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Clustering of Regional Labor Markets Seminar Tools for the implementation of active labor market policies Rome, 25.05.2016 Prof. Dr. Wolfgang Dauth Introduction ! Regional conditions determine the achievement of goals of local employment agencies ! Need to account for different conditions when comparing performance of local employment offices ! Main tool of the Federal Emp. Agency since 2004: Classification of Regional Labor Markets ! Updates in 2006, 2009, 2013, 2014 Clustering of Regional Labor Markets 2 Unemployment Rates, April 2016 Germany: 6,3 % But there are huge disparities across regions: – Eichstätt: 1,4% + Bremerhaven: 17,1% Clustering of Regional Labor Markets 3 Starting Point ! In the late 1990ies, Germany was “The sick man of Europe” (Economist 1999) ! The “Hartz”-Reforms aimed to change labor market institutions and raise labor supply & demand ! Hartz III: transform FEA to a modern, efficient service provider ! Provide more room to maneuver for local employment offices, but under a modern management and control system Clustering of Regional Labor Markets 4 The Challenge ! Need to judge the achievement of objectives of a local employment office by comparison with other offices ! Performance largely determined by (often exogenous) local conditions ! A comparison of regional employment offices needs to account for their initial “handicap” à Classify employment offices with similar initial conditions into comparison types Clustering vs. Benchmarking Clustering of Regional Labor Markets 5 Methodological Innovation: A Two Step Procedure ! “Model based classification” rather than arbitrary selection of cluster variables ! Select “candidate” variables from economic theory ! Use regression analysis to identify important variables ‐ Provides info on relative importance (use as weights!) ‐ Accounts for interdependencies between variables ! Use in the second step for the actual classification Clustering of Regional Labor Markets 6 1st Step: Regression Analysis Outcome Variable: “Integration rate” (IR) (Integrationsgrad gesamt) = ∑ Integrations over one year (including re-employment, no job to job-integrations) Potential of clients (opening stock on January 1st plus all new entrants over the year) Clustering of Regional Labor Markets 7 Integration Rate 2012 Integration rates 2012 by local employment office districts Kiel Schwerin Hamburg Bremen Western Germany (w/o Berlin): 44,5% Eastern Germany (w/ Berlin) 46,8% Germany 45,1% Berlin Hannover Spread Potsdam Magdeburg 36,6 – 56,6% Düsseldorf Erfurt Dresden (13) National border State border Wiesbaden District border Mainz State capital 100 km Saarbrücken Stuttgart less than 40,0 (23) 40,0 to 43,0 (52) 43,0 to 46,0 (42) 46,0 to 49,0 (17) 49,0 to 52,0 (5) 52,0 to 55,0 (2) 55,0 to 58,0 (0) 58,0 and above ( ) Number of districts München Data: FEA Statistics Department © IAB 2013 Clustering of Regional Labor Markets 8 Results of the Regression Analysis: Choice and Weight of Classification Variables Outcome variable: log Integration rate Exogenous variables (in logs) Weighting Effect direction abs. (=|t - ratio|) rel. (%) Unemployment rate, average for 2012 (%) - 8,6 25,1 Seasonal effect 7/11-6/12 (%-points ) + 7,7 22,4 Labor force share of unqualified 2012 (%) - 5,9 17,2 Employment share in the service sector 30.6.2012 (%) + 3,9 11,4 Employment shr. in small plants (< 100) 30.6.2012 (%) + 2,6 7,6 Job density 30.6.2012 (%) + 2,2 6,4 Spatial spillovers (seasonal effect) 7/11-6/12 (%-points.) + 3,4 9,9 R squared: 85,5 % Clustering of Regional Labor Markets Regression Details 9 2nd Step: Cluster Analysis Cluster Details Number of Types Clustering of Regional Labor Markets 10 Comparison Types under Social Code III The labor market types 2014 Flensburg Kiel Neumünster Classification of employment office districts according to unemployment rate, seasonal span, labor force share of formally unqualified, employment share in the tertiary sector, employment share in small plants (<100 employees), job density, and weighted average of seasonal span in surrounding regions. Stralsund Lübeck Greifswald Rostock Heide Elmshorn Bad Oldesloe Emden/Leer Oldenburg/ Wilhelmshaven 1 Bochum 2 Dortmund 3 Duisburg 4 Düsseldorf 5 Essen 6 Gelsenkirchen 7 Mettmann 8 Oberhausen 9 Solingen/Wuppertal Bremen/ Bremerhaven Nienburg/ Verden Berlin Frankfurt (Oder) Helmstedt Herford 6 6 3 8 1 5 4 7 Mönchenglbch. 