Abstract
The Structural Funds (SF), are the most important strategic tool of the European Union (EU) for the promotion of regional development. This chapter analyzes the effects of regionally targeted SF on labor productivity growth in 180 Nomenclature of Territorial Units for Statistics, level 2 (NUTS2) regions from 1989 to 2006. The main contributions of this chapter to the debate on the effectiveness of Cohesion Policy consist of two aspects: the spatial econometric technique adopted and the analysis of the effectiveness of SF conditional to regional institutional quality. Regarding the first contribution, the chapter shows that EU regions, after controlling for spatial dependence, not only have multiple steady states, but also heterogeneous convergence rates. Regarding the second contribution, it is demonstrated that, while regional institutional quality is not significant per se, it positively affects the effectiveness of Objective 1 SF. Under a policy perspective, regions, to achieve a higher effectiveness of SF on productivity growth, should invest primarily in strengthening their institutional capacity.
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Notes
- 1.
A complete literature review can be found in Mohl and Hagen (2010).
- 2.
Rodríguez-Pose and Garcilazo (2015) combine regional institutional quality indicators with Cohesion Policy expenditures to assess their effects on GDP per capita growth. Studies of Beugelsdijk and Eijffinger (2005), Ederveen et al. (2006) and Bähr (2008) include institutional quality indicators and data at the national level, while Arbia et al. (2010) use measures of national institutional quality with regional data.
- 3.
In 1989–2006 period, the average correlation between Gross Value Added per employee and investment over Gross Value Added is −0.43. Correlation between Gross Value Added per employee and employment rate is, on average, 0.33.
- 4.
Regions are specified in the appendix.
- 5.
- 6.
According to ISCED, tertiary programmes include academic orientation which are largely theoretical and tertiary programmes with an occupational orientation. Examples are Bachelor’s degrees in many English-speaking countries, the “Diplom” in many German-speaking countries and the Licence in many French-speaking countries.
- 7.
- 8.
The matrix that produced better results is a four-nearest neighbors row standardized matrix.
- 9.
To compute the MC statistic and spatial filters, a set of runtimes were developed in the R environment.
- 10.
A lower bound would cause the inclusion of eigenvectors that account for a spatial random process.
- 11.
This is evident in Italy, where the assessment of Institutions of southern regions is one of the worst in Europe, while the assessment of Institutions of the regions of northern Italy is immediately below German regions, which have very positive assessments.
- 12.
For Model 1 (Base model), the Wald test is 9.949, excluding H0 at 5% level, while for Model 2 (Base model + SF), the Wald test is 13.598 and leading to do not accept H0 at 1% level.
- 13.
In the appendix, speed of convergence of Model 1 (Base model) and Model 2 (Base model + SF) are reported.
- 14.
A case study approach would allow to understand the link between regions and the cluster to which they belong to.
