1. 1 German Regional Labour Markets: Research Notes and Questions Roberto Patuelli Department of Spatial Economics Free University Amsterdam

2. 2 The Data Available for Germany Level of disaggregation: 439 NUTS-3 districts (kreise) 326 in former West Germany 113 in former East Germany Annual fulltime employment: 1987-2004 (West) and 1993- 2004 (East). Nine economic sectors Annual unemployment rate: 1996-2002 Regional daily wages (same years as empl.) Classification of districts on the basis of economic activity Journey-to-work flows (1995, 1999, 2002, 2004): number of employees that are reported to work in a different area from the one in which they live

3. 3 Experiments Carried out Concerning German Labour Market: Three Main Topics Neural network forecasts of regional employment Nonlinear, no-hypothesis modelling Forecasting models for t + 1 and t + 2 at NUTS-3 level Integration of shift-share analysis and neural networks (also spatial shift-share and shift-share regression) Spatial filtering analysis for regional unemployment Construction of spatial autocorrelation filters (linear combinations of eigenvectors), according to different spatial connectivity matrices Finding filters common to all years of data (1996-2002) Use of filters as explanatory variables (random spatial effects?) Analysis of commuting flows, in the framework of complex network theory Comparative statics analysis of network connectivity over time Interpretation of exponential and power-law spatial interaction models in terms of logical network connectivity

4. 4 Need for further experiments Next steps in the projects are: Dynamic space-time modeling of unemployment Use of spatial filters common over the years as explanatory variables Introducing covariates - construction of spatial filters in the presence of covariates Joint neural network – spatial filtering approach Spatial analysis of employment growth rates Use of spatial filters as explanatory variables in neural networks Integration of network and spatial approaches for commuting Elaboration of framework for integration of connectivity and infrastructure Adaptation of spatial interaction modelling to space and networks

5. 5 Research Questions Neural networks need a benchmark conventional panel model for comparison Naïve random walks are used now, but a more sophisticated model is desirable, using the same information as the NNs. Which type of models to go for? Neural networks do not explicitly consider time Using a single time variable (internally rescaled by the software) rather than time dummies proved successful. How to interpret this result? Which approach could be ‘compared’ to time fixed effects? How to integrate spatial interaction modelling and GIS? And, most of all, how to integrate layers of (road) infrastructure and commuting network connections? (weighting the network by the flows while considering efficiency/criticality/shortest routes)

6. 6 Regional employment in Germany (2002-04): growth rate (left) and regional share change (right)

7. 7 Regional unemployment in Germany

8. 8 Mapping the Eigenvectors (S-coding rook matrix)

9. 9 Spatial Filter: C-coding rook matrix

10. 10 Results of the Different Approaches