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Regional economic development in Europe: the role of total factor productivity

Regional economic development in Europe: the role of total factor productivity REGIONAL STUDIES, 2018 VOL. 52, NO. 4, 461–476 https://doi.org/10.1080/00343404.2017.1334118 Regional economic development in Europe: the role of total factor productivity a b c Sjoerd Beugelsdijk , Mariko J. Klasing and Petros Milionis ABSTRACT Regional economic development in Europe: the role of total factor productivity. Regional Studies. This paper documents the fact that the large and persistent differences in economic development across subnational regions in European Union countries can largely be attributed to differences in total factor productivity (TFP). Applying the technique of development accounting, the paper decomposes differences in output per worker across 257 European Union regions into a component due to the local availability of production factors and a component due to TFP. As the analysis reveals, TFP differences are large even within countries, and are strongly related to economic geography and historical development paths. This suggests limited interregional diffusion of technology and of efficient production practices. KEYWORDS regional economic development; total factor productivity; development accounting; European regions 摘要 欧洲的区域经济发展:全要素生产力的角色。Regional Studies. 本文纪录欧盟横跨次国家区域的经济发展中,广大且 续存的差别可大幅归因于全要素生产力(TFP)的差别之现实。本研究运用发展会计的技术,将欧盟二百五十七座区 域中工人的平均产出差异,分解为由生产要素的在地可及性所导致的因素,以及由TFP所导致的因素。本分析揭露, TFP的差异,即便在国家内部亦相当大,并与经济地理和历史发展路径强烈相关。这显示出有限的跨区域技术扩散及 有效的生产实践。 关键词 区域经济发展; 全要素生产力; 发展会计; 欧洲区域 RÉSUMÉ L’aménagement du territoire en Europe: le rôle de la productivité globale des facteurs. Regional Studies. Cet article démontre que les importants écarts de développement économique qui persistent à travers les régions infranationales situées dans les pays-membres de l’Union européenne s’expliquent dans une large mesure par les écarts de productivité globales des facteurs (PGF). En appliquant la méthode de la comptabilité de développement (development accounting), on décompose les écarts de rendement par travailleur selon 257 régions de l’Union européenne en une composante relative à la disponibilité locale des facteurs de production et une deuxième composante qui s’explique par la PGF. Comme laisse voir l’analyse, les écarts de PGF s’avèrent importants même au sein des pays, et se rapportent étroitement à la géographie économique et aux sentiers de développement historiques. Cela laisse supposer une diffusion interrégionale limitée de la technologie et des procédés de production efficaces. MOTS-CLÉS aménagement du territoire; productivité globale des facteurs; comptabilité de développement; régions européennes CONTACT s.beugelsdijk@rug.nl Department of Global Economics & Management, Faculty of Economics & Business, University of Groningen, Groningen, the Netherlands. m.j.klasing@rug.nl Department of Global Economics & Management, Faculty of Economics & Business, University of Groningen, Groningen, the Netherlands. (Corresponding author) p.milionis@rug.nl Department of Economics, Econometrics & Finance, Faculty of Economics & Business, University of Groningen, Groningen, the Netherlands. © 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc- nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. 462 Sjoerd Beugelsdijk et al. ZUSAMMENFASSUNG Regionale Wirtschaftsentwicklung in Europa: die Rolle der Gesamtfaktorproduktivität. Regional Studies. In diesem Beitrag dokumentieren wir die Tatsache, dass sich die umfangreichen und anhaltenden Unterschiede in der Wirtschaftsentwicklung verschiedener subnationaler Regionen der Mitgliedstaaten der Europäischen Union in großem Umfang auf Unterschiede bei der Gesamtfaktorproduktivität zurückführen lassen. Unter Anwendung der Technik der Entwicklungsbilanzierung zerlegen wir die Unterschiede bei der Leistung pro Arbeitnehmer in 257 Regionen der Europäischen Union in eine durch die lokale Verfügbarkeit von Produktionsfaktoren bedingte Komponente sowie in eine durch die Gesamtfaktorproduktivität bedingte Komponente. Aus der Analyse geht hervor, dass die Unterschiede bei der Gesamtfaktorproduktivität selbst innerhalb desselben Landes umfangreich ausfallen und in einem engen Zusammenhang mit der Wirtschaftsgeografie und den bisherigen Entwicklungspfaden stehen. Dies lässt auf eine begrenzte interregionale Diffusion von Technik und effizienten Produktionspraktiken schließen. SCHLÜSSELWÖRTER regionale Wirtschaftsentwicklung; Gesamtfaktorproduktivität; Entwicklungsbilanzierung; europäische Regionen RESUMEN Desarrollo económico regional en Europa: el papel de la productividad total de los factores. Regional Studies. En este artículo documentamos el hecho de que las diferencias enormes y persistentes en el desarrollo económico en las regiones subnacionales de los países de la Unión Europea pueden atribuirse en gran medida a las diferencias en la productividad total de los factores (PTF). Aplicando la técnica de la contabilidad del desarrollo, desglosamos las diferencias de rendimiento por trabajador en 257 regiones de la Unión Europea en un componente según la capacidad local de los factores de producción y un componente según la PTF. A partir del análisis podemos determinar que las diferencias en la PTF son mayores incluso dentro de un mismo país, y están estrechamente vinculadas con la geográfica económica y las rutas de desarrollo histórico. Esto indica una difusión interregional limitada de la tecnología y las prácticas de producción eficientes. PALABRAS CLAVES desarrollo económico regional; productividad total de los factores; contabilidad del desarrollo; regiones europeas JEL O18, O47, O52, R10 HISTORY Received 22 September 2015; in revised form 8 May 2017 countries have been very stable, with a correlation coeffi- INTRODUCTION cient of around 0.93 since 2000 and of 0.84 since 1980. A better understanding of the persistent nature of these regional economic disparities in Europe is important for two Within-country differences in the level of economic devel- reasons. First, it is the European Commission’s explicit goal opment are large. Using data from 2005 for a large sample to reduce economic disparities between EU regions in order of subnational regions across the world, Gennaioli, La to promote social cohesion (European Commission, 2010). Porta, Lopez-de Silanes, and Shleifer (2013) show that Second, contemporary EU Cohesion Policy emphasizes the the ratio of income per worker between a country’s richest role of technological progress, innovation and knowledge and poorest region is on average equal to 4.4. The same externalities (Barca, 2009; McCann & Ortega-Argilés, data indicate also sizeable income differences within Euro- 2015), recognizing that improvements in productivity are pean Union (EU) countries, which are relatively small and key to enhancing regional economic performance, and that homogeneous, with the corresponding ratio being approxi- innovation and knowledge creation are critical to achieve mately equal to 2.2. such productivity gains. Against this background, this Starting with Barro & Sala-i-Martin (1991), Sala-i- paper explores three related research questions: Martin (1996), and Quah (1996), a vast literature has explored the evolution of these regional income differences over time and the extent to which they have been growing How big are regional differences in technological or shrinking, leading to income convergence or divergence. sophistication and production efficiency, as captured While this literature provides evidence of some degree of by total factor productivity (TFP), and which regions convergence taking place among groups of closely inte- are Europe’s leaders and laggards in terms of TFP? grated regions (Bosker, 2009; Fischer & Stirboeck, 2006; What is the relative importance of differences in TFP in Geppert & Stephan, 2008), overall, regional income dis- explaining differences in the level of economic develop- parities are very persistent. EUROSTAT data show that ment across EU regions? the ratio of incomes between a country’s richest and poorest Which are the main factors that can account for the region as well as the income ranking of regions within observed regional differences in TFP? REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 463 To address these questions, this paper uses the technique of countries and is robust to modifications in the way factor development accounting to decompose regional differences inputs are measured. in output per worker into a component capturing the local Having documented that a large share of regional availability of measurable production factors and a com- income differences is due to variation in TFP, the paper ponent related to TFP. Using development accounting to proceeds to explore which factors can account for this vari- assess the relative contributions of differences in pro- ation. For this purpose it considers a broad range of factors duction factors versus TFP across countries is standard in from the literature on economic development and regional the growth and development literature (Caselli, 2005; economics, which are not already accounted for in the cal- Hall & Jones, 1999; Klenow & Rodríguez-Clare, 1997) culation of regional TFP levels. These are factors related to and has produced important insights regarding the mech- a region’s physical and economic geography, its economic anics of economic development. Yet, it has to structure, its cultural characteristics and institutional qual- our knowledge never been systematically applied at the ity, and its history. Regressing the computed regional TFP regional level. levels on all these factors, while controlling for country- Most of the existing analyses of productivity differences specific effects, the paper finds that the observed variation at the regional level have focused on labour productivity in TFP can be largely attributed to regional differences in (LP), measured simply as output per worker (Corrado, terms of economic geography and historical development Martin, & Weeks, 2005; Esteban, 2000; Gardiner, Martin, paths. This pattern is robust to a battery of additional & Tyler, 2004; Vieira, Neira, & Vázquez, 2011). This is tests including alternative methods to calculate TFP and because LP can be calculated easily from available data considerations of regional heterogeneity and spatial inter- on output and employment. Measuring TFP, on the action across regions. other hand, requires data on other inputs as well, such as The above described findings regarding (1) the large physical and human capital, which are not widely available. size of TFP differences within EU countries and (2) their Looking at TFP, however, has an important advantage strong association with economic geography and historical over LP. It captures productivity conditional not only on development are new and in line with the theoretical pre- available labour inputs but also on other factors of pro- dictions of New Economic Geography (NEG). NEG duction. It reflects solely the efficiency with which different models attribute a critical role to agglomeration effects production inputs are utilized and combined, while LP and localized knowledge spillovers in explaining growth bundles production efficiency and the availability of non- and development patterns (Krugman, 1991, 1993). As labour inputs together into one measure. TFP therefore such, this paper complements previous empirical studies captures better the overall sophistication of the production that have related regional productivity advantages to the process. To the extent that TFP has been studied at the geographical concentration of economic activity (Ciccone regional level (e.g., Capello & Lenzi, 2015; Dettori, Mar- & Hall, 1996; Henderson, Kuncoro, & Turner, 1995) rocu, & Paci, 2012; Marrocu, Paci, & Usai, 2013), it has and knowledge spillovers (Anselin, Varga, & Acs, 1997; been estimated indirectly by means of regression analyses Jaffe, Trajtenberg, & Henderson, 1993; Rauch, 1993). that derive TFP as residuals from regressions of output The separation between factor inputs and TFP that this levels on production inputs. This approach requires factor paper provides, however, goes a step further by highlighting inputs and TFP to be orthogonal, an assumption which how the regional concentration of production activities is unlikely to hold in practice due to complementarities spurs technological progress and gives rise to more efficient between factor inputs and productivity. The develop- production practices that are slow to diffuse even within the ment-accounting approach followed in this paper, in con- same country. trast, does not rely on such an assumption. Overall, the paper suggests that the spatial dimension of Using data from EUROSTAT and focusing on 257 technology diffusion is an important factor behind the per- NUTS-2 regions embedded in 21 of the current 28 EU sistent development gaps across European regions. This countries, the paper performs a development-accounting implies that in order to promote regional economic devel- analysis for 2007. We focus on NUTS-2 regions as these opment and reduce regional disparities, regional policy are the administrative units at which most EU regional pol- should focus on facilitating the diffusion of knowledge icies are targeted, and conduct the analysis based on 2007 and best practices and support regions in specializing data in order to abstract from the influences of the post- smartly by building on existing synergies and exploiting 2007 financial crisis. The results of the analysis demon- economies of scale (McCann & Ortega-Argilés, 2015). strate that both across and within countries TFP differ- The paper is structured as follows. The next section ences explain most of the observed variation in output outlines the basic rationale behind the development per worker. Specifically, we find that measurable factor accounting approach and describes the data on the basis inputs account for about 23% of the variation in output of which TFP is computed. The third section discusses per worker. This implies that differences in technological the obtained TFP figures and their importance in explain- sophistication and production efficiency account for most ing regional differences in output per worker. The fourth of the differences in regional economic development, cor- section presents the results of the regression analyses roborating previous work documenting the important role regarding the correlates of within-country TFP differences. of TFP at the country level (Hsieh & Klenow, 2010). The final section summarizes the findings and discusses This percentage is only slightly higher across than within their broader implications. REGIONAL STUDIES 464 Sjoerd Beugelsdijk et al. Data DEVELOPMENT ACCOUNTING: The data used to calculate TFP levels for regions in the EU METHODOLOGY AND DATA are taken from the EUROSTAT Regional Database. The database covers regions at different levels of aggregation Methodology following the NUTS classification of the EU. The present Development accounting constitutes a well-established analysis focuses on 257 NUTS-2 regions in 21 EU methodology for disentangling observed differences in out- countries, excluding small EU countries and few overseas put levels into differences in factors of production and territories. To calculate TFP levels we need data on output differences in TFP. It builds on the works of Klenow and and employment, which are readily available, and data on Rodríguez-Clare (1997), Hall and Jones (1999), and Case- physical and human capital, which we construct ourselves lli (2005). It begins by postulating an aggregate production based on the available information for investment spending function, which, following the standard in the literature, is and educational attainment. taken to be of the Cobb–Douglas form: a 1−a Y = A K (h L ) (1) it it it it it Output per worker where Y is aggregate output in region i in year t; K is the The employed output data reflect gross value added (GVA) it it respective stock of physical capital; L is the employed in each region, which excludes taxes paid or subsidies it labour force; and h is the average level of human capital it received from the government, and are based on the Euro- of each worker. A reflects the efficiency with which the pean System of Accounts (ESA) 2010 accounting stan- it factors inputs K , h and L are used in the production dards. The data are adjusted for price differences across it it it process, or, in other words, TFP. α is the capital share of countries and over time with country-specific purchasing output, which in our baseline case is assumed to be the power standard (PPS) indices and price deflators provided same in all regions and throughout all time periods. by EUROSTAT. This way the nominal GVA series is Specifically, we set α ¼ 1/3, the typical value assumed in converted into constant 2005 PPS terms. The resulting the macroeconomic literature reflecting the cross-sectional figures are then divided by the total number of workers, and time-series evidence reported by Gollin, Parente, and including self-employed individuals, in each region. Rogerson (2002). In our robustness analyses, we also Thus, the output data used in the calculations of regional explore the alternative approach of allowing for region- TFP levels correspond to regional purchasing-power- specific capital income shares by using a generalized trans- adjusted levels of real GVA per worker. log version of equation (1) as in Inklaar and Timmer (2013). This more flexible approach permits differences Physical capital per worker in production structures across regions to be reflected in To obtain estimates of regional physical capital stocks the different values for α. perpetual inventory method is employed. This method Rewriting equation (1) in per worker terms, the pro- allows for the construction of a capital stock series based duction function implies that output per worker, y ,is a it on investment data using the formula: function of the per worker inputs of physical capital, k , it and human capital, h : K = I + (1 − d )K . (4) it it it i it−1 a 1−a y = A k h (2) Thus, the physical capital stock, K , in region i in period t it it it it it is equal to the capital investment, I , in that period plus the it We use this expression to back out the level of productivity amount of un-depreciated capital left over from the pre- from data on y , k and h . Based on expression (2), we can it it it vious period, with d indicating the rate of physical capital also assess how much of the regional variation in output per depreciation. worker is explained by variation in the factor inputs and Data on regional investment in terms of gross fixed how much should be attributed to underlying differences capital formation are available in the EUROSTAT in TFP. This can be done, as discussed in greater detail Regional Database for the years since 2000. These are by Caselli (2005), by performing a standard variance then converted from current prices to constant prices by decomposition and calculating the following statistic: using the