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Appendix: List of EU Regions Included in the Analysis and Associated Coefficient of ln(GVA/EMP_89)
Appendix: List of EU Regions Included in the Analysis and Associated Coefficient of ln(GVA/EMP_89)
Country | NUTS code | Name | Coefficient of ln(GVA/EMP_89) of Model 2 (Base model + SF) | Coefficient of ln(GVA/EMP_89) of Model 1 (Base model F) |
|---|---|---|---|---|
BE | BE10 | Région de Bruxelles-Capitale | −0.00865 | 0.01526 |
BE | BE21 | Prov. Antwerpen | −0.01288 | 0.01388 |
BE | BE22 | Prov. Limburg | −0.01683 | −0.00931 |
BE | BE23 | Prov. Oost-Vlaanderen | −0.02198 | −0.00746 |
BE | BE24 | Prov. Vlaams-Brabant | −0.00930 | 0.01212 |
BE | BE25 | Prov. West-Vlaanderen | −0.02512 | −0.01372 |
BE | BE31 | Prov. Brabant Wallon | −0.00930 | 0.01212 |
BE | BE32 | Prov. Hainaut | −0.01673 | 0.00028 |
BE | BE33 | Prov. Liège | −0.00987 | −0.01503 |
BE | BE34 | Prov. Luxembourg | −0.00513 | −0.01270 |
BE | BE35 | Prov. Namur | −0.00941 | 0.00095 |
DE | DE11 | Stuttgart | −0.06502 | −0.03573 |
DE | DE12 | Karlsruhe | −0.04963 | −0.03184 |
DE | DE13 | Freiburg | −0.04009 | −0.02895 |
DE | DE14 | Tübingen | −0.05349 | −0.03184 |
DE | DE21 | Oberbayern | −0.05858 | −0.03500 |
DE | DE22 | Niederbayern | −0.05018 | −0.03316 |
DE | DE23 | Oberpfalz | −0.05695 | −0.03535 |
DE | DE24 | Oberfranken | −0.05868 | −0.03609 |
DE | DE25 | Mittelfranken | −0.06824 | −0.03724 |
DE | DE26 | Unterfranken | −0.06392 | −0.03744 |
DE | DE27 | Schwaben | −0.06190 | −0.03468 |
DE | DE30 | Berlin | −0.03184 | −0.04930 |
DE | DE41 | Brandenburg | −0.03147 | −0.04439 |
DE | DE42 | Brandenburg | −0.03285 | −0.05236 |
DE | DE50 | Bremen | −0.02660 | −0.03265 |
DE | DE60 | Hamburg | −0.02459 | −0.03116 |
DE | DE71 | Darmstadt | −0.04841 | −0.03255 |
DE | DE72 | Gießen | −0.04001 | −0.02999 |
DE | DE73 | Kassel | −0.04515 | −0.03302 |
DE | DE80 | Mecklenburg-Vorpommern | −0.03174 | −0.03855 |
DE | DE91 | Braunschweig | −0.03423 | −0.03446 |
DE | DE92 | Hannover | −0.02600 | −0.03413 |
DE | DE93 | Lüneburg | −0.02348 | −0.03349 |
DE | DE94 | Weser-Ems | −0.03879 | −0.02609 |
DE | DEA1 | Düsseldorf | −0.02831 | −0.02092 |
DE | DEA2 | Köln | −0.01794 | −0.02204 |
DE | DEA3 | Münster | −0.03586 | −0.02425 |
DE | DEA4 | Detmold | −0.03306 | −0.03174 |
DE | DEA5 | Arnsberg | −0.03305 | −0.02725 |
DE | DEB1 | Koblenz | −0.01651 | −0.02428 |
DE | DEB2 | Trier | −0.00507 | −0.02199 |
DE | DEB3 | Rheinhessen-Pfalz | −0.02970 | −0.02814 |
DE | DEC0 | Saarland | −0.00589 | −0.02300 |
DE | DED1 | Chemnitz | −0.03807 | −0.04172 |
DE | DED2 | Dresden | −0.03316 | −0.04500 |
DE | DED3 | Leipzig | −0.03388 | −0.05044 |
DE | DEE0 | Sachsen-Anhalt | −0.03564 | −0.04078 |
DE | DEF0 | Schleswig-Holstein | −0.02459 | −0.03116 |
DE | DEG0 | Thüringen | −0.04473 | −0.03620 |
DK | DK0 | Danmark | −0.02742 | −0.02887 |
EL | EL11 | Anatoliki Makedonia, Thraki | −0.03449 | −0.03559 |
EL | EL12 | Kentriki Makedonia | −0.03832 | −0.03722 |
EL | EL13 | Dytiki Makedonia | −0.02832 | −0.02180 |
EL | EL14 | Thessalia | −0.02674 | −0.02669 |
EL | EL21 | Ipeiros | −0.02826 | −0.02708 |
EL | EL22 | Ionia Nisia | −0.02807 | −0.03972 |
EL | EL23 | Dytiki Ellada | −0.02710 | −0.04447 |
EL | EL24 | Sterea Ellada | −0.02582 | −0.04036 |
