Service Trade by Enterprise Characteristics (S-TEC )

1. Service Trade by Enterprise Characteristics (S-TEC) Søren Burman Nordic meeting 2014,Tórshavn

2. Outline • What is S-TEC – Short introduction • Bias by design – Implications of a survey • The brickwall of confidentiality • Quality considerations • What is next

3. What is S-TEC – Short introduction • From ”what is traded” to ”who is trading” • Traditional trade statistics depicts the trade of a nation • S-TEC sheds light on the enterprises behind the trade • Based on the TEC statistic • Link trade statistics with business statistics at the unit level • Close cooperation with business statistics • S-TEC taskforce produced the first S-TEC template tables in 2013

4. What is S-TEC – Short introduction • Additional dimensions in the S-TEC template tables • Activity (SBR) • Size (SBR) • Ownership (FATS) • … some information on trade intensity (SBR) • Linking between SBR, FATS and ITSS is relatively simple

5. Example of S-TEC tables

6. Example of S-TEC tables

7. Example of S-TEC tables

8. Bias by design – Implications of a survey • Short presentation of the sources for the ITSS 8

9. Bias by design – Implications of a survey • Trade reported by ~1500 enterprises covers roughly 80 pct. of total service trade • Directly reported trade ~85 pct. (~68 pct. of the total ITSS) • ~15 pct. is enumerated to the rest of the population, i.e. the ~38500 enterprises (40000 – 1500) • S-TEC can either be compiled only with the enterprises that report directly • …or by the entire population requiring an estimator in order to distribute the enumerated trade

10. Bias by design – Implications of a survey • If directly reported trade is used, the “lightly” represented cells will be underestimated  Bias towards well represented cells • If the enumerated data is distributed to the rest of the population by using a proxy variable (i.e. employees), variation will inherit that of the proxy variable  Bias towards larger enterprises • This bias will be smaller if a correlated proxy variable is available

11. Bias by design – Implications of a survey • What you cannot do when distributing with a non-correlated variable • Count the number of enterprises engaged in ITS • Classify ITS by the non-correlated variable (e.g. by size if number of employees are the non-correlated variable used for the distribution of trade) • Classify ITS by other variables that are highly correlated with the variable used for the distribution of trade • What you can do • Classify trade on a more aggregated level that is not highly correlated with the variable used for the distribution of trade

12. The brickwall of confidentiality • Identifying ”risky” cells is relatively easy • Ensuring that they are sufficiently concealed is the challenge • Number of ways to suppress a single cell is V-1 * H-1 • The difficulty of applying optimal secondary confidentiality increases with level of detail • Automated process is often not up to the task

13. The brickwall of confidentiality • Overview of the confidentialityissues for the template S-TEC tables

14. Quality considerations • One has to becarefullwhenmakingconclusions on biased data • A lot of resourcesareused on applyingsecondaryconfidentialitythatcouldbeusedelsewhere. • Is a tablehiddenbehind the brickwall of anyuse to the users? • Will the missing data discourageusers, eventhough new information is available?

15. What is next • Eurostat is working on establishing a new S-TEC taskforce in 2015 with the following topics: • Methodology • Confidentiality • Framework for regular data collection