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

2. 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 ESD Integration

3. –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 ESD Integration phase 1

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

5. Table 1. Available annual sources on enterprise level for six key variables. GBRVATCTTSSSDSBSSEEPC Number of employed persons XXXX Gross wages and salaries XXXXX Total labour costsXX Net turnoverXXXX Purchase valueXXX ProfitXX

6. Table 2. Causes for differences between sources at publication and/or micro level. Causes for differences at publication level (only) Causes for differences at publication and micro level Difference in target populationMatching error Difference in weightsDifference in variable definition Classification errorDifference in measurement time (period) Measurement errors in variables Processing errors in variables, e.g. due to wrong unit transformations Difference in editing strategy Observed versus imputed value Difference in imputation method

7. - 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 Steps in integration process I

8. - 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 Steps in integration process II

9. –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 Long-term challenges