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The Labour Market Impact of the EU Enlargement pp 75–97Cite as

Labour Productivity Polarization Across Western European Regions: Threshold Effects Versus Neighbourhood Effects

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Abstract

The regional distribution of labour productivity in Western Europe is characterised by a Core-Periphery spatial pattern: high (low) productivity regions are in a proximate relationship with other high (low) productivity regions. Over the period 1980–2003, intra-distribution dynamics has generated long-run multiple equilibria with the formation of two clubs of convergence. The observed dynamics can only marginally be explained by nonlinear (threshold) effects in the accumulation of physical capital. In contrast, the joint effect of spatial dependence and nonlinearities in growth behaviour play a key role in determining multiple equilibria and reinforcing polarization of labour productivity.

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Notes

  1. 1.

    The IDD approach allows for the examination of how the whole productivity distribution changes over time and it is therefore much more informative than the convergence approach developed within the regression paradigm (the so-called β-convergence approach) which only gives information on the dynamics of the average economy (Quah 2007).

  2. 2.

    Important contributions to the literature on regional convergence have recently been presented by Rey and Janikas (2005), Rey (2004a, b), Rey and Montouri (2004) and Carrington (2006).

  3. 3.

    The consideration of nonparametric (spatial auto-regressive) models instead of linear models strongly differentiates this paper from previous contributions to this kind of literature. The ‘control function’ approach (Blundell and Powell 2003), used to take into account the endogeneity of the nonparametric spatial lag term, represents another methodological novelty proposed in this paper.

  4. 4.

    See, in particular, Fotopoulos (2008), Fiaschi and Lavezzi (2007), Ezcurra et al. (2007) and Benito and Ezcurra (2005).

  5. 5.

    The univariate analysis allows for the identification of the features of regional distribution of labour productivity at different points in time (for example, in the initial and final years of a long period of time). The conditional density analysis gives information on the changes of the relative position of various regions in the cross-section distribution of labour productivity over time, so-called ‘intra-distribution mobility’.

  6. 6.

    Regional labour productivity is computed as the ratio between GVA (Gross Value Added) at constant prices 1995 and total employment for a sample of 190 NUTS-2 European regions over the period 1980-2003. Labour productivity levels are normalized with respect to the EU15 average in order to remove co-movements due to the European wide business cycle and trends in the average values.

  7. 7.

    Univariate densities have been estimated using the local likelihood density estimator (Loader 1996). A variable bandwidth, selected by generalised cross validation (GCV), has been used together with a tricube kernel function. In order to allow time comparison, we have used the same span parameter (α = 0.4) for both years. Following Fiaschi and Lavezzi (2007), we have also applied a bimodality test based on the bootstrap procedure suggested by Efron and Tibshirani (1993). The p-values of this test are equal to 0.004 for 1980 and to 0.000 for the last year, indicating the rejection of the unimodality hypothesis.

  8. 8.

    In our context, \( G_i^* \) is a measure of local clustering of labour productivity around region i. If high (low) values of x tend to be clustered around i, the standardized \( G_i^* \) will be positive (negative). In order to compute local \( G_i^* \) indices, we have used distance-based binary spatial weights matrices. Under the null hypothesis, the standardized \( G_i^* \) statistics are asymptotically normally distributed (Ord and Getis 1995). p-values have been adjusted using the Bonferroni’s criterion. Figure 2 shows standardized \( G_i^* \) variates for lag distances of 423 km (the minimum distance allowing all regions to have at least one link) and 923 km (the distance cut-off at which the global spatial autocorrelation G reaches a maximum value) for 1980 and 2003. For a cut-off distance of 923 km, the cluster of high-productivity regions is much larger, indicating that the territory becomes more homogenous.

  9. 9.

    We have used the local linear conditional density estimator developed by Hyndman and Yao (2002), with a variable bandwidth.

  10. 10.

    All the studies on IDD, which make use of nonparametric stochastic kernel density estimators, provide three-dimensional perspective plots and/or the corresponding contour plots of the conditional density to describe the law of motion of cross-sectional distributions. In this way, they treat the conditional density as a bivariate density function, while the latter must be interpreted as a sequence of univariate densities of relative productivity levels conditional on certain initial levels.

  11. 11.

    Since the conditional density plot has been evaluated on an equi-spaced grid of 100 values over the range of x and y directions, Fig. 3a displays 100 stacked univariate densities.

  12. 12.

    Cambridge Econometrics is the source of data for the all the variables in the growth regression models.

  13. 13.

