Abstract
Club-convergence analysis provides a more realistic and detailed picture about regional income growth than traditional convergence analysis. This paper presents a spatial econometric framework for club-convergence testing that relates the concept of club-convergence to the notion of spatial heterogeneity. The study provides evidence for the club-convergence hypothesis in cross-regional growth dynamics from a pan-European perspective. The conclusions are threefold. First, we reject the standard Barro-style regression model which underlies most empirical work on regional income convergence in favour of a two regime [club] alternative in which different regional economies obey different linear regressions when grouped by means of Getis and Ord’s local clustering technique. Second, the results point to a heterogeneous pattern in the pan-European convergence process. Heterogeneity appears in both the convergence rate and the steady-state level. But, third, the study also reveals that spatial error dependence introduces an important bias in our perception of the club-convergence and shows that neglect of this bias would give rise to misleading conclusions.
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Notes
For a review of the empirical literature on regional income convergence see Magrini (2004). The vast majority of regional or international growth studies fail to consider and model spatial dependence and heterogeneity in the convergence process.
Multiplicity of steady-state equilibria is consistent with the neoclassical paradigm (Azariadis and Drazen 1990). If heterogeneity is permitted across regions, the dynamical system of the Solow growth model could be characterised by multiple steady-state equilibria, and club-convergence becomes a viable testable hypothesis despite diminishing marginal productivity of capital (Galor 1996).
The countries chosen are the EU-25 countries (except Cyprus and Malta) and the two accession countries Bulgaria and Romania.
There is a lack of reliable gross regional product figures in CEE countries. This comes partly from the change in accounting conventions now used in the CEE economies. More important, even if reliable estimates of the change in the volume of output produced did exist, these would be hardly possible to interpret meaningfully because of the fundamental change of production, from a centrally planned to a market system. As a consequence, figures for GRP are difficult to compare between EU-15 and CEE regions until the mid-1990s (European Commission 1999)
We express the definition in terms of the logarithm of per capita output between economies, as the empirical literature has generally focused on logs rather than levels.
This definition implies that σ-convergence is not guaranteed if y jt −y j' t does not converge to a limiting stochastic process. For example, if y jt =y j't equals one in even periods and minus one in odd periods, the two economies will fail to converge in the sense of σ-convergence, although the sample mean of the differences is equal to zero.
In some formulations of cross-section tests, Eq. 2 is modified to include a set of control variables. Here, a negative β means that convergence holds conditional on some set of exogenous factors such as national dummies, regional industrial structure, and various terms intended to capture possible endogenous growth effects such as regional educational levels and proxies for regional research and development (R&D). Galor (1996) has shown that the assessment of the conditional and the club-convergence hypothesis is nearly isomorphic from a neoclassical perspective.
As the pioneering paper of Baumol (1986), β has become a popular criterion for evaluating whether or not convergence holds. A negative correlation is taken as evidence of convergence as it implies that—on average—regions with lower per capita initial incomes are growing faster than those with higher initial per capita incomes.
Test Eq. 3 can be derived as a log-linear approximation from the transition path of the neoclassical model of growth for closed economies (Solow 1956) by taking a Taylor series approximation around a deterministic steady-state. Many studies share this neoclassical underpinning. The assumption of diminishing returns that drives the neoclassical convergence process and the assumption of a closed economy are particularly questionable for regional economies. But there are solid empirical reasons why it makes sense to fit growth regression models in which there is a significant convergence process even if the reasons for this convergence may be debated.
Convergence clubs had been studied in Baumol (1986); Chatterji (1992); Armstrong (1995); Dewhurst and Mutis-Gaitan (1995); Durlauf and Johnson (1995); Chatterji and Dewhurst (1996); Fagerberg and Verspagen (1996); Baumont et al. (2003), and LeGallo and Dall’erba (2005). But only the latter two studies have considered and modelled the spatial dimension of the growth and convergence process to avoid misspecification.