country-level price deflator of gross fixed-capital kh formation reported by EUROSTAT. The depreciation Var[ ln (y )] kh it kh a 1−a V = , with y = k h (3) rate d is allowed to vary across regions. Specifically, the t it it it i Var[ ln (y )] it region-specific depreciation rates employed are weighted kh Specifically, V reflects the share of the observed variance averages of the sector-specific depreciation rates reported in the natural logarithm of output per worker across regions in the World Input–Output Database (WIOD) database that is explained solely by variation in physical and human (Timmer, Dietzenbacher, Los, Stehrer, & de Vries, capital. Note that this share would be equal to 1 if A were 2015) with the weights corresponding to the average it the same across all regions, and it would be strictly less than share of each region’s sector in the total GVA of each 1 as long as there is some regional variation in TFP. Thus, region between 2000 and 2007. kh lower values for V imply that a larger share of the Beyond data on investment spending and depreciation observed differences in output per worker should be attrib- rates, the application of the perpetual inventory formula uted to TFP. requires also a value for the capital stock in the initial REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 465 year, which in our case is 2000. Typically, in the literature shares with the respective years of schooling attained at this value is a guesstimate (Bernanke & Gürkaynak, 2002; each level. In addition to this baseline estimate, we also Klenow & Rodríguez-Clare, 1997), which in the case of a make two alternative assumptions regarding the years of low long time-series for investment has little effect on end-of- schooling attained. In our lower-bound estimate, ays , it period capital stock estimates. In our case, though, as the we assume that ISCED levels 0–2, 3–4 and 4–5 correspond investment series is relatively short, the end-of-period esti- to six, nine and 12 years of schooling respectively. In our high mate is likely to be sensitive to the initial value chosen. In upper-bound estimate, ays , we assume nine, 12 and 16 it light of this, we use three different approaches to pin down years of schooling for each of the respective ISCED edu- the value of the capital stock in 2000. cation levels. Section B in the supplemental material online For the baseline series, K , the paper follows the provides details on these assumptions and discusses robust- it approach proposed by Garofalo and Yamarik (2002) and ness checks. attributes the country-level sector-specific capital stocks To convert average schooling years into human capital, reported in WIOD (Timmer et al., 2015) to each region we assume a standard Mincerian human capital function of based on the share of each region’s GVA in that sector in the form: the country-wide GVA in that sector. w(ays ) it B h = e it For the alternative series, K , we apportion the it country-level capital stocks reported in Penn World Tables where w(ays ) is a piecewise linear and parameterized as it 8 (PWT8) (Feenstra, Inklaar, & Timmer, 2015) to each follows: country’s subnational regions based on the share of each ⎧ ⎫ 0.134 · ays if ays ≤ 4 ⎪ it it ⎪ region’s average share in the GVA of the country. ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0.134 · 4 if 4 , ays ≤ 8 ⎨ it ⎬ For the alternative series, K , we follow Feenstra et al. it w(ays ) = + 0.101(ays − 4) . it it (2015) and postulate for all regions a capital-output ratio of ⎪ ⎪ ⎪ ⎪ ⎪ 0.134 · 4 + 0.101 · 4 if ays . 4 ⎪ it 2.6. This produces a conservative estimate for the initial ⎪ ⎪ ⎩ ⎭ + 0.068(ays − 8) it differences in capital stocks across regions that ignores existing variation in capital intensities due to variation in The assumed values for the returns to schooling follow the the sectoral composition of each region. More details earlier development-accounting exercises of Hall and Jones regarding the construction of the initial values for these (1999) and Caselli (2005) and are in line with the microe- three initial capital stock series are explained in Section A conomic evidence summarized by Psacharopoulos (1994). in the supplemental material online. They are also identical to the values used in the PWT8 By using these three alternative values for the initial to convert years of schooling into human capital. Thus, regional capital stock, we can produce three different our human capital estimates are comparable with the regional capital stock series for the subsequent years. The country-level estimates reported in the PWT8. In accord- A B C per worker capital stocks, k , k , k , are then constructed it it it ance with the three sets of figures for ays , we derive three it by dividing the estimated figures for each region with the sets of figures for the average level of human capital per corresponding number of workers and multiplying by the base region: the baseline estimate, h , and the two alternatives, it high country-specific price indexes for capital goods, which low h and h . it it matches the units in which the employed output data are measured. TFP DIFFERENCES ACROSS EUROPEAN Human capital REGIONS To measure the average level of human capital in each region, we use information on the share of the working-age popu- Based on the above-described figures for regional output per lation that has attained different levels of education. The worker, physical capital per worker and human capital per EUROSTAT Regional Database provides data on the worker, we compute TFP scores for our sample of 257 share of the population aged 25–64 years who have attained NUTS-2 regions. For the purpose of comparison, we also each of the following levels in the International Standard calculate TFP scores for the 21 countries in which these Classification of Education (ISCED) system: 257 regions are nested. Given the three different physical capital and alternative human capital stocks series and the ISCED 0–2: Pre-primary, primary, lower secondary. possibility to allow for region-specific capital elasticities α, ISCED 3–4: Upper secondary, post-secondary non- we construct six different TFP estimates. Our baseline tertiary. TFP estimate, A , is computed from equation (2) based ISCED 5–6: First- and second-stage tertiary. on a fixed α ¼ 1/3 and using the physical capital stock esti- A base mate k , and the human capital estimate h . The second it it Following Barro and Lee (2013), we assume that ISCED estimate, A , uses the same physical and human capital 0–2 corresponds to six years of schooling, ISCED 3–4to stock estimates but employs a translog production function 12 years, and ISCED 5–6 to 16 years. Based on this with region-specificvaluesfor α instead. The region-specific assumption, our baseline estimate of average years of capital elasticities, α, are based on the industry-specificratios schooling for the working-age population in each region, of non-labour income to output taken from the WIOD. base ays , can be calculated by multiplying the population Series A and A use the two alternative physical capital 3 4 it REGIONAL STUDIES 466 Sjoerd Beugelsdijk et al. Table 1. Summary statistics, total factor productivity (TFP) estimates. Sample: 257 European Union regions (NUTS-2) Observations Mean SD Minimum Maximum Correlation with A1 A1, Baseline TFP estimate 257 0.998 0.200 0.461 2.517 A2, Alternative TFP estimate with varying α 257 0.999 0.187 0.506 2.416 0.999 A3, Alternative TFP estimate with k 257 0.993 0.206 0.452 2.572 0.977 A4, Alternative TFP estimate with k 257 0.983 0.199 0.454 2.512 0.980 low A5, Alternative TFP estimate with h 257 1.000 0.204 0.466 2.573 0.996 high A6, Alternative TFP estimate with h 257 1.000 0.202 0.468 2.548 0.996 stocks series respectively in combination with the baseline other. The reported standard deviations (SD) of around 0.2 base human capital estimate, h ,and α ¼ 1/3. Series A and also reveal a high degree of dispersion in TFP. it A are also based on a fixed value for α, but employ the To visualize the differences in TFP across EU regions, two alternative human capital stocks series in combination Figure 1 maps the distribution of the baseline TFP scores. with our baseline estimate for physical capital, k . It shows high TFP values for core Western European it Basic summary statistics for all six TFP estimates are regions, particularly those along the London–Amsterdam– reported in Table 1. To facilitate interpretation of the Munich–Milan corridor, with Inner London recording the TFP scores, we report values relative to the EU average. highest value. Low TFP values are observed in most per- As Table 1, indicates the estimates in all six cases are simi- ipheral Eastern European regions, with regions in Bulgaria lar in terms of magnitudes and highly correlated with each and Romania dominating the bottom of the distribution. Figure 1. Baseline total factor productivity (TFP) levels (darker colours indicate higher TFP). REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 467 function and allowing for region-specific values of α do we obtain a higher ratio of about 29%. This implies that the observed variation in factor inputs explain less than one-quarter of the observed variation in output per worker at the regional level. This leaves the remaining share of the variation to be attributed to TFP differences and the covariance between TFP and factor inputs. The second row reports the variation in output per worker explained by factor inputs across countries. This ratio is found to be on average about 2 percentage points higher than the overall ratio. Production factors explain a bit more of the observed output differences across countries and the resulting shares of around 0.25 resonate well with the estimate of Caselli (2005). This similarity of our devel- Figure 2. Total factor productivity (TFP) dispersion between opment accounting results across regions and countries give and within countries. us confidence in our TFP estimates for European regions. kh The next six rows report the values for V for the five The map also reveals substantial variation in TFP within largest EU economies, Britain, France, Germany, Italy and countries. We explore the sources of this regional variation Spain, as well as the average of the within-country ratios in TFP in more detail in the next section. across all 21 EU countries in our sample. On average, Figure 2 visualizes the variation in TFP within each within countries factor inputs explain about 20% of the country in a dispersion diagram. As shown there, the degree variation in output per worker. Yet, for France and Spain of dispersion in TFP differs substantially across countries. we find this ratio to be substantially higher, while for Brit- While in some EU countries, such as Britain and Germany, ain and Germany it is lower. This pattern mirrors the dis- there is substantial interregional variation in TFP, in other persion in TFP within countries presented in Figure 2. large countries, such as France and Spain, the distribution All the above results are similar when we perform the of TFP across regions is relatively condensed. Looking analysis for years other than 2007 or when we exclude at Eastern European countries where TFP is on average specific regions from the sample or merge functional low, we find in many of them sizeable dispersion in TFP, regions following Annoni and Dijkstra (2013). with the capital-city regions outperforming the rest. In summary, the variance decomposition analysis indi- To assess more carefully the relative importance of TFP cates that both within and between countries differences in in accounting for regional differences in output per worker, output per worker are less the result of the local availability kh in Table 2 we calculate, using equation (3), V , the share of production factors and more a consequence of the effec- of the variance in output per worker that can be explained tiveness with which these factors are combined in the pro- solely by the variation in factor inputs. In this context, a duction process. Given this importance of TFP for kh lower value for V implies a larger role for TFP. Global understanding regional differences in the level of economic country-level development-accounting studies typically development, in the following we explore which factors can kh find values for V around 40% (Hsieh & Klenow, 2010), explain the regional variation in TFP. but these values have been shown to be lower for European countries and closer to 25% (Caselli, 2005). kh Table 2 reports the V for different groups of regions CORRELATES OF REGIONAL TFP and based on all six TFP estimates. The first row reports DIFFERENCES that across all 257 regions the variation in output per worker explained by factor inputs is in most cases about To understand better the sources of the large TFP differ- 23%. Only when employing the translog production ences across EU regions, we relate regional TFP levels to Table 2. Development accounting results. kh V for different subsamples and total factor productivity (TFP) estimates (%) TFP estimate A1 A2 A3 A4 A5 A6 257 EU regions (NUTS-2) 23.11 29.02 22.11 23.18 23.26 23.65 21 EU countries 25.01 32.43 24.28 25.86 25.42 25.83 37 British regions (NUTS-2) 15.48 19.43 11.52 11.52 13.73 14.38 27 French regions (NUTS-2) 37.09 44.42 26.69 26.63 27.85 30.14 38 German regions (NUTS-2) 13.30 15.35 11.10 11.96 11.94 12.63 21 Italian regions (NUTS-2) 19.27 24.38 16.48 17.00 16.01 15.08 19 Spanish regions (NUTS-2) 47.69 53.12 37.73 38.25 30.83 32.90 Within-country average 19.63 22.80 15.77 16.38 15.71 16.65 REGIONAL STUDIES 468 Sjoerd Beugelsdijk et al. a set of variables that have been emphasized in the literature we consider the share of workers employed in science as being important factors influencing regional economic and technology, emphasized by Anselin et al. (1997), development. This set of variables is not meant to be and the social filters measure proposed by Rodríguez- exhaustive, as the list of relevant regional development Pose (1999), which reflects the innovative and learning determinants can be potentially quite long. Instead, we capacity of each region. focus on variables reflecting different potential sources of Economic structure: since TFP levels naturally vary across TFP differences in order to provide a comparative assess- sectors, our analysis accounts for the economic structure ment of their importance for EU regions. of each region. Specifically, we consider the share of To assess the strength of the relationship between TFP labour employed in agriculture as productivity in the and these variables, we estimate the following cross-sec- agricultural sector tends to be lower than in the rest of tional regression: the economy (Restuccia, Yang, & Zhu, 2008). We also consider the amount of oil production and reserves A = a + X b + u + 1 , ic ic c ic in each region as the presence of a large oil and gas sector may lead to overestimation of TFP as the extraction of where the dependent variable is our measure of TFP, as cal- natural resources typically involves relatively little pro- culated in the previous section, for region i in country c rela- duction inputs but generates high value added (Gunton, tive to the EU average. To control for country-specific 2003). To capture general productivity-enhancing characteristics influencing TFP, we include a set of country activities, we include the number of patents filed per dummies, denoted by u . All regressions are based on TFP worker and the share of regional research and develop- figures and explanatory variables measured in 2007 (pro- ment (R&D) spending in regional GDP, both of vided they are time varying). This is motivated by the which should be sources of positive spillover effects aim of understanding the relationship between TFP and (Audretsch & Feldman, 1996; Jaffe et al., 1993). other regional characteristics in a long-run equilibrium, Culture: one important mechanism through which cul- which arguably was disrupted by the post-2007 financial ture may affect regional TFP is social capital. Social crisis. TFP data for 2007 are available for 257 regions in capital is typically measured by the level of generalized 21 countries, but due to missing observations on some of trust. As generalized trust has been shown to have a the explanatory variables, X , most regressions include ic positive association with regional economic develop- 251 observations. ment (Beugelsdijk & van Schaik, 2005; Tabellini, 2010), our analysis employs the level of trust in each Explanatory variables region, measured by data from the European Values We consider variables related to physical and economic Study (EVS). In addition to social capital, the analysis geography, culture, institutions, history, and other also considers the degree of ethnic heterogeneity by structural characteristics of each region. When selecting including an ethnic fractionalization index, as in Gen- these variables, we focus on measures that vary at the naioli et al. (2013). This is motivated by the fact that regional level and for which data are widely available, higher diversity is generally associated with lower levels which imposes limits on the variables selection. Below of economic development (Alesina & Zhuravskaya, we briefly describe the main variables employed in the 2011; Beugelsdijk & Klasing, 2017; Beugelsdijk, analysis. Measurement details of all variables are provided Klasing & Milionis, 2017). in the Data Appendix below. Institutions: in light of the documented regional differ- ences in the quality of institutions (Charron, Dijkstra, Physical geography: to capture the physical geography of & Lapuente, 2015; Rodríguez-Pose & Garcilazo, each region we consider three key characteristics: its 2015), we construct a measure of the quality of govern- latitude, its access to the sea and its access to navigable ance at the regional level. We follow Becker, Egger, and rivers. These characteristics have been shown to be von Ehrlich (2013) and use Eurobarometer survey data important for long-run economic development (Bosker capturing respondents’ satisfaction with local democracy & Buringh, 2017; Bosker, Buringh, & van Zanden, and their trust in the local judicial system. This measure 2013; Gallup, Sachs, & Mellinger, 1999). is by construction highly correlated with the regional Economic geography: to capture the economic geography quality of governance index assembled by the European of each region, we consider its population density, its Commission (Charron et al., 2015), but covers a larger rate of urbanization and its market potential measured number of regions. Furthermore, we consider whether by the level of gross domestic product (GDP) in the a region was part of the Communist Bloc to capture nearby regions, all three of which are sources of positive the heritage of Communism in regions of Eastern agglomeration effects (Brakman, Garretsen, & van Europe and parts of present-day Germany. Marrewijk, 2009; Ciccone, 2002; Redding & Venables, History: development outcomes have been shown to be 2004). We also consider the average distance of each persistent. Today’s centres of economic activity may be region to the