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

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Presentation on theme: "Service Trade by Enterprise Characteristics (S-TEC) Søren Burman Nordic meeting 2014,Tórshavn."— Presentation transcript:

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

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

3  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 What is S-TEC – Short introduction 3

4  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 What is S-TEC – Short introduction 4

5 Example of S-TEC tables 5

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

9  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 Bias by design – Implications of a survey 9

10  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 Bias by design – Implications of a survey 10

11  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 Bias by design – Implications of a survey 11

12  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 The brickwall of confidentiality 12

13  Overview of the confidentiality issues for the template S-TEC tables The brickwall of confidentiality 13

14  One has to be carefull when making conclusions on biased data  A lot of resources are used on applying secondary confidentiality that could be used elsewhere.  Is a table hidden behind the brickwall of any use to the users?  Will the missing data discourage users, even though new information is available? Quality considerations 14

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


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