    All model specifications impose a restriction on the effects of \( \ln \left[ {{i_k}} \right] \) and \( \ln \left[ {n + g + \delta } \right] \). This restriction has been formally tested using a Likelihood Ratio test (see Table 1).

  14. 14.

    \( W \) is a standardized spatial weights matrix.

  15. 15.

    Other covariates included in our cross-section analysis may be endogenous as they may be influenced by the same factors that affect output. We might also have used the control function approach to take into account these endogeneity sources. However, in these cases, treatment of endogeneity problems is more difficult due to the absence of internal instruments (as already observed by Brock and Durlauf 2000).

  16. 16.

    See Basile (2008) for a thorough interpretation of semiparametric unrestricted Spatial Durbin models.

  17. 17.

    We are aware that other traditional variables, such as human capital investments, investments in R&D and labour migration are missing from the model specification. However, proxy of these variables are not available for the period under examination. For example, regional education statistics are available from Eurostat Regio starting only from 1998.

  18. 18.

    Apart from the semiparametric approach used here as well as in some other growth analyses (such as Liu and Stengos 1999), at least four other methods have been used in the growth regression literature to search for parameter heterogeneity: the regression trees approach (Durlauf and Johnson 1995) the threshold estimator (Masanjala and Papageorgiou 2004), the varying coefficient model (Kourtellos 2001) and the Geographically Weighted Regression model (Bivand and Brunstad 2006).

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Acknowledgments

A previous version of this paper was presented at the SEA conference in Cambridge, the AIEL conference in Naples, the AISRE conference in Bolzano and at a seminar in Perugia. I wish to thank all the participants of those meetings, as well as two anonymous referees and the co-editor of this book, Francesco Pastore, for helpful comments. The usual disclaimers apply.

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Authors and Affiliations

  1. Institute for Studies and Economic Analysis (ISAE) and Centro di Economia, Regionale, dei Trasporti e del Turismo (CERTeT), Piazza dell’Indipendenza, 4, 00185, Rome, Italy

    Roberto Basile

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  1. Roberto Basile
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Correspondence to Roberto Basile .

Editor information

Editors and Affiliations

  1. Fac. Economia, Università Napoli Parthenope, Via Medina 40, Napoli, 80133, Italy

    Floro Ernesto Caroleo

  2. Dipto. Discipline Giuspubblicistiche, Seconda Università di Napoli, Palazzo Melzi, Piazza Matteotti, Santa Maria Capua Vetere, 81055, Italy

    Francesco Pastore

Appendix: List of regions

Appendix: List of regions

NUTS2 code

Region

NUTS2 code

Region

AT11

Burgenland

GR11

Anatoliki Makedonia, Thraki

AT12

Niederösterreich

GR12

Kentriki Makedonia

AT13

Wien

GR13

Dytiki Makedonia

AT21

Kärnten

GR14

Thessalia

AT22

Steiermark

GR21

Ipeiros

AT31

Oberösterreich

GR22

Ionia Nisia

AT32

Salzburg

GR23

Dytiki Ellada

AT33

Tirol

GR24

Sterea Ellada

AT34

Vorarlberg

GR25

Peloponnisos

BE10

Région de Bruxelles-Capitale

GR30

Attiki

BE21

Prov. Antwerpen

GR41

Voreio Aigaio

BE22

Prov. Limburg (B)

GR42

Notio Aigaio

BE23

Prov. Oost-Vlaanderen

GR43

Kriti

BE24

Prov. Vlaams Brabant

IE01

Border, Midlands and Western

BE25

Prov. West-Vlaanderen

IE02

Southern and Eastern

BE31

Prov. Brabant Wallon

ITC1

Piemonte

BE32

Prov. Hainaut

ITC2

Valle d’Aosta/Vallée d’Aoste

BE33

Prov. Liège

ITC3

Liguria

BE34

Prov. Luxembourg (B)