EL | EL25 | Peloponnisos | −0.02699 | −0.04804 |
EL | EL30 | Attiki | −0.02726 | −0.04189 |
EL | EL41 | Voreio Aigaio | −0.02987 | −0.02859 |
EL | EL42 | Notio Aigaio | −0.02978 | −0.03640 |
EL | EL43 | Kriti | −0.02967 | −0.03824 |
ES | ES11 | Galicia | −0.02972 | −0.00708 |
ES | ES12 | Principado de Asturias | −0.02978 | −0.00795 |
ES | ES13 | Cantabria | −0.04389 | −0.03369 |
ES | ES21 | País Vasco | −0.04283 | −0.02951 |
ES | ES22 | Comunidad Foral de Navarra | −0.04170 | −0.02842 |
ES | ES23 | La Rioja | −0.04599 | −0.03179 |
ES | ES24 | Aragón | −0.04304 | −0.03458 |
ES | ES30 | Comunidad de Madrid | −0.04089 | −0.03087 |
ES | ES41 | Castilla y León | −0.04129 | −0.03457 |
ES | ES42 | Castilla-la Mancha | −0.03872 | −0.02989 |
ES | ES43 | Extremadura | −0.03056 | −0.03322 |
ES | ES51 | Cataluña | −0.03587 | −0.03776 |
ES | ES52 | Comunidad Valenciana | −0.03908 | −0.03316 |
ES | ES53 | Illes Balears | −0.03496 | −0.03412 |
ES | ES61 | Andalucía | −0.03587 | −0.02921 |
ES | ES62 | Región de Murcia | −0.03775 | −0.02949 |
FR | FR10 | Île de France | −0.02784 | −0.01839 |
FR | FR21 | Champagne-Ardenne | −0.01813 | −0.02104 |
FR | FR22 | Picardie | −0.02645 | −0.01638 |
FR | FR23 | Haute-Normandie | −0.02749 | −0.01914 |
FR | FR24 | Centre | −0.02800 | −0.02013 |
FR | FR25 | Basse-Normandie | −0.02724 | −0.02294 |
FR | FR26 | Bourgogne | −0.01720 | −0.02707 |
FR | FR30 | Nord | −0.02573 | −0.01530 |
FR | FR41 | Lorraine | −0.00836 | −0.02221 |
FR | FR42 | Alsace | −0.02476 | −0.02621 |
FR | FR43 | Franche-Comté | −0.01708 | −0.02755 |
FR | FR51 | Pays de la Loire | −0.02881 | −0.02437 |
FR | FR52 | Bretagne | −0.02909 | −0.02755 |
FR | FR53 | Poitou–Charentes | −0.02908 | −0.02195 |
FR | FR61 | Aquitaine | −0.03287 | −0.02552 |
FR | FR62 | Midi-Pyrénées | −0.03135 | −0.03218 |
FR | FR63 | Limousin | −0.02751 | −0.02596 |
FR | FR71 | Rhône-Alpes | −0.01573 | −0.03007 |
FR | FR72 | Auvergne | −0.02372 | −0.02943 |
FR | FR81 | Languedoc-Roussillon | −0.03018 | −0.03472 |
FR | FR82 | Provence-Alpes-Côte d’Azur | −0.02145 | −0.03165 |
FR | FR83 | Corse | −0.03360 | −0.03846 |
IE | IE01 | Border, Midland and Western | −0.02945 | −0.02447 |
IE | IE02 | Southern and Eastern | −0.02839 | −0.02145 |
IT | ITC1 | Piemonte | −0.02273 | −0.03570 |
IT | ITC2 | Valle d’Aosta | −0.01868 | −0.03323 |
IT | ITC3 | Liguria | −0.02450 | −0.03897 |
IT | ITC4 | Lombardia | −0.03287 | −0.04005 |
IT | ITD5 | Emilia-Romagna | −0.05180 | −0.03658 |
IT | ITE1 | Toscna | −0.04515 | −0.04081 |
IT | ITE2 | Umbria | −0.04630 | −0.03866 |
IT | ITE3 | Marche | −0.04347 | −0.03312 |
IT | ITE4 | Lazio | −0.04299 | −0.04520 |
IT | ITF1 | Abruzzo | −0.04827 | −0.05604 |
IT | ITF2 | Molise | −0.05309 | −0.06160 |
IT | ITF3 | Campania | −0.05309 | −0.06160 |
IT | ITF4 | Puglia | −0.05728 | −0.06795 |
IT | ITF5 | Basilicata | −0.05766 | −0.06698 |
IT | ITF6 | Calabria | −0.05936 | −0.06709 |
IT | ITG1 | Sicilia | −0.05536 | −0.05978 |
IT | ITG2 | Sardegna | −0.05045 | −0.05225 |
IT | ITH1 | Provincia Autonoma di Bolzano | −0.05446 | −0.05803 |
IT | ITH2 | Provincia Autonoma di Trento | −0.04800 | −0.04856 |
IT | ITH3 | Veneto | −0.04475 | −0.04429 |
IT | ITH4 | Friuli-Venezia Giulia | −0.03583 | −0.03930 |
LU | LU0 | Luxembourg | −0.00117 | −0.01872 |