country’s economic centre to measure the in specific locations not because of the current optimality importance of spillover effects operating from centre to of these locations but because of historical path depen- periphery (Rice, Venables, & Patacchini, 2006). Fur- dence (Akcomak, Webbink, & ter Weel, 2016; Bleakley thermore, to capture knowledge-related externalities, & Lin, 2012; Davis & Weinstein, 2002). To account for REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 469 the legacy of history on current TFP differences, we significance level lower than the conventional threshold consider each region’s historical urban density in 1800 of 0.1; and in column 6 again variables that subsequently based on data from Bairoch, Batou, and Chevre fall below that threshold. (1988). We also consider for each region how many The resulting specification of column 6 highlights the cities were historically located on the crossing of two main variables that are closely associated with regional vari- or more Roman roads, which Bosker et al. (2013) ation in TFP. Specifically, we find that TFP levels are higher have shown to be correlated with historical development in regions that are closer to large markets, have a young and over the past two millennia. well-educated workforce, are more trusting, and have also historically been more urbanized On the other hand, TFP Table 3 shows the descriptive statistics for all these is lower in regions with a Communist history and a relatively explanatory variables, as well as their correlations with our large share of the agricultural sector. These seven variables baseline TFP estimate. Several variables exhibit a strong together with the country dummies explain 75% of the over- positive correlation with TFP. The regression analysis all variation in regional TFP levels and 72% of the variation below assesses more carefully the relative importance of in TFP levels within EU countries. these variables in explaining TFP differences across regions. To assess the relative importance of our main explana- tory variables, Table 5 reports the implied effect sizes in terms of a 1 SD change in the explanatory variables, with REGRESSION RESULTS the variables ordered by their quantitative importance. Quantitatively most important is the post-Communist Table 4 shows the main regression results relating regional dummy, with TFP being on average 22 percentage points TFP levels, expressed relative to the EU average, to the lower in a region that was part of the former Communist aforementioned explanatory variables. Column 1 reports Bloc. This is followed by historical urban density with an the estimation results of a cross-sectional regression speci- effect size on TFP relative to the EU average of 7 percen- fication including all explanatory variables. Column 2 adds tage points for a 1 SD change. This is much larger than the country dummies to the specification. In columns 3–6we effect of the contemporary urbanization rate whose stan- follow a standard general-to-specific approach and itera- dardized effect is only 1.3 percentage points. Next in line tively eliminate from the specification variables based on are the social filters and the agricultural labour share their significance levels. In column 3 we drop variables whose implied magnitudes are slightly above and slightly with a significance level lower than 0.5; and in column 4 below 5 percentage points respectively. A 1 SD increase variables that subsequently fall in this category. Then in in market potential is associated with an increase in relative column 5 we proceed to eliminate variables with a Table 3. Summary statistics, regressors. Sample: 257 European Union regions (NUTS-2) Correlation Observations Mean SD Minimum Maximum with A1 A1, Baseline total factor productivity (TFP) estimate 251 1.000 0.201 0.444 2.357 Latitude 251 48.517 5.687 28.353 66.439 0.254 River Access 251 1.422 1.832 0.000 14.000 0.126 Sea Border 251 0.470 0.500 0.000 1.000 0.177 Population Density 251 0.353 0.869 0.003 9.244 0.479 Urbanization Rate 251 0.356 0.283 0.000 1.585 0.291 Workers in Science & Technology 251 26.680 6.750 12.000 51.600 0.529 Market Potential 251 0.217 0.252 0.003 1.784 0.598 Distance to Economic Center 251 0.224 0.196 0.000 1.739 –0.141 Agr Labor Share 251 0.064 0.080 0.000 0.507 –0.624 Oil Production 251 0.017 0.055 0.000 0.548 –0.389 R&D Spending 249 0.014 0.012 0.001 0.067 0.438 Patents per Worker 250 0.109 0.131 0.000 0.672 0.456 Social Filters 251 0.141 1.521 –3.444 4.321 0.554 Ethnic Diversity 251 0.625 0.541 0.000 1.946 –0.343 Trust 251 0.343 0.157 0.037 0.781 0.415 Institutional Quality 251 0.298 0.576 –1.200 1.713 0.496 Post Communist 251 0.235 0.425 0.000 1.000 –0.688 Urban Density 1800 251 0.025 0.192 0.000 2.946 0.462 Roman Roads Hubs 251 0.610 1.308 0.000 9.000 0.066 REGIONAL STUDIES 470 Sjoerd Beugelsdijk et al. Table 4. Stepwise regression results. Dependent variable: A1, Baseline total factor productivity (TFP) estimate (1) (2) (3) (4) (5) (6) Latitude 0.003** 0.001 [0.001] [0.002] River Access 0.007** 0.002 [0.003] [0.004] Sea Border 0.000 0.006 [0.015] [0.013] Population Density –0.012 –0.010 –0.011 –0.012 [0.015] [0.012] [0.012] [0.012] Urbanization Rate 0.022 0.040* 0.045** 0.052* 0.048* 0.047* [0.024] [0.021] [0.020] [0.026] [0.025] [0.026] Workers in Science & Technology 0.005*** 0.002 0.002 [0.002] [0.002] [0.002] Market Potential 0.141*** 0.070** 0.070* 0.074** 0.079** 0.079** [0.038] [0.032] [0.035] [0.034] [0.028] [0.029] Distance to Economic Center 0.012 –0.023 –0.027 –0.028 [0.030] [0.025] [0.021] [0.021] Agr Labor Share –0.335** –0.544** –0.550** –0.574** –0.606*** –0.620*** [0.143] [0.251] [0.246] [0.219] [0.193] [0.189] Oil Production –0.171 –0.175 –0.182 –0.190 [0.162] [0.133] [0.129] [0.135] R&D Spending 0.722 0.751 0.943 1.057 [0.681] [0.822] [0.714] [0.684] Patents per Worker 0.011 0.045 [0.070] [0.053] Social Filters –0.009 0.021* 0.023* 0.028*** 0.038*** 0.035*** [0.006] [0.012] [0.013] [0.008] [0.006] [0.005] Ethnic Diversity –0.008 –0.005 [0.010] [0.011] Trust 0.024 0.048* 0.051** 0.051** 0.046** 0.042* [0.049] [0.024] [0.022] [0.022] [0.021] [0.020] Institutional Quality –0.014 –0.033 –0.034* –0.036* –0.037 [0.016] [0.021] [0.020] [0.020] [0.023] Post Communist –0.265*** –0.234*** –0.241*** –0.246*** –0.252*** –0.222*** [0.024] [0.021] [0.019] [0.018] [0.022] [0.006] Urban Density 1800 0.374*** 0.396*** 0.402*** 0.405*** 0.359*** 0.356*** [0.045] [0.032] [0.032] [0.033] [0.025] [0.025] Roman Roads Hubs –0.010** 0.000 [0.004] [0.004] Constant 0.733*** 0.933*** 0.998*** 1.038*** 1.044*** 1.030*** [0.089] [0.125] [0.078] [0.024] [0.024] [0.023] Countries 21 21 21 21 21 21 Observations 248 248 249 249 251 251 Country dummies No Yes Yes Yes Yes Yes Overall adjusted R 0.821 0.778 0.774 0.756 0.738 0.747 Within adjusted R – 0.720 0.725 0.726 0.718 0.715 Notes: Estimation with ordinary least squares (OLS). Robust standard errors clustered at the country level are shown in brackets. ***p < 0.01, **p < 0.05, *p < 0.1. REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 471 Table 5. Magnitudes. development paths and trust play a key role in explaining why some regions have higher TFP levels than others. Based on regression specification of Table 4, column (6) The finding that TFP differences are important in Change in total explaining the development gaps across European regions factor productivity and that these differences are related to economic geogra- Variables Coefficient SD (TFP) phy are broadly supportive of the extensive literature on Post –0.222 0.425 –9.42% New Economic Geography. The persistent nature of Communist these gaps, even within the same country, and the associ- Urban Density 0.356 0.192 6.84% ation with historical development and culture suggest that there is a strong local dimension to technology and Social Filters 0.035 1.521 5.26% knowledge that needs to be better understood. This has important implications for regional development policy, Agr Labor –0.620 0.080 –4.96% which should be designed primarily with the aim to support Share regions (1) in building their comparative advantages in Market 0.079 0.252 2.00% terms of technology and knowledge, (2) in specializing Potential smartly to exploit economies of scale and (3) in building Urbanization 0.047 0.283 1.33% on existing synergies. These conclusions are very much in Rate line with the current discussion on smart specialization Trust 0.042 0.157 0.66% and place-based development strategies within the EU (Barca, 2009; McCann & Ortega-Argilés, 2015). They underscore the need for EU policies to take into account TFP of 2 percentage points. Finally, trust has with 0.66 available knowledge in each region and linkages across percentage points the smallest effect. regions in order to help regions achieve their long-run In Section C in the supplemental material online we development potential. assess the robustness of our regression results along the fol- The orientation of regional development policy along lowing lines. First, we show that the results hold also for these lines, of course, may not be easy. Yet, our approach the five alternative TFP figures. Second, we document of calculating, documenting and analysing TFP at the that they are robust to changes in the sample composition regional level could provide a useful tool for policy and pro- and other corrections for heterogeneity across regions not vide interesting avenues for future research. The analysis captured in the main analysis, such as accounting for could be further extended to the sectoral level and could city-region effects and including spatial lags. Third, we also be used to make comparisons over time. Future show that the results also hold when employing alternatives research could also compare TFP levels with more disag- measures of institutional quality and the innovative capacity gregate regional characteristics such as regional diversity of a region, but that the positive results for trust do not (Frenken, van Oort, & Verburg, 2007), spatial diversifica- extend to alternative proxies of social capital (Beugelsdijk tion patterns (Neffke, Henning, & Boschma, 2011), and & Smulders, 2003; Knack & Keefer, 1997). workforce mobility patterns and information on their spatial networks (Huber, 2012). Moreover, one could further explore how the spatial dimension of technology CONCLUSIONS diffusion may differ depending on the innovation being embodied (i.e., new products and or services) or disembo- Differences in the level of economic development within died (i.e., superior measurement practices), and how the EU countries are large and persistent. The aim of this rents extracted from these types of innovation may have paper is to shed more light on why this is the case by cal- different spatial implications (Rodríguez-Pose & culating, documenting and analysing TFP levels for 257 Crescenzi, 2008; Keller, 2004). Although disembodied EU regions. To that end, we conduct, to our knowledge, innovation has long been recognized in the management the first development-accounting exercise at the subna- literature (e.g., Beugelsdijk, 2008), its significance for tional level to decompose regional differences in output TFP has only recently been acknowledged in the economic per worker into a component reflecting the local availability development literature (Bloom & van Reenen, 2007). Such of factor inputs and a component capturing differences in studies could extend our analysis of regional TFP differ- TFP. This exercise reveals that about 75% of the differ- ences by exploring the microfoundations and underlying ences in regional economic development can be attributed mechanisms behind the broad patterns uncovered in this to differences in TFP. This is similar between and within paper. countries, suggesting that the spatial diffusion of technol- ogy and efficient production practices is limited and that ACKNOWLEDGEMENTS the limits extend beyond national borders. TFP levels tend to be highest along the London–Amsterdam– This paper benefited from useful suggestions made by three Munich–Milan axis and lowest in peripheral regions in anonymous referees as well as Steven Brakman, Marta Eastern Europe. We furthermore document that regional Curto-Grau, Lewis Dijkstra, Robert Inklaar and Ton van differences in terms of economic geography, historical Schaik. The authors also thank seminar participants at REGIONAL STUDIES 472 Sjoerd Beugelsdijk et al. the universities of Basel, Groningen, Louvain and well as eight overseas territories of France, Portugal and St. Gallen, as well as conference participants at the 2014 Finland due to limited data availability. U4 Globalization Workshop, the 2015 SMYE (Spring 7. The information provided by EUROSTAT does not Meeting of Young Economists), the 2015 EPCS (Euro- allow us to correct for price differences within countries. Yet, pean Public Choice Society) and the 2015 World Congress as noted by Acemoglu and Dell (2010) and Gennaioli et al. of Comparative Economics for helpful comments. (2013), this should not have a major impact on the analysis. 8. The average regional depreciation rate is 6.3%, which is very close to the typical value of 6% employed in most DISCLOSURE STATEMENT development accounting studies (Caselli, 2005). 9. This corridor is also referred to as the ‘Blue Banana’,with No potential conflict of interest was reported by the the ‘banana’ describing its shape and blue alluding to the EU authors. flag. The term was coined by geographer Roger Brunet. 10. As is evident from Figure 2, there is a big outlier in FUNDING the TFP distribution which is the Inner London area. None of our results, however, is affected by this outlier observation, Sjoerd Beugelsdijk acknowledges financial support from as we discuss in greater detail in the robustness analysis. the Nederlandse Organisatie voor Wetenschappelijk 11. Allowing for region-specific α increases the ability of Onderzoek (NWO) [grant number 054-11-010]. factor inputs to explain the variation in output – both across and within countries – and reduces the relative importance SUPPLEMENTAL DATA of TFP differences. Nevertheless, this does not alter our main conclusions regarding the relative explanatory power Supplemental material for this article can be accessed of factor inputs versus TFP between and within countries. https://dx.doi.org/10.1080/00343404.2017.1334118 12. This is because for some arguably relevant factors we were either unable to find comprehensive data or the avail- NOTES able data only displayed variation at the country level. 13. In Section C in the supplemental material online we 1. In fact, 35% of the EU’s total budget – corresponding also explore the role played by regional research and inno- to €347 billion – was allocated during the 2007–13 budget vation networks. Yet, as the available data only cover a period in the form of development-promoting Structural smaller set of regions, we do not consider this variable in Funds to less developed regions. our main analysis. 2. Both the Lisbon Agenda as well as the Europe 2020 14. Following the standard in the literature, this is calcu- strategy goals of making Europe and its regions the most lated as the share of the regional population indicating that competitive world economy stress the importance of build- ‘most people can be trusted’ (as opposed to ‘you can’t be too ing knowledge infrastructures, enhancing innovation and careful when dealing with people’) averaged across all sur- promoting economic reform (European Commission, vey waves (1984–2008). 2010; European Council, 2000). 15. Section C in the supplemental material online also 3. This decomposition, for example, has been instrumen- reports results using alternative institutional quality measures. tal for the analysis of the information and communication 16. An alternative approach here would be to estimate technology (ICT) revolution (Jorgenson & Stiroh, 2000; repeatedly our regression specification eliminating in each Oliner & Sichel, 2000), the rapid growth of East Asian round the variable with the highest p-value until all insig- economies (Hsieh, 2002; Young, 1995), and the pro- nificant variables have been removed from the specification. ductivity gap between Europe and the United States (van Following this more cumbersome approach leads to exactly Ark, O’Mahony, & Timmer, 2008). the same specification as that of column (6). 