See Breiman et al. (1984) for a description of the procedure and its properties
Fagerberg and Verspagen (1996) have attempted to identify groups of similarly behaving European regions using, in principle, the second method and taking unemployment as control variable. The regression-tree procedure partitions the cross-section of 70 regions from six EU member countries (W-Germany, France, Italy, UK, Netherlands, and Belgium) into three distinct groups of regions determined by unemployment levels (high, intermediate, low). But the study—this holds also true for Durlauf and Johnson (1995)—fails to consider and model the spatial dimension of the growth and convergence process although it is evident from López-Bazo et al. (1999), Fingleton (1999), and others that this may be necessary to avoid misspecification.
Heterogeneity, in a spatial context, means broadly speaking, that the parameters describing the data vary from location to location.
The Chow test has been extended to spatial models (see Anselin 1990). In both the spatial lag and the spatial error models, the test is based on an asymptotic Wald statistic, distributed as chi-square with k=2 degrees of freedom.
For a more technical discussion of the effect of spatial autocorrelation see Anselin (1988a).
The vector of error terms ɛɛ is assumed to be normally distributed and independently of Y and W g, under the assumption that all spatial dependence effects are captured by the lagged variable.
Row-standardisation guarantees estimates for the spatial autoregressive coefficient, ρ, that yield a stable spatial model (see Anselin and Bera 1998).
The identification of the critical distance δ in this study is based on sensitivity analyses along with theoretical considerations.
Another approach towards estimating the two club-spatial lag model is based on the instrumental variable (IV) principle. This is equivalent to the two-stage least squares estimation in systems of simultaneous equations. The correlation between the spatial lag, Wg and the error term, ɛ, is controlled for by replacing the spatial lag variable with an appropriate instrument, that is, a variable which is highly correlated with Wg, but uncorrelated with ɛ. The choice of the appropriate instrument is a major problem in the practical implementation of this approach. As there are insufficient variables available to construct a good instrument in the context of the current study, we will not use this approach here.
Note that Eq. 12 can also be expressed as ɛ=(I n −λ W)−1 μ.
Kelejian and Prucha (1999) suggest an alternative estimation approach leading to a generalised moment estimator that is computationally simpler, irrespective of the sample size.
In practice, the spatially lagged constant is not included in W Y as there is an identification problem for a row-standardised W.
The common factor hypothesis can be tested, for example, by means of a likelihood ratio test: \(\chi ^{2}_{{{\left( 1 \right)}}} = - 2{\left( {L_{{\text{R}}} - L_{{\text{U}}} } \right)}\)where L R(L U) is the value of the log likelihood function for the restricted (unrestricted) estimator.
Focused tests for spatial dependence have been developed in a ML framework and generally take the Lagrange multiplier form rather than the asymptotically equivalent Wald or likelihood ratio form because of ease of computation. The Wald and likelihood ratio tests are computationally more demanding because they require ML estimation under the alternative; for technical details see Anselin and Bera (1998).
The sum of the main diagonal elements of the matrix in question.
Some authors (for example, Armstrong 1995, López-Bazo et al. 1999) use per capita GRP expressed in purchasing power standards (PPS). But as Ertur et al. (2004) point out, the construction of regional accounts in PPS that are comparable across space and time is very complicated and can raise serious problems. First, the conversion should be based on regional purchasing power parity, but—due to data non-availability—this adjustment is computed on the basis of national price levels. Second, per capita GRP expressed in PPS can change in one regional economy relative to another not only because of a difference in the rate of GRP growth in real terms but also because of relative price level changes. This complicates the analysis of growth changes over time because a relative increase in per capita GRP arising from a reduction in the relative price level might have a different implication than one resulting from a relative growth in real GRP (Ertur et al. 2004).
A full list of the regions along with the data used appear in the Appendix.
