1. Quality of NUT level 3 figures
Pedro Ferreira
2/3-Oct-2008

2. Structure of my talk Briefing on the last data collection
Quality assessment framework
NUTS 1 to NUTS 2 exercise
NUTS 3 quality evaluation
Conclusions
Proposal
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Quality of NUTS 3 estimates

3. 2008 data collection
All NUTS level 2 regions were collected. For the first time, there are no missing data at regional level for the main indicators
At NUTS level 3, there are some missing data:
All regions of Belgium
All regions of Ireland
All regions of Portugal
Illes Balears, Spain
All regions of Northern Ireland, United Kingdom
Please send the missing data!
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Quality of NUTS 3 estimates

4. Quality of NUTS 3 figures
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Quality of NUTS 3 estimates

5. Chosen countries
Only 10 out of 27 Member-States base their NUTS 3 figures on registered data
Registered data for Belgium not available for recent years
Ireland and Slovenia comprise just two NUTS 2 regions
Seven countries were chosen for this quality report:
Denmark
Austria
Germany
Portugal
France
Sweden
Netherlands
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Quality of NUTS 3 estimates

6. Basic assumption
“The geographical distribution of registered data is a good estimator for the geographical distribution of LFS data”
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Quality of NUTS 3 estimates

7. Estimation process
Under our basic assumption, the NUTS 3 estimation process is as follow:
LFS N2
unemp
break
Reg N3
estim N3
rate
active
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Quality of NUTS 3 estimates

8. Quality assessment framework
There is no easy way to calculate estimation errors
NUTS 1 to NUTS 2 exercise
NUTS 3 simulation
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Quality of NUTS 3 estimates

9. From NUTS 1 to NUTS 2 analysis
LFS N1
unemp
break
Reg N2
estim N2
rate
active
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Quality of NUTS 3 estimates

10. Comparing distributions
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11. Comparing rates
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Quality of NUTS 3 estimates

12. Comparing dispersions of unemployment rates
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Quality of NUTS 3 estimates

13. Comparing dispersions of unemployment rates
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Quality of NUTS 3 estimates

14. Comparing dispersions of unemployment rates
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Quality of NUTS 3 estimates

15. Comparing EU level dispersions of unemployment rates
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Quality of NUTS 3 estimates

16. From NUTS 1 to NUTS 2 conclusions
Negligible impacts on EU estimates
Negligible impacts for Germany
Small impacts for France, Netherlands and Sweden
Big impacts for Austria, Denmark (not shown by a graph because only two years were available) and Portugal
But most important, we have now an idea of the (maximum) differences magnitude per Member State
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Quality of NUTS 3 estimates

17. NUTS 3 quality evaluation – Monte Carlo approach
The NUTS estimates are used as a reference
In a Monte Carlo simulation, random errors are integrated with the NUTS 3 estimates, and for each replica, the dispersion of unemployment rates is computed
The variability of random errors is chosen in a way that the MSE between registered and LFS samples has the same magnitude as the one found in the NUTS 1 to NUTS 2 exercise
The overall distribution of dispersions will give us an idea of how robust is the dispersion of unemployment rates at NUTS level 3
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Quality of NUTS 3 estimates

18. Random errors Three types of random errors were analysed:
Additive gaussian
Multiplicative gaussian
Uniform gaussian
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Quality of NUTS 3 estimates

19. Dispersion of unemployment rates
Monte Carlo results
Member-State
Dispersion of unemployment rates
Confidence set
Error (pp)
CV (%)
Denmark
26.7
[ 25.3 ; 28.7 ]
±1.9
1.2
Germany
60.3
[ 60.2 ; 60.4 ]
± 0.1
0.1
France
41.5
[ 40.9 ; 42.0 ]
± 0.6
0.3
Netherlands
28.4
[ 27.9 ; 28.9 ]
Austria
43.1
[ 40.4 ; 45.7 ]
± 2.7
1.6
Portugal
30.9
[ 29.8 ; 32.0 ]
± 1.1
0.7
Sweden
17.7
[ 16.7 ; 18.8 ]
0.6
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Quality of NUTS 3 estimates

20. Monte Carlo main conclusions
Even for Member-States that show relative big differences between the geographical distributions of registered and LFS samples at NUTS level 2 have better and acceptable results at NUTS level 3
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Quality of NUTS 3 estimates

21. Further analysis
Some methods were used to compute the standard error for the dispersion of unemployment rates, e.g., bootstrap approaches
New method to compute standard errors and bias was proposed, which we called “nested jackknife”, suited whenever there is a hierarchical classification like the NUTS classification
Our unit and the Eurostat methodological unit are working together to go deeper in this subject. A methodological contractor is already give some input on this
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Quality of NUTS 3 estimates

22. Future data collection
Germany, France, Netherlands, Sweden and EU level estimates can continue like before but…
… there is the need to improve the estimates quality for, at least, Denmark, Austria and Portugal.
Explore if different aspects of registered data could give better results, e.g. analyse the use of “net flows” to employment centres instead of registered data
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Quality of NUTS 3 estimates

23. The optimal solution… Aggregate LFS micro data at NUTS level 3
The geographical distribution of NUTS 3 LFS is necessarily better than registered data
This solution falls perfectly in the TASK project philosophy: having confidential (and maybe unreliable) NUTS 3 data which will only be used as building blocks to compute meaningful aggregates, e.g., rural regions
In the dispersion indicator, the meaningful aggregate would be “all NUTS 3 regions” within each country. The reliability of this aggregate will surely be high enough and better than registered estimates.
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Quality of NUTS 3 estimates

24. Thank you for your attention
Any questions?
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Quality of NUTS 3 estimates