Clustering of Regional Labor Markets

Transcription

1 Seminar Tools for the implementation of active labor market policies Rome, Prof. Dr. Wolfgang Dauth

2 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,

3 Unemployment Rates, April 2016 Germany: 6,3 % But there are huge disparities across regions: Eichstätt: 1,4% + Bremerhaven: 17,1% 3

4 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 4

5 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 5

6 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 6

7 1 st 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 1 st plus all new entrants over the year) 7

8 Integration Rate 2012 Kiel Integration rates 2012 by local employment office districts Bremen Hamburg Schwerin Western Germany (w/o Berlin): 44,5% Eastern Germany (w/ Berlin) 46,8% Hannover Magdeburg Potsdam Berlin Germany 45,1% Spread 36,6 56,6% Düsseldorf Erfurt Dresden Wiesbaden Mainz National border State border District border State capital (13) (23) (52) (42) 40,0 43,0 46,0 less than to to to 40,0 43,0 46,0 49,0 Saarbrücken 100 km (17) 49,0 to 52,0 (5) 52,0 to 55,0 Stuttgart (2) (0) 55,0 58,0 to and above 58,0 München ( ) Number of districts Data: FEA Statistics Department IAB

9 Results of the Regression Analysis: Choice and Weight of Classification Variables Outcome variable: log Integration rate Exogenous variables (in logs) Effect direction Weighting 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 (%) + 3,9 11,4 Employment shr. in small plants (< 100) (%) + 2,6 7,6 Job density (%) + 2,2 6,4 Spatial spillovers (seasonal effect) 7/11-6/12 (%-points.) + 3,4 9,9 R squared: 85,5 % Regression Details 9

10 2 nd Step: Cluster Analysis Cluster Details Number of Types 10

11 The labor market types 2014 Flensburg Kiel Stralsund Neumünster Lübeck Comparison Types under Social Code III 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. Heide Rostock Greifswald Elmshorn Bad Oldesloe Neubrandenburg Schwerin Hamburg Emden/Leer Stade Oldenburg/ Wilhelmshaven Bremen/ Eberswalde Bremerhaven Lüneburg/Uelzen 1 Bochum Neuruppin 2 Dortmund 3 Duisburg Vechta Celle Nienburg/ 4 Düsseldorf Stendal Verden 5 Essen Nordhorn Berlin 6 Gelsenkirchen Frankfurt (Oder) Helmstedt 7 Mettmann Hannover 8 Oberhausen Herford 9 Solingen/Wuppertal Osnabrück Magdeburg Potsdam Rheine Braunschweig/ Hildesheim Goslar Coesfeld Bielefeld Ahlen/ Detmold Hameln Dessau-Roßlau/ Cottbus Münster Bernburg Halberstadt Wittenberg Reckling- Wesel hausen Hamm Paderborn Göttingen Sangerhausen Oschatz 8 5 Hagen Meschede/Soest Nordhausen Halle Krefeld Leipzig Bautzen 4 Riesa 7 9 Iserlohn Mönchen- Kassel Weißenfels Dresden glbch. Gotha Bergisch Glbch. Korbach Freiberg Pirna Köln Siegen Erfurt Chemnitz Aachen/ Marburg Jena Düren Bonn Zwickau Altenburg/ Annaberg- Gera Buchholz Brühl Neuwied Limburg/ Suhl Bad Hers- Wetzlar Gießen feld/fulda Plauen Koblenz/Mayen Bad Hom- National border Montabaur burg Hanau Bamberg/ Frankfurt Schweinfurt State border Wiesbaden Coburg Bayreuth/Hof Trier Offenbach District border Aschaf- Bad Kreuznach Mainz fenburg Würzburg Darmstadt Weiden Type I (5): Type IIa (6): Type IIb (11): Type IIc (8): Type IIIa (25): Type IIIb (14): Type IVa (21): Type IVb (22): Type IVc (7): Predominantly metropolitan districts with favourable labour market conditions Metropolitan districts with above average unemployment rates Metropolitan districts with very high unemployment rates Urbanised districts with slightly above average unemployment rates Districts with conurbational features with below average unemployment rates Predominantly rural districts with average unemployment rates Districts with conurbational features with a large manufacturing sector and favourable labour market conditions Predominantly rural districts with favourable labour market conditions and strong seasonal dynamics Rural districts with very strong seasonal dynamics and low unemployment rates Saarland Kaiserslautern/ Pirmasens Freiburg Lörrach Ludwigshf. Landau Offenburg Mannheim Heidelberg Karlsruhe/ Heilbronn Rastatt Nagold/ Pforzheim Rottweil/ Villingen- Ludwigs- Stuttgart burg Reutlingen Balingen Schwenningen Schwäbisch Hall/ Tauberbischofsheim Waiblingen Göppingen Ulm Konstanz/ Ravensburg Fürth Nürnberg Schwandorf Ansbach/Weißenburg Regensburg Deggendorf Aalen Donau- Ingolstadt wörth Landshut/Pfarrkirchen Augsburg Freising München Kempten/ Weilheim Traunstein Rosenheim Memmingen Passau 100 km Type Va (7): Type Vb (11): Type Vc (17): ( ) Number of districts in each type Predominantly metropolitan districts with high unemployment rates Predominantly rural districts with high unemployment rates Rural districts with very severe labour market conditions Source: Statistics department of the Federal Employment Agency and Regional Labor Market Research IAB

12 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%) 12

13 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 13

14 Practical Application Spreads of the average unemployment duration by comparison types (VT), March

15 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 15

16 Thanks! Prof. Dr. Wolfgang Dauth

17 BACKUP

18 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 18

19 Clustering vs. Benchmarking (II) Sociological conditions Economic conditions Back 19

20 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 20

21 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!) 21

22 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 22

23 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 23

24 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 24

25 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 25

26 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 26