We exclude the French overseas Departments (French Guyane in South America and the small islands Guadaloupe, Martinique, and Réunion), the Portuguese regions of Azores and Madeira, the Canary Islands, and Ceuta y Mellila in Spain.
NUTS is the acronym for “Nomenclature of Territorial Units for Statistics” which is a hierarchical system of regions used by the statistical office of the European Community for the production of regional statistics. At the top of the hierarchy are the NUTS-0 regions (countries), below which are NUTS-1 regions (regions within countries) and then NUTS-2 regions (subdivisions of NUTS-1 regions).
The European Commission uses NUTS-2 and NUTS-3 regions as targets for the convergence process, and has defined NUTS-2 as the spatial level at which the persistence or disappearance of unacceptable inequality should be measured (Boldrin and Canova 2001). Since 1989, NUTS-2 is the spatial level at which eligibility for Objective 1, Structural Funds is determined (European Commission 1999). Cheshire and Carbonaro (1995) argue that functional urban areas would be more appropriate, but the problem with these spatial units is that they are dynamic rather than static so that their definition is not fixed in time.
The modifiable areal unit problem (MAUP) consists of two related parts: the scale problem and the zoning problem. The scale problem refers to the challenge to choose an appropriate spatial scale for the analysis while the zoning problem is concerned with the spatial configuration of the sample units. Study results may differ depending on the boundaries of the spatial units under study. If the regions of a country, for example, were configured differently, the results based on data for those regions would be different (Getis 2005).
Note that the statistic is based on a specification of the spatial weight matrix that is distinct from that in subsection 2.3, a specification where the main diagonal elements are set equal to one. This allows the statistic to include the information at region i. The statistic is asymptotically normally distributed as δ increases. Under the null hypothesis that there is no association between i and j within δ of i, the expectation is zero, the variance is one, thus, values of this statistic may be interpreted as the standard normal variate.
Club A (club B) represents a strong pattern which suggests that around region i regions with high (low) per capita GRP tend to be clustered more often than would be due to random choice.
The Appendix details the regions in the two clubs.
All estimation and specification tests in this study were carried out with SpaceStat (Anselin 1999).
A value of 12.225 for a chi-square distribution with two degrees of freedom.
Note that many of the specification tests are based on normality of errors. But this is rejected by the Jarque and Bera (1987) test. Because of the large sample, the test is very powerful, detecting significant deviations from normality which have, however, little practical significance in practice.
This conclusion confirms that spatial dependence in growth rates is not just caused by the spatial pattern in the distribution of initial GRP per capita.
The LM(error) test value is equal to 425.835 which is highly significant when referred to the chi-square distribution with one degree of freedom and exceeds the LM(lag) test value of 404.463. The same indication is given by the robust versions of the LM tests: LM*(error)=45.588 exceeds LM*(lag)=24.226.
The spatial view of the Breusch–Pagan test reveals heterogeneity. To accommodate error heterogeneity we estimated a clubwise error specification using generalised methods of moments approach (Kelejian and Prucha 1999). It is beyond the scope of this paper to go into detail, but it is worth mentioning that jointly modelling error heteroskedasticity and spatial dependence does change neither the estimates of the convergence parameters nor the estimates of the constants. The \(\widehat{{\beta _{A} }} = - 0.016(0.001)\,{\text{and}}\,{\kern2pt\hat{\kern-2pt\beta }} = - 0.026(0.000).\) The α-parameter estimates are \(\widehat{{\alpha _{A} }} = 0.206\,(0.000)\,{\text{and}}\,\widehat{{\alpha _{B} }} = 0.296\,(0.000).\,\widehat{\lambda } = 0.904\,(0.000)\,{\text{and}}\,{\text{Sigma sq}}{\text{.}}\,{\text{is}}\,{\text{0}}{\text{.00021}}\).
For prima facie empirical evidence of barriers to knowledge spillovers between high-technology firms in Europe see Fischer et al. (2006), accepted for publication in Geographical Analysis.
