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Handling inconsistencies in integrated business data

Handling inconsistencies in integrated business data. Bonn 25-27 September 2006 * Jeffrey Hoogland Ilona Verburg. ESD Integration. Goals Improvement of transparency and quality of business data sources

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Handling inconsistencies in integrated business data

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  1. Handling inconsistencies in integrated business data Bonn 25-27 September 2006 * Jeffrey Hoogland Ilona Verburg

  2. ESD Integration • Goals • Improvement of transparency and quality of business data sources • Integration of business data for enterprises for FATS, National Accounts, SBS+, CEREM (external users such as CPB) • Improvement of consistency of data sources • Improvement of usability of business registers to determine reliable aggregates

  3. ESD Integration phase 1 • 5 business registers and 3 annual business surveys for 2001-2004 • 6 key variables • Enterprises with less than 100 employees • Goal: integrated data, consistent on aggregated level (publication cell  size class group) for 2004 • Development of methodology • methods for filtering outliers in registers • methods for weighting of incomplete registers • methods for detecting influential inconsistencies at micro level • List of causes, consequences and solutions for inconsistencies

  4. Annual business data sources GBR VAT CT TS JSSD SBS GFCF SEE ICT PC R&D surveys registers

  5. Table 1. Available annual sources on enterprise level for six key variables.

  6. Table 2. Causes for differences between sources at publication and/or micro level.

  7. Steps in integration process I • - Tune target populations • - Synchronize classifications (NACE, size class) • - Harmonization of variables and units • - Match data on enterprise level • - Correct obvious mistakes • - Filter and weight incomplete registers

  8. Steps in integration process II • - Filter and weight incomplete registers • - Compute temporary aggregates • - Indicate inconsistent aggregates • - Detect influential inconsistent records • - Solve matching errors, edit influential errors, and adapt weights • - Compute consistent aggregates

  9. Long-term challenges • Use the Fellegi-Holt principle to obtain consistent integrated micro-data • Use repated weighting techniques to obtain consistent aggregates • Develop a general editing system for business registers and surveys • Minimize the burden for respondents using a maximum number of registers and a minimum number of surveys

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