4. NUTS ¼ Nomenclature des Unités Territoriales 17. The dummy variable indicating post-Communist Statistiques. regions is highly significant and negatively related to pro- 5. For each EU country, there is a hierarchical system of ductivity differences, even after the inclusion of country regional subdivision that proceeds from coarser to finer sub- dummies. The inclusion of country dummies implies that national NUTS units. In this system, NUTS-0 refers to the the identification of this variable comes from the variation country as a whole, NUTS-1 refers to the coarsest level of within Germany, with regions that were part of the former subnational division, NUTS-2 to an intermediate level and German Democratic Republic (GDR) being significantly NUTS-3 to the finest level. This system is designed such less productive than West German regions. As regional that the resulting regions at each level of aggregation are institutional quality itself does not appear to be a significant comparable in terms of population size. predictor of within-country productivity differences, this 6. Specifically, we exclude the six smallest EU countries implies that the post-Communist dummy variable is not (Cyprus, Estonia, Latvia, Lithuania, Luxembourg and picking up effects related to the current quality of insti- Malta), which due to their size do not have a subnational tutions in these regions, but instead captures the more fun- division at the NUTS-2 level. It also excludes Croatia as damental and long-lasting impacts of Communism. REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 473 APPENDIX: VARIABLE DESCRIPTIONS AND SOURCES Variable Description Source Gross Value Added Gross value added (GVA) in all sectors converted into 2005 EUROSTAT (nama_10r_3gva) purchasing power standard (PPS) (European System of Accounts (ESA) 2010). Employment Employment in all sectors EUROSTAT (nama_10r_3empers) Investment Gross fixed capital formation converted into 2005 euros (ESA EUROSTAT (nama_10r_2gfcf) 2010 system of accounts) Primary and Lower Share of the population aged 25–64 years with a lower EUROSTAT (edat_lfse_04) Secondary Education secondary, primary and pre-primary education (International Standard Classification of Education (ISCED) levels 0–2) Upper Secondary Share of the population aged 25–64 years with an upper- EUROSTAT (edat_lfse_04) Education secondary education (ISCED levels 3–4) Tertiary Education Share of the population aged 25–64 years with a tertiary EUROSTAT (edat_lfse_04) education (ISCED levels 5–8) Latitude Degrees of latitude of the region’s centroid EUROSTAT Geodata River Access Number of cities in a region located by a river or a navigable Bosker et al. (2013) waterway Sea Border Dummy variable for regions located on the sea. Hamburg and Authors’ own coding London are coded as 1 due to their almost direct sea access and the importance of maritime trade in these cities Population Density Population per area (km ) EUROSTAT (nama_r_e3popgdp; demo_r_d3area) Urbanization Rate Share of each region’s population living in cities EUROSTAT (ubr_cpop1; nama_r_e3popgdp) Workers in Science & Scientists and engineers as a percentage of the active EUROSTAT (hrst_st_rcat) Technology population Market Potential Aggregate level of gross domestic product (GDP) within a 100- European Commission DG Regio kilometre circle around the region Distance to Economic Areal distance between each region’s largest city and the Authors’ own coding using a Center economic centre of the country distance calculator Agr Labor Share Number of persons employed in agriculture as a share of total EUROSTAT (nama_10r_3empers) regional employment Oil Production Oil production and reserves in logs Gennaioli et al. (2013) R&D Spending Share of total regional research and development (R&D) EUROSTAT (rd_e_gerdreg; spending in regional GDP nama_10r_2gdp) Patents per Worker Patent applications per million of the active population EUROSTAT (pat_ep_rtot) Young Share of the population aged 15–24 years in the total regional EUROSTAT (demo_r_d2jan) population Training Percentage of the regional population that has participated in EUROSTAT (trng_lfse_04) education and training in the past four weeks Long-Term Long-term unemployment (12 months and more) as a EUROSTAT (lfst_r_lfu2ltu) Unemployment percentage of unemployment Social Filters First principal component of young, training, long-term Following Rodríguez-Pose and unemployment and tertiary education Crescenzi (2008) (Continued) REGIONAL STUDIES 474 Sjoerd Beugelsdijk et al. Continued. Variable Description Source Ethnic Diversity Number of ethnic groups per region Weidman, Rod, and Cederman (2010) Trust Share of the population saying ‘most people can be trusted’ as European Values Study (EVS) opposed to ‘you can’t be too careful when dealing with people’, averaged across all European Values Study (EVS) waves Institutional Quality Regional quality of governance predicted from regression of Charron et al. (2015); the regional quality of governance measure by Charron et al. Eurobarometer (2015) on regional values of Satisfaction with democracy and Trust in the justice system from Eurobarometer Post-Communism Dummy variable equal to 1 for Eastern European countries and Authors’ own coding the regions of Germany that belonged to the former German Democratic Republic (GDR) Urban Density 1800 Number of people living in cities with a population above Bairoch et al. (1988); EUROSTAT 10,000 in 1800 relative to area (km ) (demo_r_d3area) Roman Road Hub Number of cities in a region located at a meeting point of two Bosker et al. (2013) or more Roman roads Bonding Social Capital First principal component of importance of both family and European Values Study friends averaged at the region of residence Bridging Social Capital Number of organizations an individual belongs to out of the European Values Study following list, averaged at the region of residence: religious organizations, cultural activities organization, youth work organizations, sports/recreation organizations and women’s groups Trust in National Share of the regional population trusting the national Eurobarometer 70.1 Government government Trust in Regional Share of the regional population trusting the regional or local Eurobarometer 70.1 Government public authorities SMEs Innovating in House Share of small and medium-sized enterprises (SMEs) with in- Regional Innovation Scoreboard, house innovation activities European Commission SMEs Innovating Share of SMEs that collaborate in innovation activities with Regional Innovation Scoreboard, Collaboratively other enterprises and institutions European Commission Audretsch, D., & Feldman, M. P. (1996). R&D spillovers and the REFERENCES geography of innovation and production. American Economic Review, 86(3), 630–640. Acemoglu, D., & Dell, M. (2010). Productivity differences between Bairoch, P., Batou, J., & Chevre, P. (1988). The population of and within countries. American Economic Journal: Macroeconomics, European cities, 800–1850. Geneva: Droz. 2(1), 169–188. doi:10.1257/mac.2.1.169 Barca, F. (2009). An agenda for reformed cohesion policy, a place based Akcomak, I. S., Webbink, D., & ter Weel, B. (2016). Why did the approach to meeting European Union challenges and Netherlands develop so early? The legacy of the brethren of the expectations (Independent report prepared at the request of the common life. Economic Journal, 126(593), 821–860. doi:10. European Commissioner for Regional Policy). Brussels: 1111/ecoj.12193 European Commission. Alesina, A., & Zhuravskaya, E. (2011). Segregation and the quality of Barro, R. J., & Lee, J. W. (2013). A new data set of educational government in a cross section of countries. American Economic attainment in the world, 1950–2010. Journal of Development Review, 101(5), 1872–1911. doi:10.1257/aer.101.5.1872 Economics, 104, 184–198. doi:10.1016/j.jdeveco.2012.10.001 Annoni, P., & Dijkstra, L. (2013). EU regional competitiveness Barro, R. J., & Sala-i-Martin, X. (1991). Convergence across states index (Discussion Paper). European Commission, Joint and regions. Brookings Papers on Economic Activity, 1991(1), Research Centre. 107–182. doi:10.2307/2534639 Anselin, L., Varga, A., & Acs, Z. (1997). Local geographic spillovers Becker, S. O., Egger, P. H., & von Ehrlich, M. (2013). Absorptive between university research and high technology innovations. capacity and the growth and investment effects of regional trans- Journal of Urban Economics, 42(3), 422–448. doi:10.1006/juec. fers: A regression discontinuity design with heterogeneous 1997.2032 REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 475 treatment effects. American Economic Journal: Economic Policy, 5 Regional Studies, 46(10), 1401–1416. doi:10.1080/00343404. (4), 29–77. doi:10.1257/pol.5.4.29 2010.529288 Bernanke, B. S., & Gürkaynak, R. S. (2002). Is growth exogenous? Esteban, J. (2000). Regional convergence in Europe and the industry Taking Mankiw, Romer, and Weil seriously. In B. S. Bernanke mix: A shift–share analysis. Regional Science and Urban Economics, & K. Rogoff (Eds.), NBER macroeconomics annual (Vol. 16, pp. 30(3), 353–364. doi:10.1016/S0166-0462(00)00035-1 11–57). Cambridge, MA: MIT Press. European Commission. (2010). Europe 2020. A strategy for smart, Beugelsdijk, S. (2008). Strategic human resource practices and pro- suitable and inclusive growth. Brussels: European Commission. duct innovation. Organization Studies, 29(6), 821–847. doi:10. European Council. (2000). Presidency conclusions Lisbon European 1177/0170840608090530 Council. 23–24 March 2000. Brussels: European Council. Beugelsdijk, S., & Klasing, M. J. (2017). Measuring value diversity Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015). The next gen- within countries. In J. Costa-Font & M. Macis (Eds.), Social eration of the Penn World Table. American Economic Review, economics: Current and emerging avenues (pp. 129–172). 105(10), 3150–3182. doi:10.1257/aer.20130954 Cambridge, MA: MIT Press. Fischer, M. M., & Stirboeck, C. (2006). Pan-European regional Beugelsdijk, S., Klasing, M. J., & Milionis, P. (2017, in press). Value income growth and club-convergence. Annals of Regional diversity and regional economic development. Scandinavian Science, 40(4), 693–721. doi:10.1007/s00168-005-0042-6 Journal of Economics. doi:10.1111/sjoe.12253 Frenken, K., van Oort, F., & Verburg, T. (2007). Related variety, Beugelsdijk, S., & van Schaik, T. (2005). Social capital and growth in unrelated variety and regional economic growth. Regional European regions: An empirical test. European Journal of Political Studies, 41(5), 685–697. doi:10.1080/00343400601120296 Economy, 21(2), 301–324. doi:10.1016/j.ejpoleco.2004.07.004 Gallup, J. L., Sachs, J. D., & Mellinger, A. D. (1999). Geography and Beugelsdijk, S., & Smulders, S. (2003). Bonding and bridging social economic development. International Regional Science Review, capital: Which type is good for economic growth? In W. Arts, L. 22(2), 179–232. doi:10.1177/016001799761012334 Halman, & J. Hagenaars (Eds.), The cultural diversity of European Gardiner, B., Martin, R., & Tyler, P. (2004). Competitiveness, pro- unity (pp. 147–184). Leiden: Brill. ductivity and economic growth across the European regions. Bleakley, H., & Lin, J. (2012). Portage and path dependence. Regional Studies, 38(9), 1045–1067. doi:10.1080/ Quarterly Journal of Economics, 127(2), 587–644. doi:10.1093/ 0034340042000292638 qje/qjs011 Garofalo, G. A., & Yamarik, S. (2002). Regional convergence: Bloom, N., & van Reenen, J. (2007). Measuring and explaining manage- Evidence from a new state-by-state capital stock series. Review ment practices across firms and countries. Quarterly Journal of of Economics and Statistics, 84(2), 316–323. Economics, 122(4), 1351–1408. doi:10.1162/qjec.2007.122.4.1351 Gennaioli, N., La Porta, R., Lopez-de Silanes, F., & Shleifer, A. Bosker, M. (2009). The spatial evolution of regional GDP disparities (2013). Human capital and regional development. Quarterly in the ‘old’ and the ‘new’ Europe. Papers in Regional Science, 88(1), Journal of Economics, 128(1), 105–164. doi:10.1093/qje/qjs050 3–27. doi:10.1111/j.1435-5957.2008.00183.x Geppert, K., & Stephan, A. (2008). Regional disparities in the European Bosker, M., & Buringh, E. (2017, in press). City seeds: Geography Union: Convergence and agglomeration. Papers in Regional Science, and the origins of the European city system. Journal of Urban 87(2), 193–217. doi:10.1111/j.1435-5957.2007.00161.x Economics. Gollin, D., Parente, S., & Rogerson, R. (2002). The role of agricul- Bosker, M., Buringh, E., & van Zanden, J. L. (2013). From Baghdad ture in development. American Economic Review Papers and to London: Unraveling urban development in Europe, the Proceedings, 92(2), 160–164. Middle East, and North Africa, 800–1800. Review of Economics Gunton, T. (2003). Natural resources and regional development: An and Statistics, 95(4), 1418–1437. doi:10.1162/REST_a_00284 assessment of dependency and comparative advantage paradigms. Brakman, S., Garretsen, H., & van Marrewijk, C. (2009). Economic Economic Geography, 79(1), 67–94. doi:10.1111/j.1944-8287. geography within and between European nations: The role of mar- 2003.tb00202.x ket potential and density across space and time. Journal of Regional Hall, R. E., & Jones, C. I. (1999). Why do some countries produce so Science, 49(4), 777–800. doi:10.1111/j.1467-9787.2009.00633.x much more output per worker than others? Quarterly Journal of Capello, R., & Lenzi, C. (2015). Knowledge, innovation and pro- Economics, 114(1), 83–116. doi:10.1162/003355399555954 ductivity gains across European regions. Regional Studies, Henderson, J. V., Kuncoro, A., & Turner, M. (1995). Industrial 49(11), 1788–1804. doi:10.1080/00343404.2014.917167 development in cities. Journal of Political Economy, 103(5), Caselli, F. (2005). Accounting for cross-country income differences. 1067–1090. doi:10.1086/262013 In P. Aghion & S. N. Durlauf (Eds.), Handbook of economic Hsieh, C.-T. (2002). What explains the industrial revolution in East growth (vol. 1A, pp. 679–741). Amsterdam: Elsevier. Asia? Evidence from the factor markets. American Economic Charron, N., Dijkstra, L., & Lapuente, V. (2015). Mapping the Review, 92(3), 502–526. doi:10.1257/00028280260136372 regional divide in Europe: A measure for assessing quality of gov- Hsieh, C.-T., & Klenow, P. J. (2010). Development accounting. ernment in 206 European regions. Social Indicators Research, American Economic Journal: Macroeconomics, 2(1), 207–223. 122(2), 315–346. doi:10.1007/s11205-014-0702-y doi:10.1257/mac.2.1.207 Ciccone, A. (2002). Agglomeration effects in Europe. European Huber, F. (2012). On the role and interrelationship of spatial, social Economic Review, 46(2), 213–227. doi:10.1016/S0014-2921 and cognitive proximity: Personal knowledge relationships of (00)00099-4 R&D workers in the Cambridge information technology cluster. Ciccone, A., & Hall, R. E. (1996). Productivity and the density of Regional Studies, 46(9), 1169–1182. doi:10.1080/00343404. economic activity. American Economic Review, 86(1), 54–70. 2011.569539 Corrado, L., Martin, R., & Weeks, M. (2005). Identifying and interpret- Inklaar, R., & Timmer, M. P. (2013). Capital, labor and TFP in ing regional convergence clusters across Europe. Economic Journal, PWT8.0 (Mimeo). Groningen: University of Groningen. 115(502), C133–C160. doi:10.1111/j.0013-0133.2005.00984.x Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic Davis, D. R., & Weinstein, D. E. (2002). Bones, bombs, and localization of knowledge spillovers as evidenced by patent cita- break points: The geography of economic activity. American tions. Quarterly Journal of Economics, 108(3), 577–598. doi:10. Economic Review, 92(5), 1269–1289. doi:10.1257/ 2307/2118401 000282802762024502 Jorgenson, D. W., & Stiroh, K. J. (2000). Raising the speed limit: U.S. Dettori, B., Marrocu, E., & Paci, R. (2012). Total factor productivity, economic growth in the information age. Brookings Papers on intangible assets and spatial dependence in the European regions. Economic Activity, 2000(1), 125–210. doi:10.1353/eca.2000.0008 REGIONAL STUDIES 476 Sjoerd Beugelsdijk et al. Keller, W. (2004). International technology diffusion. Journal of Monetary Economics, 55(2), 234–250. doi:10.1016/j.jmoneco. Economic Literature, 42(3), 752–782. doi:10.1257/ 2007.11.006 0022051042177685 Rice, P., Venables, A. J., & Patacchini, E. (2006). Spatial determi- Klenow, P. J., & Rodríguez-Clare, A. (1997). The neoclassical revival nants of productivity: Analysis for the regions of Great Britain. in growth economics: Has it gone too far? In B. S. Bernanke & J. Regional Science and Urban Economics, 36(6), 727–752. doi:10. J. Rotemberg (Eds.), NBER macroeconomics annual (Vol. 12, pp. 1016/j.regsciurbeco.2006.03.006 73–103). Cambridge, MA: MIT Press. Rodríguez-Pose, A. (1999). Innovation prone and innovation averse Knack, P., & Keefer, P. (1997). Does social capital have an economic societies: Economic performance in Europe. Growth and payoff? A cross country investigation. Quarterly Journal of Change, 30(1), 75–105. doi:10.1111/0017-4815.00105 Economics, 112(4), 1251–1288. Rodríguez-Pose, A., & Crescenzi, R. (2008). Research and develop- Krugman, P. R. (1991). Increasing returns and economic geography. ment, spillovers, innovation systems, and the genesis of regional Journal of Political Economy, 99(3), 483–499. doi:10.1086/261763 growth in Europe. Regional Studies, 42(1), 51–67. doi:10.1080/ Krugman, P. R. (1993). First nature, second nature, and metropolitan 00343400701654186 location. Journal of Regional Science, 33(2), 129–144. doi:10.1111/ Rodríguez-Pose, A., & Garcilazo, E. (2015). Quality of government j.1467-9787.1993.tb00217.x and the returns of investment: Examining the impact of cohesion Marrocu, E., Paci, R., & Usai, S. (2013). Productivity growth in the expenditure in European regions. Regional Studies, 49(8), 1274– old and new Europe: The role of agglomeration externalities. 1290. doi:10.1080/00343404.2015.1007933 Journal of Regional Science, 53(3), 418–442. doi:10.1111/jors. Sala-i-Martin, X. X. (1996). Regional cohesion: Evidence and the- 12000 ories of regional growth and convergence. European McCann, P., & Ortega-Argilés, R. (2015). Smart specialization, Economic Review, 40(6), 1325–1352. doi:10.1016/0014-2921 regional growth, and applications to European union cohesion (95)00029-1 policy. Regional Studies, 49(8), 1291–1302. doi:10.1080/ Tabellini, G. (2010). Culture and institutions: Economic develop- 00343404.2013.799769 ment in the regions of Europe. Journal of the European Neffke, F., Henning, M., & Boschma, R. A. (2011). How do regions Economic Association, 8(4), 677–716. doi:10.1111/j.1542-4774. diversify over time? Industry relatedness and the development of 2010.tb00537.x new growth paths in regions. Economic Geography, 87(3), 237– Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R., & de 265. doi:10.1111/j.1944-8287.2011.01121.x Vries, G. J. (2015). An illustrated user guide to the world Oliner, S. D., & Sichel, D. E. (2000). The resurgence of growth in input–output database: The case of global automotive production. the late 1990s: Is information technology the story? Journal of Review of International Economics, 23(3), 575–605. doi:10.1111/ Economic Perspectives, 14(4), 3–22. doi:10.1257/jep.14.4.3 roie.12178 Psacharopoulos, G. (1994). Returns to investment in education: A Van Ark, B., O’Mahony. M.., & Timmer, M. P. (2008). The pro- global update. World Development, 22(9), 1325–1343. doi:10. ductivity gap between Europe and the United States: Trends 1016/0305-750X(94)90007-8 and causes. Journal of Economic Perspectives, 22(1), 25–44. Quah, D. T. (1996). Empirics for economic growth and convergence. doi:10.1257/jep.22.1.25 European Economic Review, 40(6), 1353–1375. doi:10.1016/ Vieira, E., Neira, I., & Vázquez, E. (2011). Productivity and inno- 0014-2921(95)00051-8 vation economy: Comparative analysis of European NUTS II, Rauch, J. E. (1993). Productivity gains from geographic 1995–2004. Regional Studies, 45(9), 1269–1286. doi:10.1080/ concentration of human capital: Evidence from the cities. 00343404.2010.486781 Journal of Urban Economics, 34(3), 380–400. doi:10.1006/juec. Weidmann, N. B., Rod, J. K., & Cederman, L.-E. (2010). 1993.1042 Representing ethnic groups in space: A new dataset. Journal Redding, S., & Venables, A. J. (2004). Economic geography and of Peace Research, 47(4), 491–499. doi:10.1177/0022343310 international inequality. Journal of International Economics, 368352 62(1), 53–82. doi:10.1016/j.jinteco.2003.07.001 Young, A. (1995). The tyranny of numbers: Confronting the statisti- Restuccia, D., Yang, D. T., & Zhu, X. (2008). Agriculture and aggre- cal realities of the East Asian growth experience. Quarterly Journal gate productivity: A quantitative cross-country analysis. Journal of of Economics, 110(3), 641–680. doi:10.2307/2946695 REGIONAL STUDIES http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Regional Studies Taylor & Francis

Regional economic development in Europe: the role of total factor productivity

Regional economic development in Europe: the role of total factor productivity

Abstract

Regional economic development in Europe: the role of total factor productivity. Regional Studies. This paper documents the fact that the large and persistent differences in economic development across subnational regions in European Union countries can largely be attributed to differences in total factor productivity (TFP). Applying the technique of development accounting, the paper decomposes differences in output per worker across 257 European Union regions into a component due to the local availability of production factors and a component due to TFP. As the analysis reveals, TFP differences are large even within countries, and are strongly related to economic geography and historical development paths. This suggests limited interregional diffusion of technology and of efficient production practices.

