1 / 17

Goals

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.

prentice
Download Presentation

Goals

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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?

More Related