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Impact of using fiscal data on the imputation strategy of the Unified Enterprise Survey of Statistics Canada Ryan Chepita, Yi Li, Jean-Sébastien Provençal, Chi Wai Yeung Statistics Canada ICES III, Montréal, June 2007. Goals.

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Goals

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  1. Impact of using fiscal data on the imputation strategy of the Unified Enterprise Survey of Statistics CanadaRyan Chepita, Yi Li, Jean-Sébastien Provençal, Chi Wai YeungStatistics CanadaICES III, Montréal, June 2007

  2. Goals • To illustrate the challenges of applying a centralized E and I strategy to a broad range of industrial sectors • To discuss the changes put in place due to the increasing use of fiscal data • To discuss one approach used to quantify the overall E and I effect

  3. Outline • Overview of the Unified Enterprise Survey (UES) • Survey content • Imputation strategy • Use of fiscal data • Challenges • Diagnostic tool • Conclusion

  4. Overview of the UES • Annual business survey • Initiated with 7 industries in 1997 • Presently integrates over 40 industries covering the major sectors of the economy • 950K establishments in the population • 127K establishments in the sample

  5. Overview of the UES • Stratified sampling design • NAICS, province, and size in terms of revenue • Data collection • Mail out survey, fax and phone follow-up • Edit and Imputation • Estimation • H.-T. for totals and provincial and industrial breakdowns

  6. Survey content • 2 or 3 Key variables • Total revenue and total expenses • Similar concepts from one industry to another • A lot of details (over 50 variables) • Totals breakdowns • By province, type of expenses or source of revenue • Industry specific • Can be revised from year to year

  7. Survey content • Example : manufacturing sector Details Key

  8. Imputation Strategy • Categories of non-response • Category 1: Partial response with at least 1 key variable reported • Category 2: Total non-response with historical data • Category 3: Total non-response without historical data

  9. Imputation Strategy • Historical data for some records • Records sampled the year before • Same questionnaire • Administrative data for all records • Stratification information • NAICS, province, size in terms of revenue

  10. Imputation Strategy • Type 1 and type 2 non-response • Missing key variables • Historical Trend • Ratio using current survey information • Missing details • Historical distribution • Distribution from all respondent within a homogeneous group • Distribution from a single donor

  11. Imputation Strategy • Type 3 non-response • Donor imputation • Closest neighbour based on administrative data

  12. Use of fiscal data • Use fiscal data as a proxy value for total non-response • Use fiscal data as a proxy value for simple units randomly selected at the sampling stage • Use to update the initial size in terms of revenue • Number of survey variables for which we use fiscal data as proxy range from 7 to 25

  13. Challenges • Conceptual differences • Questionnaire content review • Variables for which there is no proxy value on the fiscal data base • Modeling • Industry specific needs • Tailored strategy

  14. Challenges • Monitoring the effect • Creation of a distinct path for records where we used fiscal data (category 4 of non-response) • Creation of a diagnostic tool

  15. Diagnostic tool • Identification section • Industry, province, variable description • Weighted sums, share and percentages by category of non-response Variable Y (Total) 150M 25M 20M 55M 250M

  16. Conclusion • Centralized E and I strategy vs industry specific needs • Diagnostic tool • Modeling

  17. Thank you!Questions?

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