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REGIONAL STUDIES, 2018 VOL. 52, NO. 4, 461–476 https://doi.org/10.1080/00343404.2017.1334118 Regional economic development in Europe: the role of total factor productivity a b c Sjoerd Beugelsdijk , Mariko J. Klasing and Petros Milionis ABSTRACT Regional economic development in Europe: the role of total factor productivity. Regional Studies. This paper documents the fact that the large and persistent differences in economic development across subnational regions in European Union countries can largely be attributed to differences in total factor productivity (TFP). Applying the technique of development accounting, the paper decomposes differences in output per worker across 257 European Union regions into a component due to the local availability of production factors and a component due to TFP. As the analysis reveals, TFP differences are large even within countries, and are strongly related to economic geography and historical development paths. This suggests limited interregional diffusion of technology and of efficient production practices. KEYWORDS regional economic development; total factor productivity; development accounting; European regions 摘要 欧洲的区域经济发展:全要素生产力的角色。Regional Studies. 本文纪录欧盟横跨次国家区域的经济发展中,广大且 续存的差别可大幅归因于全要素生产力(TFP)的差别之现实。本研究运用发展会计的技术,将欧盟二百五十七座区 域中工人的平均产出差异,分解为由生产要素的在地可及性所导致的因素,以及由TFP所导致的因素。本分析揭露, TFP的差异,即便在国家内部亦相当大,并与经济地理和历史发展路径强烈相关。这显示出有限的跨区域技术扩散及 有效的生产实践。 关键词 区域经济发展; 全要素生产力; 发展会计; 欧洲区域 RÉSUMÉ L’aménagement du territoire en Europe: le rôle de la productivité globale des facteurs. Regional Studies. Cet article démontre que les importants écarts de développement économique qui persistent à travers les régions infranationales situées dans les pays-membres de l’Union européenne s’expliquent dans une large mesure par les écarts de productivité globales des facteurs (PGF). En appliquant la méthode de la comptabilité de développement (development accounting), on décompose les écarts de rendement par travailleur selon 257 régions de l’Union européenne en une composante relative à la disponibilité locale des facteurs de production et une deuxième composante qui s’explique par la PGF. Comme laisse voir l’analyse, les écarts de PGF s’avèrent importants même au sein des pays, et se rapportent étroitement à la géographie économique et aux sentiers de développement historiques. Cela laisse supposer une diffusion interrégionale limitée de la technologie et des procédés de production efficaces. MOTS-CLÉS aménagement du territoire; productivité globale des facteurs; comptabilité de développement; régions européennes CONTACT s.beugelsdijk@rug.nl Department of Global Economics & Management, Faculty of Economics & Business, University of Groningen, Groningen, the Netherlands. m.j.klasing@rug.nl Department of Global Economics & Management, Faculty of Economics & Business, University of Groningen, Groningen, the Netherlands. (Corresponding author) p.milionis@rug.nl Department of Economics, Econometrics & Finance, Faculty of Economics & Business, University of Groningen, Groningen, the Netherlands. © 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc- nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. 462 Sjoerd Beugelsdijk et al. ZUSAMMENFASSUNG Regionale Wirtschaftsentwicklung in Europa: die Rolle der Gesamtfaktorproduktivität. Regional Studies. In diesem Beitrag dokumentieren wir die Tatsache, dass sich die umfangreichen und anhaltenden Unterschiede in der Wirtschaftsentwicklung verschiedener subnationaler Regionen der Mitgliedstaaten der Europäischen Union in großem Umfang auf Unterschiede bei der Gesamtfaktorproduktivität zurückführen lassen. Unter Anwendung der Technik der Entwicklungsbilanzierung zerlegen wir die Unterschiede bei der Leistung pro Arbeitnehmer in 257 Regionen der Europäischen Union in eine durch die lokale Verfügbarkeit von Produktionsfaktoren bedingte Komponente sowie in eine durch die Gesamtfaktorproduktivität bedingte Komponente. Aus der Analyse geht hervor, dass die Unterschiede bei der Gesamtfaktorproduktivität selbst innerhalb desselben Landes umfangreich ausfallen und in einem engen Zusammenhang mit der Wirtschaftsgeografie und den bisherigen Entwicklungspfaden stehen. Dies lässt auf eine begrenzte interregionale Diffusion von Technik und effizienten Produktionspraktiken schließen. SCHLÜSSELWÖRTER regionale Wirtschaftsentwicklung; Gesamtfaktorproduktivität; Entwicklungsbilanzierung; europäische Regionen RESUMEN Desarrollo económico regional en Europa: el papel de la productividad total de los factores. Regional Studies. En este artículo documentamos el hecho de que las diferencias enormes y persistentes en el desarrollo económico en las regiones subnacionales de los países de la Unión Europea pueden atribuirse en gran medida a las diferencias en la productividad total de los factores (PTF). Aplicando la técnica de la contabilidad del desarrollo, desglosamos las diferencias de rendimiento por trabajador en 257 regiones de la Unión Europea en un componente según la capacidad local de los factores de producción y un componente según la PTF. A partir del análisis podemos determinar que las diferencias en la PTF son mayores incluso dentro de un mismo país, y están estrechamente vinculadas con la geográfica económica y las rutas de desarrollo histórico. Esto indica una difusión interregional limitada de la tecnología y las prácticas de producción eficientes. PALABRAS CLAVES desarrollo económico regional; productividad total de los factores; contabilidad del desarrollo; regiones europeas JEL O18, O47, O52, R10 HISTORY Received 22 September 2015; in revised form 8 May 2017 countries have been very stable, with a correlation coeffi- INTRODUCTION cient of around 0.93 since 2000 and of 0.84 since 1980. A better understanding of the persistent nature of these regional economic disparities in Europe is important for two Within-country differences in the level of economic devel- reasons. First, it is the European Commission’s explicit goal opment are large. Using data from 2005 for a large sample to reduce economic disparities between EU regions in order of subnational regions across the world, Gennaioli, La to promote social cohesion (European Commission, 2010). Porta, Lopez-de Silanes, and Shleifer (2013) show that Second, contemporary EU Cohesion Policy emphasizes the the ratio of income per worker between a country’s richest role of technological progress, innovation and knowledge and poorest region is on average equal to 4.4. The same externalities (Barca, 2009; McCann & Ortega-Argilés, data indicate also sizeable income differences within Euro- 2015), recognizing that improvements in productivity are pean Union (EU) countries, which are relatively small and key to enhancing regional economic performance, and that homogeneous, with the corresponding ratio being approxi- innovation and knowledge creation are critical to achieve mately equal to 2.2. such productivity gains. Against this background, this Starting with Barro & Sala-i-Martin (1991), Sala-i- paper explores three related research questions: Martin (1996), and Quah (1996), a vast literature has explored the evolution of these regional income differences over time and the extent to which they have been growing How big are regional differences in technological or shrinking, leading to income convergence or divergence. sophistication and production efficiency, as captured While this literature provides evidence of some degree of by total factor productivity (TFP), and which regions convergence taking place among groups of closely inte- are Europe’s leaders and laggards in terms of TFP? grated regions (Bosker, 2009; Fischer & Stirboeck, 2006; What is the relative importance of differences in TFP in Geppert & Stephan, 2008), overall, regional income dis- explaining differences in the level of economic develop- parities are very persistent. EUROSTAT data show that ment across EU regions? the ratio of incomes between a country’s richest and poorest Which are the main factors that can account for the region as well as the income ranking of regions within observed regional differences in TFP? REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 463 To address these questions, this paper uses the technique of countries and is robust to modifications in the way factor development accounting to decompose regional differences inputs are measured. in output per worker into a component capturing the local Having documented that a large share of regional availability of measurable production factors and a com- income differences is due to variation in TFP, the paper ponent related to TFP. Using development accounting to proceeds to explore which factors can account for this vari- assess the relative contributions of differences in pro- ation. For this purpose it considers a broad range of factors duction factors versus TFP across countries is standard in from the literature on economic development and regional the growth and development literature (Caselli, 2005; economics, which are not already accounted for in the cal- Hall & Jones, 1999; Klenow & Rodríguez-Clare, 1997) culation of regional TFP levels. These are factors related to and has produced important insights regarding the mech- a region’s physical and economic geography, its economic anics of economic development. Yet, it has to structure, its cultural characteristics and institutional qual- our knowledge never been systematically applied at the ity, and its history. Regressing the computed regional TFP regional level. levels on all these factors, while controlling for country- Most of the existing analyses of productivity differences specific effects, the paper finds that the observed variation at the regional level have focused on labour productivity in TFP can be largely attributed to regional differences in (LP), measured simply as output per worker (Corrado, terms of economic geography and historical development Martin, & Weeks, 2005; Esteban, 2000; Gardiner, Martin, paths. This pattern is robust to a battery of additional & Tyler, 2004; Vieira, Neira, & Vázquez, 2011). This is tests including alternative methods to calculate TFP and because LP can be calculated easily from available data considerations of regional heterogeneity and spatial inter- on output and employment. Measuring TFP, on the action across regions. other hand, requires data on other inputs as well, such as The above described findings regarding (1) the large physical and human capital, which are not widely available. size of TFP differences within EU countries and (2) their Looking at TFP, however, has an important advantage strong association with economic geography and historical over LP. It captures productivity conditional not only on development are new and in line with the theoretical pre- available labour inputs but also on other factors of pro- dictions of New Economic Geography (NEG). NEG duction. It reflects solely the efficiency with which different models attribute a critical role to agglomeration effects production inputs are utilized and combined, while LP and localized knowledge spillovers in explaining growth bundles production efficiency and the availability of non- and development patterns (Krugman, 1991, 1993). As labour inputs together into one measure. TFP therefore such, this paper complements previous empirical studies captures better the overall sophistication of the production that have related regional productivity advantages to the process. To the extent that TFP has been studied at the geographical concentration of economic activity (Ciccone regional level (e.g., Capello & Lenzi, 2015; Dettori, Mar- & Hall, 1996; Henderson, Kuncoro, & Turner, 1995) rocu, & Paci, 2012; Marrocu, Paci, & Usai, 2013), it has and knowledge spillovers (Anselin, Varga, & Acs, 1997; been estimated indirectly by means of regression analyses Jaffe, Trajtenberg, & Henderson, 1993; Rauch, 1993). that derive TFP as residuals from regressions of output The separation between factor inputs and TFP that this levels on production inputs. This approach requires factor paper provides, however, goes a step further by highlighting inputs and TFP to be orthogonal, an assumption which how the regional concentration of production activities is unlikely to hold in practice due to complementarities spurs technological progress and gives rise to more efficient between factor inputs and productivity. The develop- production practices that are slow to diffuse even within the ment-accounting approach followed in this paper, in con- same country. trast, does not rely on such an assumption. Overall, the paper suggests that the spatial dimension of Using data from EUROSTAT and focusing on 257 technology diffusion is an important factor behind the per- NUTS-2 regions embedded in 21 of the current 28 EU sistent development gaps across European regions. This countries, the paper performs a development-accounting implies that in order to promote regional economic devel- analysis for 2007. We focus on NUTS-2 regions as these opment and reduce regional disparities, regional policy are the administrative units at which most EU regional pol- should focus on facilitating the diffusion of knowledge icies are targeted, and conduct the analysis based on 2007 and best practices and support regions in specializing data in order to abstract from the influences of the post- smartly by building on existing synergies and exploiting 2007 financial crisis. The results of the analysis demon- economies of scale (McCann & Ortega-Argilés, 2015). strate that both across and within countries TFP differ- The paper is structured as follows. The next section ences explain most of the observed variation in output outlines the basic rationale behind the development per worker. Specifically, we find that measurable factor accounting approach and describes the data on the basis inputs account for about 23% of the variation in output of which TFP is computed. The third section discusses per worker. This implies that differences in technological the obtained TFP figures and their importance in explain- sophistication and production efficiency account for most ing regional differences in output per worker. The fourth of the differences in regional economic development, cor- section presents the results of the regression analyses roborating previous work documenting the important role regarding the correlates of within-country TFP differences. of TFP at the country level (Hsieh & Klenow, 2010). The final section summarizes the findings and discusses This percentage is only slightly higher across than within their broader implications. REGIONAL STUDIES 464 Sjoerd Beugelsdijk et al. Data DEVELOPMENT ACCOUNTING: The data used to calculate TFP levels for regions in the EU METHODOLOGY AND DATA are taken from the EUROSTAT Regional Database. The database covers regions at different levels of aggregation Methodology following the NUTS classification of the EU. The present Development accounting constitutes a well-established analysis focuses on 257 NUTS-2 regions in 21 EU methodology for disentangling observed differences in out- countries, excluding small EU countries and few overseas put levels into differences in factors of production and territories. To calculate TFP levels we need data on output differences in TFP. It builds on the works of Klenow and and employment, which are readily available, and data on Rodríguez-Clare (1997), Hall and Jones (1999), and Case- physical and human capital, which we construct ourselves lli (2005). It begins by postulating an aggregate production based on the available information for investment spending function, which, following the standard in the literature, is and educational attainment. taken to be of the Cobb–Douglas form: a 1−a Y = A K (h L ) (1) it it it it it Output per worker where Y is aggregate output in region i in year t; K is the The employed output data reflect gross value added (GVA) it it respective stock of physical capital; L is the employed in each region, which excludes taxes paid or subsidies it labour force; and h is the average level of human capital it received from the government, and are based on the Euro- of each worker. A reflects the efficiency with which the pean System of Accounts (ESA) 2010 accounting stan- it factors inputs K , h and L are used in the production dards. The data are adjusted for price differences across it it it process, or, in other words, TFP. α is the capital share of countries and over time with country-specific purchasing output, which in our baseline case is assumed to be the power standard (PPS) indices and price deflators provided same in all regions and throughout all time periods. by EUROSTAT. This way the nominal GVA series is Specifically, we set α ¼ 1/3, the typical value assumed in converted into constant 2005 PPS terms. The resulting the macroeconomic literature reflecting the cross-sectional figures are then divided by the total number of workers, and time-series evidence reported by Gollin, Parente, and