The likelihood ratio test statistic is 1.936 (p=0.380), and the Wald statistic 2.088 (p=0.352). Neither is strongly significant, indicating no inherent inconsistency in the spatial error specification.
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Acknowledgements
The authors gratefully acknowledge the grant no. P19025-G11 provided by the Austrian Science Fund (FWF). They also wish to thank two anonymous referees and the editor Roger Stough for their comments, which substantially improved the paper, and gratefully acknowledge the valuable technical assistance by Katharina Kobesova, Thomas Scherngell, and Thomas Seyffertitz (Institute for Economic Geography and GIScience), and Heiko Truppel (Centre for European Research).
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Appendix
Appendix
The regions and the data used in the study
Country | NUTS-2 region | Club membership | GRP 1995 per capita in ECU | GRP 2000 per capita in EURO |
|---|---|---|---|---|
Austria | Burgenland | B | 14,471.4 | 16,362.3 |
Niederösterreich | B | 18,010.3 | 21,616.2 | |
Wien | B | 31,565.1 | 35,067.6 | |
Kärnten | A | 19,129.5 | 21,440.0 | |
Steiermark | B | 18,649.8 | 21,417.8 | |
Oberösterreich | B | 20,965.3 | 24,445.6 | |
Salzburg | A | 25,927.4 | 29,220.7 | |
Tirol | A | 22,548.7 | 25,202.9 | |
Vorarlberg | A | 23,251.8 | 26,347.1 | |
Belgium | Région Bruxelles-Capitale | A | 42,263.1 | 48,920.2 |
Antwerpen | A | 24,487.9 | 28,109.5 | |
Limburg (B) | A | 17,865.4 | 20,364.3 | |
Oost-Vlaanderen | A | 18,142.9 | 21,056.1 | |
Vlaams Brabant | A | 20,496.4 | 25,217.2 | |
West-Vlaanderen | A | 19,187.1 | 22,174.8 | |
Brabant Wallon | A | 18,572.5 | 22,639.7 | |
Hainaut | A | 14,067.6 | 15,915.0 | |
Liège | A | 16,452.4 | 18,372.2 | |
Luxembourg (B) | A | 15,542.1 | 17,145.3 | |
Namur | A | 14,727.3 | 16,841.9 | |
Bulgaria | Severozapadan | B | 1,006.2 | 1,573.6 |
Severoiztochen | B | 1,012.2 | 1,479.4 | |
Severozapad | B | 1,045.4 | 1,512.4 | |
Yugozapaden | B | 1,616.1 | 2,207.0 | |
Yuzhen Tsentralen | B | 1,089.9 | 1,389.7 | |
Yugoiztochen | B | 1,009.7 | 1,691.5 | |
Czech Republic | Praha | B | 7,073.7 | 11,689.7 |
Stredni Cechy | B | 2,997.0 | 4,536.4 | |
Jihozapad | A | 3,658.7 | 5,059.8 | |
Severozapad | B | 3,609.3 | 4,423.9 | |
Severovychod | B | 3,353.5 | 4,645.5 | |
Jihovychod | B | 3,433.2 | 4,726.2 | |
Stredni Morava | B | 3,277.5 | 4,344.8 | |
Moravskoslezsko | B | 3,638.9 | 4,505.0 | |
Denmark | Denmark | A | 26,387.1 | 32,575.7 |
