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

UNECE Statistical Division 2March 2011 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

UNECE Statistical Division 3March 2011 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 Act Plan CheckDo Continuous improvement Quality Time By Deming

UNECE Statistical Division 4March 2011 Production Process  Bring the collected data to the level of the intended statistical output! Publication Collection of data Correction of systematic errors in data Index calculation Check for the most important observations

UNECE Statistical Division 5March 2011 Data Collection Statistical 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

UNECE Statistical Division 6March 2011 Source: Statistics Finland, Strategy for economic statistics System of Statistics

UNECE Statistical Division 7March 2011  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 Data Collection Questionnaire Design

UNECE Statistical Division 8March 2011  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 Data Collection Sampling in Practice

UNECE Statistical Division 9March 2011 > Business Register to be kept up-to-date with new units Total population of units in the Business Register Large unitsMedium unitsSmall units Stratification by economic activity Covered on a complete enumeration basis Covered by sampling Covered mainly by administrative sources or administrative sources

UNECE Statistical Division 10March 2011  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. Data Collection Administrative Data Sources

UNECE Statistical Division 11March 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 Data Collection Pros and Cons of Admin Data?

UNECE Statistical Division 12March 2011  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 Data Collection Administrative Data and Quality

UNECE Statistical Division 13March 2011  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! Data Collection Legislative Issues

UNECE Statistical Division 14March 2011 Compilation Central Role of VAT Data Source: Statistics Finland 1(1)

UNECE Statistical Division 15March 2011 VAT e.g units Turnover Estimates for output and missing data Business Register e.g units Unit IDs Activity code Location Mergers LKAU (regional) 1. release 2. release revision optimal sampling small & medium enterprises feedback to BR Sample e.g units Turnover Mergers sample, basic info updates to BR activity of units combining Compilation Linking Admin and Survey Data

UNECE Statistical Division 16March 2011 Compilation Data 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

UNECE Statistical Division 17March 2011 Compilation Treating 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

UNECE Statistical Division 18March 2011 Compilation Comparing Unit Level Data Change 115%

UNECE Statistical Division 19March 2011 Compilation Impact on the Results index

UNECE Statistical Division 20March 2011 Compilation Non-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 1) Errors 2) Actual changes that are comparable 3) Actual changes that are non-comparable UN Guide on the Impact of Globalization on National Accounts > helps with STS as well

UNECE Statistical Division 21March 2011 Compilation Example of NCCs Unit A Turnover = 100 million Unit B Turnover = 75 million Exchange of goods 50 million Turnover drops by one third due to a merger! No change in the level of activity! Unit AB Turnover = (100-50) + 75 = 125 million Previous yearCurrent year

UNECE Statistical Division 22March 2011 Compilation Alternative Treatments of NCCs 1.All changes are recorded as they are (actual) − Contaminated with apparent, non-comparable changes − Difficult to obtain a picture of economic reality + Simplicity 2.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

UNECE Statistical Division 23March 2011 Compilation Alternative Treatments of NCCs 3.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 Firm X

UNECE Statistical Division 24March 2011 Compilation Confrontation 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

UNECE Statistical Division 25March 2011 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