9 Detmold Hameln Paderborn Meschede/Soest Halle Weißenfels Gotha Brühl Leipzig Dresden Korbach Freiberg Siegen Erfurt Limburg/ Wetzlar Suhl Bad Hersfeld/Fulda Gießen Altenburg/ Gera Zwickau Bad Kreuznach Hanau Frankfurt Wiesbaden Bamberg/ Coburg Schweinfurt Offenbach Trier Aschaffenburg Mainz Kaiserslautern/ Pirmasens Mannheim Ludwigshf. Landau Bayreuth/Hof Weiden Rottweil/ Balingen VillingenSchwenningen Type IVa (21): Districts with conurbational features with a large manufacturing sector and favourable labour market conditions Type IVb (22): Predominantly rural districts with favourable labour market conditions and strong seasonal dynamics Type IVc (7): Rural districts with very strong seasonal dynamics and low unemployment rates Type Va (7): Predominantly metropolitan districts with high unemployment rates Type Vb (11): Predominantly rural districts with high unemployment rates Type Vc (17): Rural districts with very severe labour market conditions Ansbach/Weißenburg Regensburg Deggendorf Waiblingen Göppingen Aalen Donauwörth Ingolstadt Passau Landshut/Pfarrkirchen Reutlingen Offenburg Lörrach 100 km Nürnberg Schwandorf Karlsruhe/Heilbronn Rastatt Ludwigsburg Freiburg Fürth Schwäbisch Hall/ Tauberbischofsheim Stuttgart Predominantly rural districts with average unemployment rates District border Würzburg Heidelberg Nagold/ Pforzheim Type IIIb (14): State border Darmstadt Saarland Districts with conurbational features with below average unemployment rates Plauen National border Bad Homburg Type IIIa (25): Pirna AnnabergBuchholz Koblenz/Mayen Montabaur Urbanised districts with slightly above average unemployment rates Chemnitz Jena Marburg Neuwied Type IIc (8): Bautzen Riesa Kassel Bonn Metropolitan districts with very high unemployment rates Oschatz Nordhausen Iserlohn Köln Type IIb (11): Cottbus Göttingen Sangerhausen Bergisch Glbch. Aachen/ Düren Dessau-Roßlau/ Wittenberg Bernburg Halberstadt Hamm Metropolitan districts with above average unemployment rates Potsdam Braunschweig/ Goslar Hildesheim 2 Hagen Krefeld Magdeburg Osnabrück Ahlen/ Münster Recklinghausen Type IIa (6): Stendal Hannover Bielefeld Predominantly metropolitan districts with favourable labour market conditions Celle Nordhorn Rheine Type I (5): Eberswalde Lüneburg/Uelzen Neuruppin Vechta Coesfeld Wesel Neubrandenburg Schwerin Hamburg Stade Augsburg Freising Ulm München ( ) Number of districts in each type Konstanz/ Ravensburg Kempten/ Memmingen Weilheim Traunstein Rosenheim Source: Statistics department of the Federal Employment Agency © IAB 2013 Clustering of Regional Labor Markets and Regional Labor Market Research 11 Results The classification result yields an intuitive pattern: ! Comparison types differ in the density of population and unemployment rates ! Partition between East and West Germany (albeit not by construction!), with the only exception of Berlin ! Explanatory power (R²) of partition is almost as high (78,3%) as the one from regression (85,5%) Clustering of Regional Labor Markets 12 Practical Application ! Integrated in IT systems of the BA (labor market monitor and data warehouse) ! Strategic meetings of office representatives from the same type (IAB researchers provide input reports) ! Classification forms the base of the “Performance Dialogue” (Leistungsdialog) for the chief officers of local employment offices. ! Update classification after 3-4 years Clustering of Regional Labor Markets 13 Practical Application Spreads of the average unemployment duration by comparison