Estonia | Estonia | B | 1,884.2 | 4,063.7 |
Finland | Itä-Suomi | A | 15,014.5 | 18,167.6 |
Väli-Suomi | A | 16,373.4 | 20,574.0 | |
Pohjois-Suomi | A | 17,676.8 | 22,297.4 | |
Uusimaa | A | 25,724.6 | 34,898.4 | |
Etelä-Suomi | A | 18,103.1 | 23,394.6 | |
Åland | A | 23,817.6 | 33,926.6 | |
France | Île de France | A | 30,888.4 | 36,616.1 |
Champagne-Ardenne | A | 18,337.4 | 21,873.0 | |
Picardie | A | 16,890.3 | 19,039.6 | |
Haute-Normandie | A | 18,757.1 | 22,022.8 | |
Centre | A | 18,535.2 | 20,996.5 | |
Basse-Normandie | A | 17,090.6 | 19,734.6 | |
Bourgogne | A | 18,185.2 | 21,442.4 | |
Nord-Pas-de-Calais | A | 15,886.5 | 18,652.1 | |
Lorraine | A | 17,275.9 | 19,312.2 | |
Alsace | A | 20,977.8 | 23,790.8 | |
Franche-Comté | A | 17,759.7 | 20,265.4 | |
Pays de la Loire | A | 17,587.8 | 20,826.3 | |
Bretagne | A | 16,769.7 | 19,933.1 | |
Poitou-Charentes | A | 16,579.1 | 19,179.5 | |
Aquitaine | A | 17,776.3 | 20,899.1 | |
Midi-Pyrénées | A | 17,605.5 | 20,477.6 | |
Limousin | A | 16,205.5 | 18,959.9 | |
Rhône-Alpes | A | 20,168.8 | 23,852.0 | |
Auvergne | A | 16,600.3 | 20,006.1 | |
Languedoc-Roussillon | A | 15,376.0 | 17,968.9 | |
Provence-Alpes-Côte d’Azur | A | 18,365.3 | 21,001.4 | |
Corse | A | 14,493.3 | 17,588.5 | |
Germany | Stuttgart | A | 27,944.7 | 31,135.3 |
Karlsruhe | A | 26,541.4 | 29,112.6 | |
Freiburg | A | 22,498.8 | 24,408.3 | |
Tübingen | A | 23,735.1 | 25,553.9 | |
Oberbayern | A | 31,173.9 | 35,827.8 | |
Niederbayern | A | 21,775.6 | 22,573.7 | |
Oberpfalz | A | 22,260.5 | 25,029.8 | |
Oberfranken | A | 22,901.9 | 24,044.5 | |
Mittelfranken | A | 26,412.3 | 29,318.3 | |
Unterfranken | A | 22,255.0 | 24,068.5 | |
Schwaben | A | 23,701.7 | 24,963.4 | |
Berlin | B | 23,278.2 | 22,197.6 | |
Brandenburg | A | 15,063.8 | 16,117.9 | |
Bremen | A | 30,308.7 | 33,165.9 | |
Hamburg | A | 38,803.0 | 42,127.7 | |
Darmstadt | A | 31,967.6 | 34,525.7 | |
Gießen | A | 20,703.2 | 22,058.0 | |
Kassel | A | 22,163.8 | 23,517.7 | |
Mecklenburg-Vorpommern | A | 14,895.2 | 16,101.6 | |
Braunschweig | A | 21,656.4 | 24,617.2 | |
Hannover | A | 23,894.8 | 25,124.4 | |
Lüneburg | A | 18,406.3 | 18,220.3 | |
Weser-Ems | A | 20,468.5 | 20,909.6 | |
Düsseldorf | A | 26,003.6 | 28,126.1 | |
Köln | A | 25,922.2 | 26,800.1 | |
Münster | A | 20,025.4 | 20,362.5 | |
Detmold | A | 23,233.0 | 24,483.8 | |
Arnsberg | A | 21,728.2 | 23,143.3 | |
Koblenz | A | 20,073.0 | 20,777.9 | |
Trier | A | 19,256.3 | 19,817.4 | |
Rheinhessen-Pfalz | A | 22,798.0 | 24,366.1 | |