including self-employed individuals, in each region. Rogerson (2002). In our robustness analyses, we also Thus, the output data used in the calculations of regional explore the alternative approach of allowing for region- TFP levels correspond to regional purchasing-power- specific capital income shares by using a generalized trans- adjusted levels of real GVA per worker. log version of equation (1) as in Inklaar and Timmer (2013). This more flexible approach permits differences Physical capital per worker in production structures across regions to be reflected in To obtain estimates of regional physical capital stocks the different values for α. perpetual inventory method is employed. This method Rewriting equation (1) in per worker terms, the pro- allows for the construction of a capital stock series based duction function implies that output per worker, y ,is a it on investment data using the formula: function of the per worker inputs of physical capital, k , it and human capital, h : K = I + (1 − d )K . (4) it it it i it−1 a 1−a y = A k h (2) Thus, the physical capital stock, K , in region i in period t it it it it it is equal to the capital investment, I , in that period plus the it We use this expression to back out the level of productivity amount of un-depreciated capital left over from the pre- from data on y , k and h . Based on expression (2), we can it it it vious period, with d indicating the rate of physical capital also assess how much of the regional variation in output per depreciation. worker is explained by variation in the factor inputs and Data on regional investment in terms of gross fixed how much should be attributed to underlying differences capital formation are available in the EUROSTAT in TFP. This can be done, as discussed in greater detail Regional Database for the years since 2000. These are by Caselli (2005), by performing a standard variance then converted from current prices to constant prices by decomposition and calculating the following statistic: using the country-level price deflator of gross fixed-capital kh formation reported by EUROSTAT. The depreciation Var[ ln (y )] kh it kh a 1−a V = , with y = k h (3) rate d is allowed to vary across regions. Specifically, the t it it it i Var[ ln (y )] it region-specific depreciation rates employed are weighted kh Specifically, V reflects the share of the observed variance averages of the sector-specific depreciation rates reported in the natural logarithm of output per worker across regions in the World Input–Output Database (WIOD) database that is explained solely by variation in physical and human (Timmer, Dietzenbacher, Los, Stehrer, & de Vries, capital. Note that this share would be equal to 1 if A were 2015) with the weights corresponding to the average it the same across all regions, and it would be strictly less than share of each region’s sector in the total GVA of each 1 as long as there is some regional variation in TFP. Thus, region between 2000 and 2007. kh lower values for V imply that a larger share of the Beyond data on investment spending and depreciation observed differences in output per worker should be attrib- rates, the application of the perpetual inventory formula uted to TFP. requires also a value for the capital stock in the initial REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 465 year, which in our case is 2000. Typically, in the literature shares with the respective years of schooling attained at this value is a guesstimate (Bernanke & Gürkaynak, 2002; each level. In addition to this baseline estimate, we also Klenow & Rodríguez-Clare, 1997), which in the case of a make two alternative assumptions regarding the years of low long time-series for investment has little effect on end-of- schooling attained. In our lower-bound estimate, ays , it period capital stock estimates. In our case, though, as the we assume that ISCED levels 0–2, 3–4 and 4–5 correspond investment series is relatively short, the end-of-period esti- to six, nine and 12 years of schooling respectively. In our high mate is likely to be sensitive to the initial value chosen. In upper-bound estimate, ays , we assume nine, 12 and 16 it light of this, we use three different approaches to pin down years of schooling for each of the respective ISCED edu- the value of the capital stock in 2000. cation levels. Section B in the supplemental material online For the baseline series, K , the paper follows the provides details on these assumptions and discusses robust- it approach proposed by Garofalo and Yamarik (2002) and ness checks. attributes the country-level sector-specific capital stocks To convert average schooling years into human capital, reported in WIOD (Timmer et al., 2015) to each region we assume a standard Mincerian human capital function of based on the share of each region’s GVA in that sector in the form: the country-wide GVA in that sector. w(ays ) it B h = e it For the alternative series, K , we apportion the it country-level capital stocks reported in Penn World Tables where w(ays ) is a piecewise linear and parameterized as it 8 (PWT8) (Feenstra, Inklaar, & Timmer, 2015) to each follows: country’s subnational regions based on the share of each ⎧ ⎫ 0.134 · ays if ays ≤ 4 ⎪ it it ⎪ region’s average share in the GVA of the country. ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0.134 · 4 if 4 , ays ≤ 8 ⎨ it ⎬ For the alternative series, K , we follow Feenstra et al. it w(ays ) = + 0.101(ays − 4) . it it (2015) and postulate for all regions a capital-output ratio of ⎪ ⎪ ⎪ ⎪ ⎪ 0.134 · 4 + 0.101 · 4 if ays . 4 ⎪ it 2.6. This produces a conservative estimate for the initial ⎪ ⎪ ⎩ ⎭ + 0.068(ays − 8) it differences in capital stocks across regions that ignores existing variation in capital intensities due to variation in The assumed values for the returns to schooling follow the the sectoral composition of each region. More details earlier development-accounting exercises of Hall and Jones regarding the construction of the initial values for these (1999) and Caselli (2005) and are in line with the microe- three initial capital stock series are explained in Section A conomic evidence summarized by Psacharopoulos (1994). in the supplemental material online. They are also identical to the values used in the PWT8 By using these three alternative values for the initial to convert years of schooling into human capital. Thus, regional capital stock, we can produce three different our human capital estimates are comparable with the regional capital stock series for the subsequent years. The country-level estimates reported in the PWT8. In accord- A B C per worker capital stocks, k , k , k , are then constructed it it it ance with the three sets of figures for ays , we derive three it by dividing the estimated figures for each region with the sets of figures for the average level of human capital per corresponding number of workers and multiplying by the base region: the baseline estimate, h , and the two alternatives, it high country-specific price indexes for capital goods, which low h and h . it it matches the units in which the employed output data are measured. TFP DIFFERENCES ACROSS EUROPEAN Human capital REGIONS To measure the average level of human capital in each region, we use information on the share of the working-age popu- Based on the above-described figures for regional output per lation that has attained different levels of education. The worker, physical capital per worker and human capital per EUROSTAT Regional Database provides data on the worker, we compute TFP scores for our sample of 257 share of the population aged 25–64 years who have attained NUTS-2 regions. For the purpose of comparison, we also each of the following levels in the International Standard calculate TFP scores for the 21 countries in which these Classification of Education (ISCED) system: 257 regions are nested. Given the three different physical capital and alternative human capital stocks series and the ISCED 0–2: Pre-primary, primary, lower secondary. possibility to allow for region-specific capital elasticities α, ISCED 3–4: Upper secondary, post-secondary non- we construct six different TFP estimates. Our baseline tertiary. TFP estimate, A , is computed from equation (2) based ISCED 5–6: First- and second-stage tertiary. on a fixed α ¼ 1/3 and using the physical capital stock esti- A base mate k , and the human capital estimate h . The second it it Following Barro and Lee (2013), we assume that ISCED estimate, A , uses the same physical and human capital 0–2 corresponds to six years of schooling, ISCED 3–4to stock estimates but employs a translog production function 12 years, and ISCED 5–6 to 16 years. Based on this with region-specificvaluesfor α instead. The region-specific assumption, our baseline estimate of average years of capital elasticities, α, are based on the industry-specificratios schooling for the working-age population in each region, of non-labour income to output taken from the WIOD. base ays , can be calculated by multiplying the population Series A and A use the two alternative physical capital 3 4 it REGIONAL STUDIES 466 Sjoerd Beugelsdijk et al. Table 1. Summary statistics, total factor productivity (TFP) estimates. Sample: 257 European Union regions (NUTS-2) Observations Mean SD Minimum Maximum Correlation with A1 A1, Baseline TFP estimate 257 0.998 0.200 0.461 2.517 A2, Alternative TFP estimate with varying α 257 0.999 0.187 0.506 2.416 0.999 A3, Alternative TFP estimate with k 257 0.993 0.206 0.452 2.572 0.977 A4, Alternative TFP estimate with k 257 0.983 0.199 0.454 2.512 0.980 low A5, Alternative TFP estimate with h 257 1.000 0.204 0.466 2.573 0.996 high A6, Alternative TFP estimate with h 257 1.000 0.202 0.468 2.548 0.996 stocks series respectively in combination with the baseline other. The reported standard deviations (SD) of around 0.2 base human capital estimate, h ,and α ¼ 1/3. Series A and also reveal a high degree of dispersion in TFP. it A are also based on a fixed value for α, but employ the To visualize the differences in TFP across EU regions, two alternative human capital stocks series in combination Figure 1 maps the distribution of the baseline TFP scores. with our baseline estimate for physical capital, k . It shows high TFP values for core Western European it Basic summary statistics for all six TFP estimates are regions, particularly those along the London–Amsterdam– reported in Table 1. To facilitate interpretation of the Munich–Milan corridor, with Inner London recording the TFP scores, we report values relative to the EU average. highest value. Low TFP values are observed in most per- As Table 1, indicates the estimates in all six cases are simi- ipheral Eastern European regions, with regions in Bulgaria lar in terms of magnitudes and highly correlated with each and Romania dominating the bottom of the distribution. Figure 1. Baseline total factor productivity (TFP) levels (darker colours indicate higher TFP). REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 467 function and allowing for region-specific values of α do we obtain a higher ratio of about 29%. This implies that the observed variation in factor inputs explain less than one-quarter of the observed variation in output per worker at the regional level. This leaves the remaining share of the variation to be attributed to TFP differences and the covariance between TFP and factor inputs. The second row reports the variation in output per worker explained by factor inputs across countries. This ratio is found to be on average about 2 percentage points higher than the overall ratio. Production factors explain a bit more of the observed output differences across countries and the resulting shares of around 0.25 resonate well with the estimate of Caselli (2005). This similarity of our devel- Figure 2. Total factor productivity (TFP) dispersion between opment accounting results across regions and countries give and within countries. us confidence in our TFP estimates for European regions. kh The next six rows report the values for V for the five The map also reveals substantial variation in TFP within largest EU economies, Britain, France, Germany, Italy and countries. We explore the sources of this regional variation Spain, as well as the average of the within-country ratios in TFP in more detail in the next section. across all 21 EU countries in our sample. On average, Figure 2 visualizes the variation in TFP within each within countries factor inputs explain about 20% of the country in a dispersion diagram. As shown there, the degree variation in output per worker. Yet, for France and Spain of dispersion in TFP differs substantially across countries. we find this ratio to be substantially higher, while for Brit- While in some EU countries, such as Britain and Germany, ain and Germany it is lower. This pattern mirrors the dis- there is substantial interregional variation in TFP, in other persion in TFP within countries presented in Figure 2. large countries, such as France and Spain, the distribution All the above results are similar when we perform the of TFP across regions is relatively condensed. Looking analysis for years other than 2007 or when we exclude at Eastern European countries where TFP is on average specific regions from the sample or merge functional low, we find in many of them sizeable dispersion in TFP, regions following Annoni and Dijkstra (2013). with the capital-city regions outperforming the rest. In summary, the variance decomposition analysis indi- To assess more carefully the relative importance of TFP cates that both within and between countries differences in in accounting for regional differences in output per worker, output per worker are less the result of the local availability kh in Table 2 we calculate, using equation (3), V , the share of production factors and more a consequence of the effec- of the variance in output per worker that can be explained tiveness with which these factors are combined in the pro- solely by the variation in factor inputs. In this context, a duction process. Given this importance of TFP for kh lower value for V implies a larger role for TFP. Global understanding regional differences in the level of economic country-level development-accounting studies typically development, in the following we explore which factors can kh find values for V around 40% (Hsieh & Klenow, 2010), explain the regional variation in TFP. but these values have been shown to be lower for European countries and closer to 25% (Caselli, 2005). kh Table 2 reports the V for different groups of regions CORRELATES OF REGIONAL TFP and based on all six TFP estimates. The first row reports DIFFERENCES that across all 257 regions the variation in output per worker explained by factor inputs is in most cases about To understand better the sources of the large TFP differ- 23%. Only when employing the translog production ences across EU regions, we relate regional TFP levels to Table 2. Development accounting results. kh V for different subsamples and total factor productivity (TFP) estimates (%) TFP estimate A1 A2 A3 A4 A5 A6 257 EU regions (NUTS-2) 23.11 29.02 22.11 23.18 23.26 23.65 21 EU countries 25.01 32.43 24.28 25.86 25.42 25.83 37 British regions (NUTS-2) 15.48 19.43 11.52 11.52 13.73 14.38 27 French regions (NUTS-2) 37.09 44.42 26.69 26.63 27.85 30.14 38 German regions (NUTS-2) 13.30 15.35 11.10 11.96 11.94 12.63 21 Italian regions (NUTS-2) 19.27 24.38 16.48 17.00 16.01 15.08 19 Spanish regions (NUTS-2) 47.69 53.12 37.73 38.25 30.83 32.90 Within-country average 19.63 22.80 15.77 16.38 15.71 16.65 REGIONAL STUDIES 468 Sjoerd Beugelsdijk et al. a set of variables that have been emphasized in the literature we consider the share of workers employed in science as being important factors influencing regional economic and technology, emphasized by Anselin et al. (1997), development. This set of variables is not meant to be and the social filters measure proposed by Rodríguez- exhaustive, as the list of relevant regional development Pose (1999), which reflects the innovative and learning determinants can be potentially quite long. Instead, we capacity of each region. focus on variables reflecting different potential sources of Economic structure: since TFP levels naturally vary across TFP differences in order to provide a comparative assess- sectors, our analysis accounts for the economic structure ment of their importance for EU regions. of each region. Specifically, we consider the share of To assess the strength of the relationship between TFP labour employed in agriculture as productivity in the and these variables, we estimate the following cross-sec- agricultural sector tends to be lower than in the rest of tional regression: the economy (Restuccia, Yang, & Zhu, 2008). We also consider the amount of oil production and reserves A = a + X b + u + 1 , ic ic c ic in each region as the presence of a large oil and gas sector may lead to overestimation of TFP as the extraction of where the dependent variable is