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

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 8/9-Oct-2009 Quality of NUTS 3 estimates

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! 8/9-Oct-2009 Quality of NUTS 3 estimates

Quality of NUTS 3 figures 8/9-Oct-2009 Quality of NUTS 3 estimates

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 8/9-Oct-2009 Quality of NUTS 3 estimates

Basic assumption “The geographical distribution of registered data is a good estimator for the geographical distribution of LFS data” 8/9-Oct-2009 Quality of NUTS 3 estimates

Estimation process Under our basic assumption, the NUTS 3 estimation process is as follow: LFS N2 unemp break Reg N3 estim N3 rate active 8/9-Oct-2009 Quality of NUTS 3 estimates

Quality assessment framework There is no easy way to calculate estimation errors NUTS 1 to NUTS 2 exercise NUTS 3 simulation 8/9-Oct-2009 Quality of NUTS 3 estimates

From NUTS 1 to NUTS 2 analysis LFS N1 unemp break Reg N2 estim N2 rate active 8/9-Oct-2009 Quality of NUTS 3 estimates

Comparing distributions 8/9-Oct-2009 Quality of NUTS 3 estimates

Comparing rates 8/9-Oct-2009 Quality of NUTS 3 estimates

Comparing dispersions of unemployment rates 8/9-Oct-2009 Quality of NUTS 3 estimates

Comparing dispersions of unemployment rates 8/9-Oct-2009 Quality of NUTS 3 estimates

Comparing dispersions of unemployment rates 8/9-Oct-2009 Quality of NUTS 3 estimates

Comparing EU level dispersions of unemployment rates 8/9-Oct-2009 Quality of NUTS 3 estimates

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 8/9-Oct-2009 Quality of NUTS 3 estimates

NUTS 3 quality evaluation – Monte Carlo approach The NUTS 3 2008 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 8/9-Oct-2009 Quality of NUTS 3 estimates

Random errors Three types of random errors were analysed: Additive gaussian Multiplicative gaussian Uniform gaussian 8/9-Oct-2009 Quality of NUTS 3 estimates

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 8/9-Oct-2009 Quality of NUTS 3 estimates

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 8/9-Oct-2009 Quality of NUTS 3 estimates

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 8/9-Oct-2009 Quality of NUTS 3 estimates

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 8/9-Oct-2009 Quality of NUTS 3 estimates

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. 8/9-Oct-2009 Quality of NUTS 3 estimates

Thank you for your attention Any questions? Pedro-Jorge.Martins-Ferreira@ec.europa.eu 8/9-Oct-2009 Quality of NUTS 3 estimates