Saarland | A | 21,869.4 | 22,475.9 | |
Chemnitz | A | 14,053.4 | 15,303.1 | |
Dresden | B | 15,372.8 | 16,627.9 | |
Leipzig | A | 17,014.7 | 17,415.1 | |
Dessau | A | 13,457.5 | 14,892.2 | |
Halle | A | 14,823.6 | 16,245.8 | |
Magdeburg | A | 13,877.9 | 16,043.1 | |
Schleswig-Holstein | A | 21,999.8 | 22,323.0 | |
Thüringen | A | 14,136.0 | 16,148.1 | |
Greece | Anatoliki Makedonia, Thraki | B | 7,249.6 | 9,407.6 |
Kentriki Makedonia | B | 8,398.2 | 11,701.3 | |
Dytiki Makedonia | B | 8,215.2 | 11,550.7 | |
Thessalia | B | 7,444.3 | 10,574.1 | |
Ipeiros | B | 5,611.0 | 8,112.1 | |
Ionia Nisia | B | 7,326.6 | 10,193.0 | |
Dytiki Ellada | B | 6,873.3 | 8,799.1 | |
Sterea Ellada | B | 10,790.6 | 13,158.8 | |
Peloponnisos | B | 6,751.8 | 9,933.8 | |
Attiki | B | 9,876.4 | 13,287.0 | |
Voreio Aigaio | B | 7,677.0 | 11,297.1 | |
Notio Aigaio | B | 9,642.3 | 13,742.3 | |
Kriti | B | 8,497.5 | 11,389.6 | |
Hungary | Közép-Magyarország | B | 2,990.3 | 4,975.5 |
Közép-Dunántúl | B | 4,769.4 | 7,540.8 | |
Nyugat-Dunántúl | B | 3,402.1 | 5,641.5 | |
Dél-Dunántúl | B | 2,697.3 | 3,706.2 | |
Észak-Magyarország | B | 2,404.5 | 3,198.6 | |
Észak-Alföld | B | 2,355.7 | 3,142.2 | |
Dél-Alföld | B | 2,748.3 | 3,559.9 | |
Ireland | Border, Midland and Western | A | 10,679.7 | 19,710.9 |
Southern and Eastern | A | 15,366.9 | 29,733.5 | |
Italy | Piemonte | A | 17,221.0 | 23,634.5 |
Valle d’Aosta | A | 19,790.3 | 24,340.9 | |
Liguria | A | 15,127.6 | 21,360.3 | |
Lombardia | A | 19,490.3 | 26,588.9 | |
Trentino-Alto Adige | A | 19,439.7 | 26,941.0 | |
Veneto | A | 17,258.8 | 23,526.1 | |
Friuli-Venezia Giulia | A | 16,839.8 | 22,559.6 | |
Emilia-Romagna | A | 18,771.9 | 25,522.6 | |
Toscana | A | 15,949.3 | 22,441.9 | |
Umbria | A | 14,388.1 | 19,883.2 | |
Marche | A | 14,603.1 | 20,173.3 | |
Lazio | A | 16,579.7 | 22,312.2 | |
Abruzzo | A | 12,499.7 | 16,543.4 | |
Molise | A | 10,962.9 | 15,573.9 | |
Campania | A | 9,252.9 | 12,907.7 | |
Puglia | B | 9,446.9 | 13,270.3 | |
Basilicata | A | 9,975.3 | 14,510.6 | |
Calabria | B | 8,671.0 | 12,285.5 | |
Sicilia | B | 9,327.9 | 12,935.1 | |
Sardegna | A | 10,756.9 | 14,926.1 | |
Latvia | Latvia | B | 1,359.4 | 3,276.7 |
Lithuania | Lithuania | B | 1,268.4 | 3,484.9 |
Luxembourg | Luxembourg | A | 33,481.1 | 47,199.5 |
The Netherlands | Groningen | A | 24,380.6 | 28,263.6 |
Friesland | A | 17,123.1 | 20,794.3 | |
Drenthe | A | 17,212.5 | 19,986.2 | |
Overijssel | A | 17,631.0 | 21,471.8 | |
Gelderland | A | 18,009.3 | 21,969.3 | |