our measure of TFP, as cal- natural resources typically involves relatively little pro- culated in the previous section, for region i in country c rela- duction inputs but generates high value added (Gunton, tive to the EU average. To control for country-specific 2003). To capture general productivity-enhancing characteristics influencing TFP, we include a set of country activities, we include the number of patents filed per dummies, denoted by u . All regressions are based on TFP worker and the share of regional research and develop- figures and explanatory variables measured in 2007 (pro- ment (R&D) spending in regional GDP, both of vided they are time varying). This is motivated by the which should be sources of positive spillover effects aim of understanding the relationship between TFP and (Audretsch & Feldman, 1996; Jaffe et al., 1993). other regional characteristics in a long-run equilibrium, Culture: one important mechanism through which cul- which arguably was disrupted by the post-2007 financial ture may affect regional TFP is social capital. Social crisis. TFP data for 2007 are available for 257 regions in capital is typically measured by the level of generalized 21 countries, but due to missing observations on some of trust. As generalized trust has been shown to have a the explanatory variables, X , most regressions include ic positive association with regional economic develop- 251 observations. ment (Beugelsdijk & van Schaik, 2005; Tabellini, 2010), our analysis employs the level of trust in each Explanatory variables region, measured by data from the European Values We consider variables related to physical and economic Study (EVS). In addition to social capital, the analysis geography, culture, institutions, history, and other also considers the degree of ethnic heterogeneity by structural characteristics of each region. When selecting including an ethnic fractionalization index, as in Gen- these variables, we focus on measures that vary at the naioli et al. (2013). This is motivated by the fact that regional level and for which data are widely available, higher diversity is generally associated with lower levels which imposes limits on the variables selection. Below of economic development (Alesina & Zhuravskaya, we briefly describe the main variables employed in the 2011; Beugelsdijk & Klasing, 2017; Beugelsdijk, analysis. Measurement details of all variables are provided Klasing & Milionis, 2017). in the Data Appendix below. Institutions: in light of the documented regional differ- ences in the quality of institutions (Charron, Dijkstra, Physical geography: to capture the physical geography of & Lapuente, 2015; Rodríguez-Pose & Garcilazo, each region we consider three key characteristics: its 2015), we construct a measure of the quality of govern- latitude, its access to the sea and its access to navigable ance at the regional level. We follow Becker, Egger, and rivers. These characteristics have been shown to be von Ehrlich (2013) and use Eurobarometer survey data important for long-run economic development (Bosker capturing respondents’ satisfaction with local democracy & Buringh, 2017; Bosker, Buringh, & van Zanden, and their trust in the local judicial system. This measure 2013; Gallup, Sachs, & Mellinger, 1999). is by construction highly correlated with the regional Economic geography: to capture the economic geography quality of governance index assembled by the European of each region, we consider its population density, its Commission (Charron et al., 2015), but covers a larger rate of urbanization and its market potential measured number of regions. Furthermore, we consider whether by the level of gross domestic product (GDP) in the a region was part of the Communist Bloc to capture nearby regions, all three of which are sources of positive the heritage of Communism in regions of Eastern agglomeration effects (Brakman, Garretsen, & van Europe and parts of present-day Germany. Marrewijk, 2009; Ciccone, 2002; Redding & Venables, History: development outcomes have been shown to be 2004). We also consider the average distance of each persistent. Today’s centres of economic activity may be region to the country’s economic centre to measure the in specific locations not because of the current optimality importance of spillover effects operating from centre to of these locations but because of historical path depen- periphery (Rice, Venables, & Patacchini, 2006). Fur- dence (Akcomak, Webbink, & ter Weel, 2016; Bleakley thermore, to capture knowledge-related externalities, & Lin, 2012; Davis & Weinstein, 2002). To account for REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 469 the legacy of history on current TFP differences, we significance level lower than the conventional threshold consider each region’s historical urban density in 1800 of 0.1; and in column 6 again variables that subsequently based on data from Bairoch, Batou, and Chevre fall below that threshold. (1988). We also consider for each region how many The resulting specification of column 6 highlights the cities were historically located on the crossing of two main variables that are closely associated with regional vari- or more Roman roads, which Bosker et al. (2013) ation in TFP. Specifically, we find that TFP levels are higher have shown to be correlated with historical development in regions that are closer to large markets, have a young and over the past two millennia. well-educated workforce, are more trusting, and have also historically been more urbanized On the other hand, TFP Table 3 shows the descriptive statistics for all these is lower in regions with a Communist history and a relatively explanatory variables, as well as their correlations with our large share of the agricultural sector. These seven variables baseline TFP estimate. Several variables exhibit a strong together with the country dummies explain 75% of the over- positive correlation with TFP. The regression analysis all variation in regional TFP levels and 72% of the variation below assesses more carefully the relative importance of in TFP levels within EU countries. these variables in explaining TFP differences across regions. To assess the relative importance of our main explana- tory variables, Table 5 reports the implied effect sizes in terms of a 1 SD change in the explanatory variables, with REGRESSION RESULTS the variables ordered by their quantitative importance. Quantitatively most important is the post-Communist Table 4 shows the main regression results relating regional dummy, with TFP being on average 22 percentage points TFP levels, expressed relative to the EU average, to the lower in a region that was part of the former Communist aforementioned explanatory variables. Column 1 reports Bloc. This is followed by historical urban density with an the estimation results of a cross-sectional regression speci- effect size on TFP relative to the EU average of 7 percen- fication including all explanatory variables. Column 2 adds tage points for a 1 SD change. This is much larger than the country dummies to the specification. In columns 3–6we effect of the contemporary urbanization rate whose stan- follow a standard general-to-specific approach and itera- dardized effect is only 1.3 percentage points. Next in line tively eliminate from the specification variables based on are the social filters and the agricultural labour share their significance levels. In column 3 we drop variables whose implied magnitudes are slightly above and slightly with a significance level lower than 0.5; and in column 4 below 5 percentage points respectively. A 1 SD increase variables that subsequently fall in this category. Then in in market potential is associated with an increase in relative column 5 we proceed to eliminate variables with a Table 3. Summary statistics, regressors. Sample: 257 European Union regions (NUTS-2) Correlation Observations Mean SD Minimum Maximum with A1 A1, Baseline total factor productivity (TFP) estimate 251 1.000 0.201 0.444 2.357 Latitude 251 48.517 5.687 28.353 66.439 0.254 River Access 251 1.422 1.832 0.000 14.000 0.126 Sea Border 251 0.470 0.500 0.000 1.000 0.177 Population Density 251 0.353 0.869 0.003 9.244 0.479 Urbanization Rate 251 0.356 0.283 0.000 1.585 0.291 Workers in Science & Technology 251 26.680 6.750 12.000 51.600 0.529 Market Potential 251 0.217 0.252 0.003 1.784 0.598 Distance to Economic Center 251 0.224 0.196 0.000 1.739 –0.141 Agr Labor Share 251 0.064 0.080 0.000 0.507 –0.624 Oil Production 251 0.017 0.055 0.000 0.548 –0.389 R&D Spending 249 0.014 0.012 0.001 0.067 0.438 Patents per Worker 250 0.109 0.131 0.000 0.672 0.456 Social Filters 251 0.141 1.521 –3.444 4.321 0.554 Ethnic Diversity 251 0.625 0.541 0.000 1.946 –0.343 Trust 251 0.343 0.157 0.037 0.781 0.415 Institutional Quality 251 0.298 0.576 –1.200 1.713 0.496 Post Communist 251 0.235 0.425 0.000 1.000 –0.688 Urban Density 1800 251 0.025 0.192 0.000 2.946 0.462 Roman Roads Hubs 251 0.610 1.308 0.000 9.000 0.066 REGIONAL STUDIES 470 Sjoerd Beugelsdijk et al. Table 4. Stepwise regression results. Dependent variable: A1, Baseline total factor productivity (TFP) estimate (1) (2) (3) (4) (5) (6) Latitude 0.003** 0.001 [0.001] [0.002] River Access 0.007** 0.002 [0.003] [0.004] Sea Border 0.000 0.006 [0.015] [0.013] Population Density –0.012 –0.010 –0.011 –0.012 [0.015] [0.012] [0.012] [0.012] Urbanization Rate 0.022 0.040* 0.045** 0.052* 0.048* 0.047* [0.024] [0.021] [0.020] [0.026] [0.025] [0.026] Workers in Science & Technology 0.005*** 0.002 0.002 [0.002] [0.002] [0.002] Market Potential 0.141*** 0.070** 0.070* 0.074** 0.079** 0.079** [0.038] [0.032] [0.035] [0.034] [0.028] [0.029] Distance to Economic Center 0.012 –0.023 –0.027 –0.028 [0.030] [0.025] [0.021] [0.021] Agr Labor Share –0.335** –0.544** –0.550** –0.574** –0.606*** –0.620*** [0.143] [0.251] [0.246] [0.219] [0.193] [0.189] Oil Production –0.171 –0.175 –0.182 –0.190 [0.162] [0.133] [0.129] [0.135] R&D Spending 0.722 0.751 0.943 1.057 [0.681] [0.822] [0.714] [0.684] Patents per Worker 0.011 0.045 [0.070] [0.053] Social Filters –0.009 0.021* 0.023* 0.028*** 0.038*** 0.035*** [0.006] [0.012] [0.013] [0.008] [0.006] [0.005] Ethnic Diversity –0.008 –0.005 [0.010] [0.011] Trust 0.024 0.048* 0.051** 0.051** 0.046** 0.042* [0.049] [0.024] [0.022] [0.022] [0.021] [0.020] Institutional Quality –0.014 –0.033 –0.034* –0.036* –0.037 [0.016] [0.021] [0.020] [0.020] [0.023] Post Communist –0.265*** –0.234*** –0.241*** –0.246*** –0.252*** –0.222*** [0.024] [0.021] [0.019] [0.018] [0.022] [0.006] Urban Density 1800 0.374*** 0.396*** 0.402*** 0.405*** 0.359*** 0.356*** [0.045] [0.032] [0.032] [0.033] [0.025] [0.025] Roman Roads Hubs –0.010** 0.000 [0.004] [0.004] Constant 0.733*** 0.933*** 0.998*** 1.038*** 1.044*** 1.030*** [0.089] [0.125] [0.078] [0.024] [0.024] [0.023] Countries 21 21 21 21 21 21 Observations 248 248 249 249 251 251 Country dummies No Yes Yes Yes Yes Yes Overall adjusted R 0.821 0.778 0.774 0.756 0.738 0.747 Within adjusted R – 0.720 0.725 0.726 0.718 0.715 Notes: Estimation with ordinary least squares (OLS). Robust standard errors clustered at the country level are shown in brackets. ***p < 0.01, **p < 0.05, *p < 0.1. REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 471 Table 5. Magnitudes. development paths and trust play a key role in explaining why some regions have higher TFP levels than others. Based on regression specification of Table 4, column (6) The finding that TFP differences are important in Change in total explaining the development gaps across European regions factor productivity and that these differences are related to economic geogra- Variables Coefficient SD (TFP) phy are broadly supportive of the extensive literature on Post –0.222 0.425 –9.42% New Economic Geography. The persistent nature of Communist these gaps, even within the same country, and the associ- Urban Density 0.356 0.192 6.84% ation with historical development and culture suggest that there is a strong local dimension to technology and Social Filters 0.035 1.521 5.26% knowledge that needs to be better understood. This has important implications for regional development policy, Agr Labor –0.620 0.080 –4.96% which should be designed primarily with the aim to support Share regions (1) in building their comparative advantages in Market 0.079 0.252 2.00% terms of technology and knowledge, (2) in specializing Potential smartly to exploit economies of scale and (3) in building Urbanization 0.047 0.283 1.33% on existing synergies. These conclusions are very much in Rate line with the current discussion on smart specialization Trust 0.042 0.157 0.66% and place-based development strategies within the EU (Barca, 2009; McCann & Ortega-Argilés, 2015). They underscore the need for EU policies to take into account TFP of 2 percentage points. Finally, trust has with 0.66 available knowledge in each region and linkages across percentage points the smallest effect. regions in order to help regions achieve their long-run In Section C in the supplemental material online we development potential. assess the robustness of our regression results along the fol- The orientation of regional development policy along lowing lines. First, we show that the results hold also for these lines, of course, may not be easy. Yet, our approach the five alternative TFP figures. Second, we document of calculating, documenting and analysing TFP at the that they are robust to changes in the sample composition regional level could provide a useful tool for policy and pro- and other corrections for heterogeneity across regions not vide interesting avenues for future research. The analysis captured in the main analysis, such as accounting for could be further extended to the sectoral level and could city-region effects and including spatial lags. Third, we also be used to make comparisons over time. Future show that the results also hold when employing alternatives research could also compare TFP levels with more disag- measures of institutional quality and the innovative capacity gregate regional characteristics such as regional diversity of a region, but that the positive results for trust do not (Frenken, van Oort, & Verburg, 2007), spatial diversifica- extend to alternative proxies of social capital (Beugelsdijk tion patterns (Neffke, Henning, & Boschma, 2011), and & Smulders, 2003; Knack & Keefer, 1997). workforce mobility patterns and information on their spatial networks (Huber, 2012). Moreover, one could further explore how the spatial dimension of technology CONCLUSIONS diffusion may differ depending on the innovation being embodied (i.e., new products and or services) or disembo- Differences in the level of economic development within died (i.e., superior measurement practices), and how the EU countries are large and persistent. The aim of this rents extracted from these types of innovation may have paper is to shed more light on why this is the case by cal- different spatial implications (Rodríguez-Pose & culating, documenting and analysing TFP levels for 257 Crescenzi, 2008; Keller, 2004). Although disembodied EU regions. To that end, we conduct, to our knowledge, innovation has long been recognized in the management the first development-accounting exercise at the subna- literature (e.g., Beugelsdijk, 2008), its significance for tional level to decompose regional differences in output TFP has only recently been acknowledged in the economic per worker into a component reflecting the local availability development literature (Bloom & van Reenen, 2007). Such of factor inputs and a component capturing differences in studies could extend our analysis of regional TFP differ- TFP. This exercise reveals that about 75% of the differ- ences by exploring the microfoundations and underlying ences in regional economic development can be attributed mechanisms behind the broad patterns uncovered in this to differences in TFP. This is similar between and within paper. countries, suggesting that the spatial diffusion of technol- ogy and efficient production practices is limited and that ACKNOWLEDGEMENTS the limits extend beyond national borders. TFP levels tend to be highest along the London–Amsterdam– This paper benefited from useful suggestions made by three Munich–Milan axis and lowest in peripheral regions in anonymous referees as well as Steven Brakman, Marta Eastern Europe. We furthermore document that regional Curto-Grau, Lewis Dijkstra, Robert Inklaar and Ton van differences in terms of economic geography, historical Schaik. The authors also thank seminar participants at REGIONAL STUDIES 472 Sjoerd Beugelsdijk et al. the universities of Basel, Groningen, Louvain and well as eight overseas territories of France, Portugal and St. Gallen, as well as conference participants at the 2014 Finland due to limited data availability. U4 Globalization Workshop, the 2015 SMYE (Spring 7. The information provided by EUROSTAT does not Meeting of Young Economists), the 2015 EPCS (Euro- allow us to correct for price differences within countries. Yet, pean Public Choice Society) and the 2015 World Congress as noted by Acemoglu and Dell (2010) and Gennaioli et al. of Comparative Economics for helpful comments. (2013), this should not have a major impact on the analysis. 