ITC4

Lombardia

BE35

Prov. Namur

ITD1

Provincia Autonoma Bolzano-Bozen

DE11

Stuttgart

ITD2

Provincia Autonoma Trento

DE12

Karlsruhe

ITD3

Veneto

DE13

Freiburg

ITD4

Friuli-Venezia Giulia

DE14

Tübingen

ITD5

Emilia-Romagna

DE21

Oberbayern

ITE1

Toscana

DE22

Niederbayern

ITE2

Umbria

DE23

Oberpfalz

ITE3

Marche

DE24

Oberfranken

ITE4

Lazio

DE25

Mittelfranken

ITF1

Abruzzo

DE26

Unterfranken

ITF2

Molise

DE27

Schwaben

ITF3

Campania

DE50

Bremen

ITF4

Puglia

DE60

Hamburg

ITF5

Basilicata

DE71

Darmstadt

ITF6

Calabria

DE72

Gießen

ITG1

Sicilia

DE73

Kassel

ITG2

Sardegna

DE91

Braunschweig

NL12

Friesland

DE92

Hannover

NL13

Drenthe

DE93

Lüneburg

NL21

Overijssel

DE94

Weser-Ems

NL22

Gelderland

DEA1

Düsseldorf

NL31

Utrecht

DEA2

Köln

NL32

Noord-Holland

DEA3

Münster

NL33

Zuid-Holland

DEA4

Detmold

NL34

Zeeland

DEA5

Arnsberg

NL41

Noord-Brabant

DEB1

Koblenz

NL42

Limburg (NL)

DEB2

Trier

PT11

Norte

DEB3

Rheinhessen-Pfalz

PT15

Algarve

DEC

Saarland

PT16

Centro (PT)

DEF0

Schleswig-Holstein

PT17

Lisboa

DK00

DENMARK

PT18

Alentejo

ES11

Galicia

SE01

Stockholm

ES12

Principado de Asturias

SE02

Östra Mellansverige

ES13

Cantabria

SE04

Sydsverige

ES21

Pais Vasco

SE06

Norra Mellansverige

ES22

Comunidad Foral de Navarra

SE07

Mellersta Norrland

ES23

La Rioja

SE08

Övre Norrland

ES24

Aragón

SE09

Småland med öarna

ES3

Comunidad de Madrid

SE0A

Västsverige

ES41

Castilla y León

UKC1

Tees Valley and Durham

ES42

Castilla-la Mancha

UKC2

Northumberland, Tyne and Wear

ES43

Extremadura

UKD1

Cumbria

ES51

Cataluña

UKD2

Cheshire

ES52

Comunidad Valenciana

UKD3

Greater Manchester

ES53

Illes Balears

UKD4

Lancashire

ES61

Andalucia

UKD5

Merseyside

ES62

Región de Murcia

UKE1

East Riding and North Lincolnshire

FI13

Itä-Suomi

UKE2

North Yorkshire

FI18

Etelä-Suomi

UKE3

South Yorkshire

FI19

Länsi-Suomi

UKE4

West Yorkshire

FI1A

Pohjois-Suomi

UKF1

Derbyshire and Nottinghamshire

FI20

Åland

UKF2

Leicestershire, Rutland and Northants

FR10

Île de France

UKF3

Lincolnshire

FR21

Champagne-Ardenne

UKG1

Herefordshire, Worcestershire and Warks

FR22

Picardie

UKG2

Shropshire and Staffordshire

FR23

Haute-Normandie

UKG3

West Midlands

FR24

Centre

UKH1

East Anglia

FR25

Basse-Normandie

UKH2

Bedfordshire, Hertfordshire

FR26

Bourgogne

UKH3

Essex

FR30

Nord – Pas-de-Calais

UKI1

Inner London

FR41

Lorraine

UKI2

Outer London

FR42

Alsace

UKJ1

Berkshire, Bucks and Oxfordshire

FR43

Franche-Comté

UKJ2

Surrey, East and West Sussex

FR51

Pays de la Loire

UKJ3

Hampshire and Isle of Wight

FR52

Bretagne

UKJ4

Kent

FR53

Poitou-Charentes

UKK1

Gloucestershire, Wiltshire and North Somerset

FR61

Aquitaine

UKK2

Dorset and Somerset

FR62

Midi-Pyrénées

UKK3

Cornwall and Isles of Scilly

FR63

Limousin

UKK4

Devon

FR71

Rhône-Alpes

UKL1

West Wales and The Valleys

FR72

Auvergne

UKL2

East Wales

FR81

Languedoc-Roussillon

UKM1

North Eastern Scotland

FR82

Provence-Alpes-Côte d’Azur

UKM2

Eastern Scotland

FR83

Corse

UKM3

South Western Scotland

  

UKM4

Highlands and Islands

  

UKN0

Northern Ireland

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Basile, R. (2010). Labour Productivity Polarization Across Western European Regions: Threshold Effects Versus Neighbourhood Effects. In: Caroleo, F., Pastore, F. (eds) The Labour Market Impact of the EU Enlargement. AIEL Series in Labour Economics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2164-2_4

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