Flevoland | A | 15,647.8 | 18,170.2 | |
Utrecht | A | 24,502.0 | 31,900.2 | |
Noord-Holland | A | 23,639.4 | 29,608.6 | |
Zuid-Holland | A | 21,395.6 | 26,310.2 | |
Zeeland | A | 19,867.7 | 22,172.6 | |
Noord-Brabant | A | 20,004.7 | 25,018.1 | |
Limburg (NL) | A | 17,968.4 | 22,198.0 | |
Poland | Dolnoslaskie | B | 2,617.8 | 4,571.8 |
Kujawsko-Pomorskie | B | 2,507.5 | 3,965.1 | |
Lubelskie | B | 1,940.9 | 3,030.3 | |
Lubuskie | B | 2,475.4 | 3,967.0 | |
Lódzkie | B | 2,298.5 | 3,922.7 | |
Malopolskie | B | 2,229.0 | 3,948.4 | |
Mazowieckie | B | 3,135.4 | 6,704.2 | |
Opolskie | B | 2,484.9 | 3,778.9 | |
Podkarpackie | B | 1,950.1 | 3,145.5 | |
Podlaskie | B | 1,908.9 | 3,286.7 | |
Pomorskie | B | 2,526.9 | 4,446.9 | |
Slaskie | B | 3,098.5 | 4,867.4 | |
Swietokrzyskie | B | 2,000.1 | 3,460.0 | |
Warminsko-Mazurskie | B | 2,007.9 | 3,295.9 | |
Wielkopolskie | B | 2,479.0 | 4,715.3 | |
Zachodniopomorskie | B | 2,591.0 | 4,363.3 | |
Portugal | Norte | B | 6,966.9 | 9,259.9 |
Centro (P) | B | 6,737.6 | 8,959.1 | |
Lisboa e Vale do Tejo | B | 10,719.4 | 15,023.7 | |
Alentejo | B | 6,993.3 | 9,006.2 | |
Algarve | B | 8,474.4 | 10,908.1 | |
Romania | Nord-Est | B | 956.1 | 1,250.9 |
Sud-Est | B | 1,176.2 | 1,592.1 | |
Sud | B | 1,139.5 | 1,472.0 | |
Sud-Vest | B | 1,146.5 | 1,512.8 | |
Vest | B | 1,298.9 | 1,846.0 | |
Nord-Vest | B | 1,122.5 | 1,664.4 | |
Centru | B | 1,286.0 | 1,910.6 | |
Bucuresti | B | 1,631.8 | 3,698.9 | |
Slovenia | Slovenia | A | 7,214.8 | 9,815.0 |
Slovak Republic | Bratislavský kraj | B | 5,443.2 | 8,426.4 |
Západné Slovensko | B | 2,562.6 | 3,669.0 | |
Stredné Slovensko | B | 2,354.1 | 3,329.2 | |
Východné Slovensko | B | 2,166.1 | 3,050.8 | |
Spain | Galicia | B | 9,210.2 | 12,010.6 |
Principado de Asturias | A | 10,043.4 | 13,155.9 | |
Cantabria | A | 10,595.3 | 14,900.5 | |
País Vasco | A | 13,599.2 | 18,836.2 | |
Comunidad Foral de Navarra | A | 14,447.6 | 19,546.0 | |
La Rioja | A | 13,082.2 | 16,929.8 | |
Aragón | A | 12,355.1 | 16,316.0 | |
Comunidad de Madrid | A | 14,997.4 | 20,411.8 | |
Castilla y León | A | 10,858.2 | 14,089.0 | |
Castilla-la Mancha | A | 9,349.4 | 12,391.0 | |
Extremadura | B | 7,189.3 | 9,838.3 | |
Cataluña | A | 13,922.5 | 18,468.3 | |
Comunidad Valenciana | A | 10,814.5 | 14,705.2 | |
Islas Baleares | A | 14,151.8 | 18,249.0 | |
Andalucia | B | 8,454.5 | 11,353.4 | |
Región de Murcia | A | 9,506.6 | 12,749.8 | |
Sweden | Stockholm | A | 26,281.1 | 40,454.1 |