types (VT) , March 2016 Clustering of Regional Labor Markets 14 Conclusion ! Tool to account for regional conditions when comparing the performance of local employment offices ! Central importance of the outcome variable: Different outcomes (i.e., performance measures) may require different classifications. However, many regional variables are highly correlated ! Since 2006 also classifications of SGB II regions and since 2010 classification of apprenticeship markets Clustering of Regional Labor Markets 15 Thanks! Prof. Dr. Wolfgang Dauth [email protected] www.iab.de BACKUP www.iab.de Clustering vs. Benchmarking (I) ! An intuitive benchmarking procedure: Calculate the expected value of a performance measure ! Regions with similar expected values could be arranged into types ! Infer on each office’s performance by looking at deviations from the benchmark value But: ! Inference is biased by omitted variables ! Reduction of information to one single dimension Clustering of Regional Labor Markets 18 Sociological conditions Clustering vs. Benchmarking (II) Economic conditions Clustering of Regional Labor Markets Back 19 Regression model Regress log integration rate on log values of: ! Unemployment rate (average of months in 2012) ! Seasonal span = Difference between min. and max. ratio of monthly number of unemployed in relation its 12-month moving average ! Labor force share of formally unqualified ! Employment share in the service sector ! Employment share in small plants (<100 workers) ! Job density = Jobs in relation to working age population ! Spatial spillovers = Average seasonal span in all other regions, weighted by commuting shares Clustering of Regional Labor Markets 20 Classification variables Unemployment rate: ! represents the general state of the labor market ! negative relationship with integration rate Seasonal span: ! Some regions have strong seasonal variations due to the importance of tourism or construction industries ! Strong seasonality leads to more integrations (but not due to better performance of the employment office!) Clustering of Regional Labor Markets 21 Classification variables Labor force share of formally unqualified: ! Indicates special constraints for placement of some groups of job seekers ! Negatively related to the integration rate Employment share in the service sector: ! Represents the industrial structure of the regional economy ! Positively related to the integration rate Clustering of Regional Labor Markets 22 Classification variables Employment share in small plants: ! Small firms have higher rates of fluctuation, which are often related to higher hiring numbers, ceteris paribus ! Positively related to the integration rate Job density: ! Represents a region’s endowment with jobs. High values indicate net-commuting inflows ! Positively related to the integration rate Clustering of Regional Labor Markets 23 Classification variables Spatial spillovers: ! Accounts for the fact that functional labor markets often stretch out beyond administrative borders ! Conditions in surrounding regions can spill over borders ! Positively related to the outcome variable Back Clustering of Regional Labor Markets 24 Cluster Analysis - Details § Combination of two techniques of clustering: ‐ Hierarchical clustering according to Ward’s method and ‐ Subsequent optimization of the partition by using k-means clustering § The resulting types possess the following features: ‐ Regional labor markets of the same type are as similar as possible, while the types are as dissimilar as possible ‐ Each region is closer to the centroid of its own type than to any other centroid Back Clustering of Regional Labor Markets 25 Number of Types More types + individual features of regional labor markets can be better accounted for + increased inner homogeneity (regions of one type are more similar to each other) but – reduced external heterogeneity (types are less distinct) Back Clustering of Regional Labor Markets 26