NL | NL11 | Groningen | −0.04957 | −0.02334 |
NL | NL12 | Friesland | −0.04987 | −0.02180 |
NL | NL13 | Drenthe | −0.05030 | −0.02261 |
NL | NL21 | Overijssel | −0.05148 | −0.02107 |
NL | NL22 | Gelderland | −0.04488 | −0.01823 |
NL | NL23 | Flevoland | −0.04948 | −0.01562 |
NL | NL31 | Utrecht | −0.04195 | −0.00958 |
NL | NL32 | Noord-Holland | −0.04076 | −0.01246 |
NL | NL33 | Zuid-Holland | −0.03385 | −0.00709 |
NL | NL34 | Zeeland | −0.02494 | −0.00682 |
NL | NL41 | Noord-Brabant | −0.02743 | −0.00345 |
NL | NL42 | Limburg | −0.02102 | −0.01447 |
PT | PT11 | Norte | −0.03289 | −0.03683 |
PT | PT15 | Algarve | −0.02936 | −0.02915 |
PT | PT16 | Centro | −0.02968 | −0.03434 |
PT | PT17 | Lisboa | −0.02910 | −0.03000 |
PT | PT18 | Alentejo | −0.02893 | −0.03068 |
UK | UKC1 | Tees Valley and Durham | −0.02386 | −0.02209 |
UK | UKC2 | Northumberland and Tyne and Wear | −0.02355 | −0.02073 |
UK | UKD1 | Cumbria | −0.02210 | −0.01628 |
UK | UKD2 | Cheshire | −0.01766 | 0.00291 |
UK | UKD3 | Greater Manchester | −0.01829 | 0.00215 |
UK | UKD4 | Lancashire | −0.01914 | −0.00389 |
UK | UKD5 | Merseyside | −0.01961 | −0.00160 |
UK | UKE1 | East Yorkshire and Northern Lincolnshire | −0.02391 | −0.01020 |
UK | UKE2 | North Yorkshire | −0.02187 | −0.01344 |
UK | UKE3 | South Yorkshire | −0.02110 | −0.00472 |
UK | UKE4 | West Yorkshire | −0.01989 | −0.00404 |
UK | UKF1 | Derbyshire and Nottinghamshire | −0.01904 | 0.00295 |
UK | UKF2 | Leicestershire, Rutland and Northamptonshire | −0.01952 | 0.00173 |
UK | UKF3 | Lincolnshire | −0.02505 | −0.00954 |
UK | UKG1 | Herefordshire, Worcestershire and Warwickshire | −0.02081 | −0.01085 |
UK | UKG2 | Shropshire and Staffordshire | −0.01910 | −0.00064 |
UK | UKG3 | West Midlands | −0.01942 | −0.00163 |
UK | UKH1 | East Anglia | −0.02577 | −0.01244 |
UK | UKH2 | Bedfordshire and Hertfordshire | −0.01789 | 0.00192 |
UK | UKH3 | Essex | −0.02151 | −0.00534 |
UK | UKI1 | Inner London | −0.01864 | −0.00086 |
UK | UKI2 | Outer London | −0.01690 | 0.00066 |
UK | UKJ1 | Berkshire, Buckinghamshire and Oxfordshire | −0.02184 | −0.00881 |
UK | UKJ2 | Surrey, East and West Sussex | −0.01973 | −0.00599 |
UK | UKJ3 | Hampshire and Isle of Wight | −0.02314 | −0.02022 |
UK | UKJ4 | Kent | −0.02203 | −0.00747 |
UK | UKK1 | Gloucestershire, Wiltshire and Bristol/Bath area | −0.02410 | −0.02361 |
UK | UKK2 | Dorset and Somerset | −0.02513 | −0.02655 |
UK | UKK3 | Cornwall and Isles of Scilly | −0.02765 | −0.02023 |
UK | UKK4 | Devon | −0.02695 | −0.02413 |
UK | UKL1 | West Wales and The Valleys | −0.02684 | −0.01633 |
UK | UKL2 | East Wales | −0.02506 | −0.01442 |
UK | UKM2 | Eastern Scotland | −0.02833 | −0.02089 |
UK | UKM3 | South Western Scotland | −0.02732 | −0.02014 |
UK | UKM5 | North Eastern Scotland | −0.02695 | −0.01394 |
UK | UKM6 | Highlands and Islands | −0.02798 | −0.01661 |
UK | UKN0 | Northern Ireland | −0.02984 | −0.00706 |
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Montresor, E., Pecci, F., Pontarollo, N. (2020). Structural Funds, Institutional Quality and Regional Economic Convergence in EU: A Spatial Econometric Approach. In: Thill, JC. (eds) Innovations in Urban and Regional Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-43694-0_13
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