UNECE Work Session on Statistical Data Editing Vienna April 2008 Topic ii – Editing Administrative Data and Combined Sources

Introduction Statistical Agencies rely on administrative data to improve the quality of statistics, reduce costs and response burden Administrative data are not originally designed for use as statistical data and need to undergo extensive processing and editing In recent years, more emphasis on the use of tax data to augment business statistics and register data to augment social and economic data

Introduction Combining multiple sources of data presents new challenges: ensuring quality in line with statistical standards and coherence across different sources. Papers cover: –Effective methods for adjusting administrative data to statistical use –Improving the usability of business and population registers –Construction of quality statistical databases using effective E&I strategies which ensure correct coverage, consistent and clean records –Enhancing the quality and efficiency of estimates from surveys

Introduction Papers: –WP.8Italy: The editing process in the Italian short- term survey on Labour Cost based on administrative data –WP.9 New Zealand: E&I of administrative data used for producing business statistics –WP.10 Norway: Role of edit and imputation in integration of sources for structural business statistics –WP.11 Norway: Prediction and imputation in ISEE: tools for more efficient use of combined data sources

Introduction Papers: –WP.12 Austria: Quality of administrative data – a challenge for the maintenance of the statistical business register –WP.13 France: The future system of French structural business statistics: the role of the estimates –WP.14 Italy: Combining survey and administrative data in the Italian EU-SILC experience: positive and critical aspects –WP.15 Netherlands: Editing Strategies for VAT Data

Presentations

The editing process in the Italian short-term survey on labour cost based on administrative data M. Carla Congia, Silvia Pacini and Donatella Tuzi – Italian National Statistical Institute (Istat) Steps in process: –Preliminary checks on administrative data and retrieval of statistical variables –Micro data editing (cross sectional and longitudinal checks) –Imputation of eligible unit non-responses –Large enterprise checks and combination with survey data –Macro editing based on time series analysis

The editing process in the Italian short-term survey on labour cost based on administrative data Interesting points –Integration of administrative and survey data and identifying errors –Combining many processes in an integrated setting –Recognition of the importance of metadata for administrative data – changes in concepts and definitions –Macro editing using time series methods for automated detection of outliers

E&I of administrative data used for producing business statistics Vera Costa, Frances Krsinich and Rudi Van der Mescht – Statistics New Zealand Challenges with using administrative data from the private sector –Electronic Card Transactions Data obtained as aggregated data from switch companies –Must rely on companies for ensuring quality data –Discussion of time series models for identifying outliers and carrying out imputation

E&I of administrative data used for producing business statistics Building a longitudinal business database –Integrating survey data, tax data and business sampling frame using a deterministic record linkage process –Donor imputation for missing/erroneous data from tax files –Expanding methods of imputation to take into account historical values and other fine-tuning mechanisms Interesting points –Advantages and disadvantages of time series methods for macro editing on aggregated administrative data –Need to consider practicality and feasibility for large scale production systems when analyzing imputation methods

The Role of E&I in integration of sources for structural business statistics Svein Gasemyr, Svein Nordbotten and Morton Anderson – Statistics Norway Integrated longitudinal business database from multiple sources –Estimate enterprise accounts distribution for complex enterprises –Aggregations from Job files –Imputation of input and output production variables, imputation of non-response and out of survey units –Corrections to enhance record linkage –Need for more computer based methods and support for editing

The Role of E&I in integration of sources for structural business statistics Standardized modules for editing and estimation –Imputation and estimation carried out interactively –Inspect effect of changed values on the estimates Interesting points –Quality information for integrated databases as opposed to single source databases with emphasis on errors in linking data and inconsistencies

Prediction and imputation in ISEE: tools for more efficient use of combined data sources Li-Chun Zhang and Svein Nordbotten – Statistics Norway Standardization of data processing for combined data sources –Editing individual data –Estimation of population parameters Integrating multiple sources by constructing a complete population data file –Imputation for non-response and out of sample –Nearest neighbour imputation method with restrictions on totals

Prediction and imputation in ISEE: tools for more efficient use of combined data sources Interesting points –Good review and discussion of imputation methods –Innovative new method for imputing out of sample units –Development of a generic statistical application

The Future System of French Structural Business Statistics: the Role of Estimates Philippe Brion - INSEE Combining administrative sources and a statistical survey –Breakdown of turnover and NACE code only available in the sample Analysis of statistical estimates produced by mass-imputation versus weighting –Imputation of APE code for out of sample units can be biased –Weights calibrated to 3-digit APE code with adjustments based on the survey outcome at the 4-digit level

The Future System of French Structural Business Statistics: the Role of Estimates Interesting points –Good discussion of advantages and disadvantages of mass imputation versus weighting to obtain population estimates –Consideration of editing strategies: micro edits and selective editing based on scores and “jack-knifed” ratios

Combining Survey and Administrative data in the Italian EU-SILC Experience Claudio Ceccarelli, Lucia Coppola, Andrea Cutillo and Davide Di Laurea Use of administrative data in the social survey EU- SILC –Tracking individuals for a longitudinal survey –Linking tax registers to reduce impact of item non- response and other measurement errors (recall effects, telescoping, etc.) Problems related to timeliness and comparability of data sources –Need for integrated processing systems, understanding of complexities, more time to process data

Combining Survey and Administrative data in the Italian EU-SILC Experience Interesting points –A good discussion is provided on the advantages and disadvantages of incorporating administrative data at different stages of the survey process –Interesting analysis of estimation methods for calculating survey weights

Quality of Administrative Data – a challenge for the maintenance of the Statistical Business Register Norbert Rainer – Statistics Austria Main administrative data sources for the Business Register Quality issues –Linking data sources –Different definitions –Continuity procedures –Missing data Improvement strategies

Quality of Administrative Data – a challenge for the maintenance of the Statistical Business Register Interesting points –Data issues leading to a need for using imputation Data available only for a higher aggregation level within the business Timeliness Annual data only, when monthly data is needed Not all activities covered Data available only for enterprises above a certain threshold

Editing Strategies for VAT Data Peter Kruiskamp – Statistics Netherlands VAT data –Current use: auxiliary variable –Future use: source for turnover data (small / medium businesses) Editing strategy –Micro editing on the fiscal units level –Data handling on the statistical units level –Macro editing on the aggregates level

Editing Strategies for VAT Data Data used for the production of the Short-Term Statistics – data frequency Interesting points –Consideration of time series model for estimating VAT to overcome seasonal effects –Discussion of cut off points for identifying outliers –Need to move from using VAT data as auxiliary versus VAT data as a source of data

Questions for discussion Editing Administrative Data –Combine collections then edit versus edit each collection then combine –Editing / imputation of back data –Keeping track of changes in administrative data definitions –Parameters for outlier detection under multiple sources of data –Use of time series models to identify outliers and impute/estimate unit record values: advantages and disadvantages –Automation of macro editing when a large number of series are produced

Questions for discussion Assessing quality –New statistical tools/methods for assessing coherency between sources and linking errors –Types of quality indicators for integrated databases –The impact of the timeliness of the data sources on the quality of the data –Does the need for practical and feasible production systems reduce the quality of the data –Variance estimation in combined data sources, especially when an auxiliary is an estimate or when massive imputation is carried out

Questions for discussion Weighting versus mass-imputation –Most papers opted for mass imputation and square datasets and a few papers opted for weighting – pros and cons –Methods for building “square” datasets when linking administrative sources to survey data

Thank you for your attention