Download Clustering of Regional Labor Markets

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
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Unemployment Rates, April 2016
Germany: 6,3 %
But there are huge disparities
across regions:
– Eichstätt: 1,4%
+ Bremerhaven: 17,1%
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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
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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
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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
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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)
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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
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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
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2nd Step: Cluster Analysis
Cluster Details
Number of Types
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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
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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%)
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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
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Practical Application
Spreads of the average
unemployment duration
by comparison types
(VT) , March 2016
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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
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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
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Sociological conditions
Clustering vs. Benchmarking (II)
Economic conditions
Clustering of Regional Labor Markets
Back
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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
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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!)
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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
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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
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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
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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
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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
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