1. François Brisebois, Statistics Canada International Total Survey Error Workshop June 15, 2010 Improvements to Economic Survey Methodologies to Reduce Revisions in Published Estimates

2. Outline  Revisions  Context  Revisions vs. Total Survey Error  My stories about revisions 1.Revisions in tax data 2.Study about the sources of revisions 3.Quality indicator incorporating revisions  Points for discussion

3. Revisions  Release of preliminary, revised and final figures Timeliness-accuracy trade-off Users want both  Tracking of the size and direction of revisions helps assessing the trade-off Coherence of signals from one vintage to another

4. Context  Sub-annual business surveys *Monthly Survey of Manufactures Monthly Food Services Survey *Monthly Wholesale and Retail Trade Survey Quarterly Industry Revenue Indices (Services)  Used by the System of National Accounts

5. Context  Publication about 50 days after the reference period for monthly surveys, about 90 days for the quarterly survey  Typical revision scheme: Preliminary, revised 1, revised 2, annual revision  Quality indicators Sampling variability Nonresponse treatment

6. Context  All use two sources of data 1.Surveyed portion (census or survey) 2.Administrative portion  Goods and Services Tax (GST) database  Includes sales of businesses  Annually, quarterly or monthly  Calendarized to monthly data  Data for a given reference month reprocessed every month

7. Revisions vs. Total Survey Error  Sources of TSE Frame Sampling Measurement Nonresponse  Focus on the last two Measurement: Reported value revised Nonresponse: Late reporting  “Longitudinal” dimension of total survey error

8. My stories about revisions 1.Revisions in tax data GST; same reference month reprocessed every month Is the quality of imputed data improving through processing (until we actually receive tax data)?

9. My stories about revisions 2.Study about the sources of revisions Monthly Survey of Manufactures Systematic downward revisions? Examined main contributors to revisions  Survey/Admin * Reported/Imputed  Main findings:  No significant trend in revisions  « Survey - Reported » category showed highly unexpected revisions  Small downward trend in administrative data. Why?  Look for improvements (operational, methodological)

10. My stories about revisions 3.Quality indicator incorporating revisions Quarterly Industry Revenue Indices Design = Census  Complex units = Collected data  Simple units = Administrative data Quality indicator?  No sampling error  Nonresponse dealt with imputation  Non-negligible revision rates for some industries

11. My stories about revisions 3.Quality indicator incorporating revisions Quality indicator combining three criteria a)Combined reported rate of the survey and administrative data portions b)Variance due to imputation c)Revision rate Approach to be examined to expend to other designs

12. Points for discussion  Magnitude of revisions: Should revisions be monitored more closely?  Users’ #1 tool to evaluate/challenge quality How are revisions dealt with in your organisation?  Overall indicator of quality How can we incorporate revisions into our measures of quality?  Typically sampling error and nonresponse/imputation rates