Östra Mellansverige | A | 19,592.8 | 25,164.8 | |
Sydsverige | A | 19,572.0 | 27,095.6 | |
Norra Mellansverige | A | 20,855.4 | 25,038.4 | |
Mellersta Norrland | A | 22,031.1 | 26,716.1 | |
Övre Norrland | A | 21,423.1 | 25,309.2 | |
Småland med öarna | A | 20,476.9 | 26,724.7 | |
Västsverige | A | 20,572.4 | 27,871.3 | |
UK | Tees Valley & Durham | A | 12,161.9 | 19,779.5 |
Northumberland & Tyne & Wear | A | 12,344.7 | 20,429.0 | |
Cumbria | A | 14,999.7 | 23,681.9 | |
Cheshire | A | 17,136.5 | 29,756.7 | |
Greater Manchester | A | 13,367.7 | 23,048.0 | |
Lancashire | A | 12,821.5 | 21,095.1 | |
Merseyside | A | 10,506.5 | 18,263.3 | |
East Riding & North Lincolnshire | A | 14,123.8 | 24,609.3 | |
North Yorkshire | A | 13,874.6 | 24,503.4 | |
South Yorkshire | A | 10,822.6 | 19,447.9 | |
West Yorkshire | A | 13,669.7 | 23,807.5 | |
Derbyshire & Nottinghamshire | A | 13,177.3 | 23,382.0 | |
Leicestershire, Rutland & Northamptonshire | A | 15,275.5 | 26,690.4 | |
Lincolnshire | A | 12,591.2 | 22,059.3 | |
Herefordshire, Worcestershire & Warwick | A | 14,226.3 | 25,289.8 | |
Shropshire & Staffordshire | A | 12,461.1 | 22,393.8 | |
West Midlands | A | 14,274.4 | 24,151.0 | |
East Anglia | A | 15,833.4 | 28,414.8 | |
Bedfordshire & Hertfordshire | A | 15,212.5 | 27,831.5 | |
Essex | A | 13,231.9 | 24,358.2 | |
Inner London | A | 35,279.9 | 62,788.2 | |
Outer London | A | 12,599.4 | 22,754.4 | |
Berkshire, Buckinghamshire & Oxfordshire | A | 18,411.4 | 33,957.4 | |
Surrey, East & West Sussex | A | 14,476.3 | 27,403.8 | |
Hampshire & Isle of Wight | A | 14,682.6 | 28,432.7 | |
Kent | A | 14,054.3 | 24,380.7 | |
Gloucestershire, Wiltshire & N. Somerset | A | 15,848.5 | 27,311.1 | |
Dorset & Somerset | A | 12,936.5 | 22,612.6 | |
Cornwall & Isles of Scilly | A | 9,443.8 | 16,898.0 | |
Devon | A | 12,174.0 | 20,595.4 | |
West Wales & The Valleys | A | 10,720.4 | 18,397.2 | |
East Wales | A | 15,450.8 | 25,433.2 | |
North Eastern Scotland | A | 19,820.5 | 31,983.1 | |
Eastern Scotland | A | 15,574.4 | 26,084.2 | |
South Western Scotland | A | 14,167.8 | 24,097.6 | |
Highlands and Islands | A | 11,872.0 | 19,606.6 | |
Northern Ireland | A | 12,066.0 | 20,223.9 |
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Fischer, M.M., Stirböck, C. Pan-European regional income growth and club-convergence. Ann Reg Sci 40, 693–721 (2006). https://doi.org/10.1007/s00168-005-0042-6
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DOI: https://doi.org/10.1007/s00168-005-0042-6