Always test changes to questionnaires • Inform the respondents of the use of data • Develop useful feedback for respondents • your company compared to others in the same activity

  8. Data CollectionSampling in Practice • Many surveys are for units above a size threshold • Burdensome and problems with the coverage of small units • Based on business register and periodically reviewed • In drawing a sample, special attention to be paid to: • Level of details to be published • Resources available • Accuracy and timeliness required • Response burden • Simple/stratified sampling by activity and size

  9. Total population of unitsin the Business Register > Business Register to be kept up-to-date with new units Stratification by economic activity Large units Medium units Small units Covered on a complete enumeration basis Covered by sampling Covered mainly byadministrative sources or administrative sources

  10. Data CollectionAdministrative Data Sources • Administrative registers or datasets can be used as: • Single source in their own right • Frame for sampling via the Business Register • Complementary source • Validation • Data source for small enterprises • For STS limited administrative sources available: • VAT (value added tax) • Social security data (employment and labor cost) • Building permits, etc.

  11. Data CollectionPros and Cons of Admin Data? + Reduction of response burden + Reduction of costs, data collection and manual work + Total populations - detailed classifications/regional indicators + Better quality and coverage (of smallest units) • Data content, units, concepts and definitions may differ • Dependence on few large data suppliers • Timeliness - may require use of estimation • Access and confidentiality • Non-observed economy unlikely to be included • Requires good IT capacity by the supplier and the NSO

  12. Data CollectionAdministrative Data and Quality • National ID-system for enterprises • New production methods: • to correct for negative values and different concepts • slow accumulation > estimation of missing data • The most important units to direct collection • Active co-operation with large enterprises • Development of questionnaires: • Simplification – part of information from registers • Efficiency – electronic data collection

  13. Data CollectionLegislative Issues • Compulsory to use existing data (if suitable) in statistics production • Guaranteed access to administrative sources • State government and social security institutions obliged to deliver their data to the NSO • Free of charge or compensation of direct costs • Co-operation in making changes in data collection • To ensure data confidentiality • Individual data collected for statistics should not be handed over to any use other than statistics or research!

  14. 1 ( 1 ) CompilationCentral Role of VAT Data Source: Statistics Finland

  15. sample, basic info • Sample • e.g.2000 units • Turnover • Mergers feedback to BR CompilationLinking Admin and Survey Data 1. release revision 2. release • Business Register • e.g. 290 000 units • Unit IDs • Activity code • Location • Mergers • LKAU (regional) combining small & medium enterprises • VAT • e.g.250 000 units • Turnover • Estimates for outputand missing data optimal sampling updates to BRactivity of units

  16. CompilationData Control and Editing • Studying data to identify errors • Detect errors that have a significant influence • Check whether values are within given ranges • Check whether values for related variables are coherent • Compare to past responses (previous months and a year ago) • Give top priority to outliers and errors that have the largest impact on the results • Outlier values require careful treatment • May be correct but caused by unusual circumstances Source: Methodology of Short-Term Business Statistics, EC

  17. CompilationTreating Non-Response • Controlling response burden • Better planning of data collection process • Offering various channels for respondents • Reducing the effect of non-response • Alternative source, e.g. administrative data • Imputation based on historical data • Mean value imputation, donor/nearist neighbour, regression of variables

  18. Change 115% CompilationComparing Unit Level Data

  19. CompilationImpact on the Results index

  20. CompilationNon-Comparable Changes (NCCs) • Structural changes in the population: • New units are set up and others stop existing • Units may be taken over, merged or split up • Units may expand, contract or change their activities • Reasons for large changes • Errors • Actual changes that are comparable • Actual changes that are non-comparable • UN Guide on the Impact of Globalization on National Accounts > helps with STS as well

  21. Previous year Current year Unit AB Turnover = (100-50) + 75= 125 million CompilationExample of NCCs Unit A Turnover = 100 million Exchange of goods 50 million Unit B Turnover = 75 million Turnover drops by one third due to a merger! No change in the level of activity!

  22. CompilationAlternative Treatments of NCCs • All changes are recorded as they are (actual) • Contaminated with apparent, non-comparable changes • Difficult to obtain a picture of economic reality • Simplicity • Panel method • Only same units in both periods are included • Start-ups and closures would be cancelled out • Seriously biased results in highly dynamic populations • Simplicity

  23. Firm X Firm X CompilationAlternative Treatments of NCCs • Overlapping method • Actual comparable changes are not adjusted • Other changes are made comparable by a. Collecting comparable information (largest units) b. Replacing non-comparable figure by an estimate c. Taken the unit out of calculation (no effect to results) • Requires more work • Results reflect actual changes in economic activity

  24. CompilationConfrontation with Other Sources • Regular confrontation may reveal discrepancies • Aim at coherence:value = price x output • First at the aggregated level and where necessary at lower levels (largest units) • Knowledge of differences between statistics helps communication with users • Quality reviews of indicators to be undertaken

  25. New Requirements for STS? • Globalization • Internationally comparable data needed • Treatment of more complex business activities • Increasing amount of services • Output and price measures, industrial services • Detection of turning points • Longer time series and seasonal adjustment • Coherence • Compare to National Accounts and between price/volume/value indicators

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