8. The average regional depreciation rate is 6.3%, which is very close to the typical value of 6% employed in most DISCLOSURE STATEMENT development accounting studies (Caselli, 2005). 9. This corridor is also referred to as the ‘Blue Banana’,with No potential conflict of interest was reported by the the ‘banana’ describing its shape and blue alluding to the EU authors. flag. The term was coined by geographer Roger Brunet. 10. As is evident from Figure 2, there is a big outlier in FUNDING the TFP distribution which is the Inner London area. None of our results, however, is affected by this outlier observation, Sjoerd Beugelsdijk acknowledges financial support from as we discuss in greater detail in the robustness analysis. the Nederlandse Organisatie voor Wetenschappelijk 11. Allowing for region-specific α increases the ability of Onderzoek (NWO) [grant number 054-11-010]. factor inputs to explain the variation in output – both across and within countries – and reduces the relative importance SUPPLEMENTAL DATA of TFP differences. Nevertheless, this does not alter our main conclusions regarding the relative explanatory power Supplemental material for this article can be accessed of factor inputs versus TFP between and within countries. https://dx.doi.org/10.1080/00343404.2017.1334118 12. This is because for some arguably relevant factors we were either unable to find comprehensive data or the avail- NOTES able data only displayed variation at the country level. 13. In Section C in the supplemental material online we 1. In fact, 35% of the EU’s total budget – corresponding also explore the role played by regional research and inno- to €347 billion – was allocated during the 2007–13 budget vation networks. Yet, as the available data only cover a period in the form of development-promoting Structural smaller set of regions, we do not consider this variable in Funds to less developed regions. our main analysis. 2. Both the Lisbon Agenda as well as the Europe 2020 14. Following the standard in the literature, this is calcu- strategy goals of making Europe and its regions the most lated as the share of the regional population indicating that competitive world economy stress the importance of build- ‘most people can be trusted’ (as opposed to ‘you can’t be too ing knowledge infrastructures, enhancing innovation and careful when dealing with people’) averaged across all sur- promoting economic reform (European Commission, vey waves (1984–2008). 2010; European Council, 2000). 15. Section C in the supplemental material online also 3. This decomposition, for example, has been instrumen- reports results using alternative institutional quality measures. tal for the analysis of the information and communication 16. An alternative approach here would be to estimate technology (ICT) revolution (Jorgenson & Stiroh, 2000; repeatedly our regression specification eliminating in each Oliner & Sichel, 2000), the rapid growth of East Asian round the variable with the highest p-value until all insig- economies (Hsieh, 2002; Young, 1995), and the pro- nificant variables have been removed from the specification. ductivity gap between Europe and the United States (van Following this more cumbersome approach leads to exactly Ark, O’Mahony, & Timmer, 2008). the same specification as that of column (6). 4. NUTS ¼ Nomenclature des Unités Territoriales 17. The dummy variable indicating post-Communist Statistiques. regions is highly significant and negatively related to pro- 5. For each EU country, there is a hierarchical system of ductivity differences, even after the inclusion of country regional subdivision that proceeds from coarser to finer sub- dummies. The inclusion of country dummies implies that national NUTS units. In this system, NUTS-0 refers to the the identification of this variable comes from the variation country as a whole, NUTS-1 refers to the coarsest level of within Germany, with regions that were part of the former subnational division, NUTS-2 to an intermediate level and German Democratic Republic (GDR) being significantly NUTS-3 to the finest level. This system is designed such less productive than West German regions. As regional that the resulting regions at each level of aggregation are institutional quality itself does not appear to be a significant comparable in terms of population size. predictor of within-country productivity differences, this 6. Specifically, we exclude the six smallest EU countries implies that the post-Communist dummy variable is not (Cyprus, Estonia, Latvia, Lithuania, Luxembourg and picking up effects related to the current quality of insti- Malta), which due to their size do not have a subnational tutions in these regions, but instead captures the more fun- division at the NUTS-2 level. It also excludes Croatia as damental and long-lasting impacts of Communism. REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 473 APPENDIX: VARIABLE DESCRIPTIONS AND SOURCES Variable Description Source Gross Value Added Gross value added (GVA) in all sectors converted into 2005 EUROSTAT (nama_10r_3gva) purchasing power standard (PPS) (European System of Accounts (ESA) 2010). Employment Employment in all sectors EUROSTAT (nama_10r_3empers) Investment Gross fixed capital formation converted into 2005 euros (ESA EUROSTAT (nama_10r_2gfcf) 2010 system of accounts) Primary and Lower Share of the population aged 25–64 years with a lower EUROSTAT (edat_lfse_04) Secondary Education secondary, primary and pre-primary education (International Standard Classification of Education (ISCED) levels 0–2) Upper Secondary Share of the population aged 25–64 years with an upper- EUROSTAT (edat_lfse_04) Education secondary education (ISCED levels 3–4) Tertiary Education Share of the population aged 25–64 years with a tertiary EUROSTAT (edat_lfse_04) education (ISCED levels 5–8) Latitude Degrees of latitude of the region’s centroid EUROSTAT Geodata River Access Number of cities in a region located by a river or a navigable Bosker et al. (2013) waterway Sea Border Dummy variable for regions located on the sea. Hamburg and Authors’ own coding London are coded as 1 due to their almost direct sea access and the importance of maritime trade in these cities Population Density Population per area (km ) EUROSTAT (nama_r_e3popgdp; demo_r_d3area) Urbanization Rate Share of each region’s population living in cities EUROSTAT (ubr_cpop1; nama_r_e3popgdp) Workers in Science & Scientists and engineers as a percentage of the active EUROSTAT (hrst_st_rcat) Technology population Market Potential Aggregate level of gross domestic product (GDP) within a 100- European Commission DG Regio kilometre circle around the region Distance to Economic Areal distance between each region’s largest city and the Authors’ own coding using a Center economic centre of the country distance calculator Agr Labor Share Number of persons employed in agriculture as a share of total EUROSTAT (nama_10r_3empers) regional employment Oil Production Oil production and reserves in logs Gennaioli et al. (2013) R&D Spending Share of total regional research and development (R&D) EUROSTAT (rd_e_gerdreg; spending in regional GDP nama_10r_2gdp) Patents per Worker Patent applications per million of the active population EUROSTAT (pat_ep_rtot) Young Share of the population aged 15–24 years in the total regional EUROSTAT (demo_r_d2jan) population Training Percentage of the regional population that has participated in EUROSTAT (trng_lfse_04) education and training in the past four weeks Long-Term Long-term unemployment (12 months and more) as a EUROSTAT (lfst_r_lfu2ltu) Unemployment percentage of unemployment Social Filters First principal component of young, training, long-term Following Rodríguez-Pose and unemployment and tertiary education Crescenzi (2008) (Continued) REGIONAL STUDIES 474 Sjoerd Beugelsdijk et al. Continued. Variable Description Source Ethnic Diversity Number of ethnic groups per region Weidman, Rod, and Cederman (2010) Trust Share of the population saying ‘most people can be trusted’ as European Values Study (EVS) opposed to ‘you can’t be too careful when dealing with people’, averaged across all European Values Study (EVS) waves Institutional Quality Regional quality of governance predicted from regression of Charron et al. (2015); the regional quality of governance measure by Charron et al. Eurobarometer (2015) on regional values of Satisfaction with democracy and Trust in the justice system from Eurobarometer Post-Communism Dummy variable equal to 1 for Eastern European countries and Authors’ own coding the regions of Germany that belonged to the former German Democratic Republic (GDR) Urban Density 1800 Number of people living in cities with a population above Bairoch et al. (1988); EUROSTAT 10,000 in 1800 relative to area (km ) (demo_r_d3area) Roman Road Hub Number of cities in a region located at a meeting point of two Bosker et al. (2013) or more Roman roads Bonding Social Capital First principal component of importance of both family and European Values Study friends averaged at the region of residence Bridging Social Capital Number of organizations an individual belongs to out of the European Values Study following list, averaged at the region of residence: religious organizations, cultural activities organization, youth work organizations, sports/recreation organizations and women’s groups Trust in National Share of the regional population trusting the national Eurobarometer 70.1 Government government Trust in Regional Share of the regional population trusting the regional or local Eurobarometer 70.1 Government public authorities SMEs Innovating in House Share of small and medium-sized enterprises (SMEs) with in- Regional Innovation Scoreboard, house innovation activities European Commission SMEs Innovating Share of SMEs that collaborate in innovation activities with Regional Innovation Scoreboard, Collaboratively other enterprises and institutions European Commission Audretsch, D., & Feldman, M. P. (1996). R&D spillovers and the REFERENCES geography of innovation and production. American Economic Review, 86(3), 630–640. Acemoglu, D., & Dell, M. (2010). Productivity differences between Bairoch, P., Batou, J., & Chevre, P. (1988). The population of and within countries. American Economic Journal: Macroeconomics, European cities, 800–1850. Geneva: Droz. 2(1), 169–188. doi:10.1257/mac.2.1.169 Barca, F. (2009). An agenda for reformed cohesion policy, a place based Akcomak, I. S., Webbink, D., & ter Weel, B. (2016). Why did the approach to meeting European Union challenges and Netherlands develop so early? The legacy of the brethren of the expectations (Independent report prepared at the request of the common life. Economic Journal, 126(593), 821–860. doi:10. European Commissioner for Regional Policy). Brussels: 1111/ecoj.12193 European Commission. Alesina, A., & Zhuravskaya, E. (2011). Segregation and the quality of Barro, R. J., & Lee, J. W. (2013). A new data set of educational government in a cross section of countries. American Economic attainment in the world, 1950–2010. Journal of Development Review, 101(5), 1872–1911. doi:10.1257/aer.101.5.1872 Economics, 104, 184–198. doi:10.1016/j.jdeveco.2012.10.001 Annoni, P., & Dijkstra, L. (2013). EU regional competitiveness Barro, R. J., & Sala-i-Martin, X. (1991). Convergence across states index (Discussion Paper). European Commission, Joint and regions. Brookings Papers on Economic Activity, 1991(1), Research Centre. 107–182. doi:10.2307/2534639 Anselin, L., Varga, A., & Acs, Z. (1997). Local geographic spillovers Becker, S. O., Egger, P. H., & von Ehrlich, M. (2013). Absorptive between university research and high technology innovations. capacity and the growth and investment effects of regional trans- Journal of Urban Economics, 42(3), 422–448. doi:10.1006/juec. fers: A regression discontinuity design with heterogeneous 1997.2032 REGIONAL STUDIES Regional economic development in Europe: the role of total factor productivity 475 treatment effects. American Economic Journal: Economic Policy, 5 Regional Studies, 46(10), 1401–1416. doi:10.1080/00343404. (4), 29–77. doi:10.1257/pol.5.4.29 2010.529288 Bernanke, B. S., & Gürkaynak, R. S. (2002). Is growth exogenous? Esteban, J. (2000). Regional convergence in Europe and the industry Taking Mankiw, Romer, and Weil seriously. In B. S. Bernanke mix: A shift–share analysis. Regional Science and Urban Economics, & K. Rogoff (Eds.), NBER macroeconomics annual (Vol. 16, pp. 30(3), 353–364. doi:10.1016/S0166-0462(00)00035-1 11–57). Cambridge, MA: MIT Press. European Commission. (2010). Europe 2020. A strategy for smart, Beugelsdijk, S. (2008). Strategic human resource practices and pro- suitable and inclusive growth. Brussels: European Commission. duct innovation. Organization Studies, 29(6), 821–847. doi:10. European Council. (2000). Presidency conclusions Lisbon European 1177/0170840608090530 Council. 23–24 March 2000. Brussels: European Council. Beugelsdijk, S., & Klasing, M. J. (2017). Measuring value diversity Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015). The next gen- within countries. In J. Costa-Font & M. Macis (Eds.), Social eration of the Penn World Table. American Economic Review, economics: Current and emerging avenues (pp. 129–172). 105(10), 3150–3182. doi:10.1257/aer.20130954 Cambridge, MA: MIT Press. Fischer, M. M., & Stirboeck, C. (2006). Pan-European regional Beugelsdijk, S., Klasing, M. J., & Milionis, P. (2017, in press). Value income growth and club-convergence. Annals of Regional diversity and regional economic development. Scandinavian Science, 40(4), 693–721. doi:10.1007/s00168-005-0042-6 Journal of Economics. doi:10.1111/sjoe.12253 Frenken, K., van Oort, F., & Verburg, T. (2007). Related variety, Beugelsdijk, S., & van Schaik, T. (2005). Social capital and growth in unrelated variety and regional economic growth. Regional European regions: An empirical test. European Journal of Political Studies, 41(5), 685–697. doi:10.1080/00343400601120296 Economy, 21(2), 301–324. doi:10.1016/j.ejpoleco.2004.07.004 Gallup, J. L., Sachs, J. D., & Mellinger, A. D. (1999). Geography and Beugelsdijk, S., & Smulders, S. (2003). Bonding and bridging social economic development. International Regional Science Review, capital: Which type is good for economic growth? In W. Arts, L. 22(2), 179–232. doi:10.1177/016001799761012334 Halman, & J. Hagenaars (Eds.), The cultural diversity of European Gardiner, B., Martin, R., & Tyler, P. (2004). Competitiveness, pro- unity (pp. 147–184). Leiden: Brill. ductivity and economic growth across the European regions. Bleakley, H., & Lin, J. (2012). Portage and path dependence. Regional Studies, 38(9), 1045–1067. doi:10.1080/ Quarterly Journal of Economics, 127(2), 587–644. doi:10.1093/ 0034340042000292638 qje/qjs011 Garofalo, G. A., & Yamarik, S. (2002). Regional convergence: Bloom, N., & van Reenen, J. (2007). Measuring and explaining manage- Evidence from a new state-by-state capital stock series. Review ment practices across firms and countries. Quarterly Journal of of Economics and Statistics, 84(2), 316–323. Economics, 122(4), 1351–1408. doi:10.1162/qjec.2007.122.4.1351 Gennaioli, N., La Porta, R., Lopez-de Silanes, F., & Shleifer, A. Bosker, M. (2009). The spatial evolution of regional GDP disparities (2013). Human capital and regional development. Quarterly in the ‘old’ and the ‘new’ Europe. Papers in Regional Science, 88(1), Journal of Economics, 128(1), 105–164. doi:10.1093/qje/qjs050 3–27. doi:10.1111/j.1435-5957.2008.00183.x Geppert, K., & Stephan, A. (2008). Regional disparities in the European Bosker, M., & Buringh, E. (2017, in press). City seeds: Geography Union: Convergence and agglomeration. Papers in Regional Science, and the origins of the European city system. Journal of Urban 87(2), 193–217. doi:10.1111/j.1435-5957.2007.00161.x Economics. Gollin, D., Parente, S., & Rogerson, R. (2002). The role of agricul- Bosker, M., Buringh, E., & van Zanden, J. L. (2013). From Baghdad ture in development. American Economic Review Papers and to London: Unraveling urban development in Europe, the Proceedings, 92(2), 160–164. Middle East, and North Africa, 800–1800. Review of Economics Gunton, T. (2003). Natural resources and regional development: An and Statistics, 95(4), 1418–1437. doi:10.1162/REST_a_00284 assessment of dependency and comparative advantage paradigms. Brakman, S., Garretsen, H., & van Marrewijk, C. (2009). Economic Economic Geography, 79(1), 67–94. doi:10.1111/j.1944-8287. geography within and between European nations: The role of mar- 2003.tb00202.x ket potential and density across space and time. Journal of Regional Hall, R. E., & Jones, C. I. (1999). Why do some countries produce so Science, 49(4), 777–800. doi:10.1111/j.1467-9787.2009.00633.x much more output per worker than others? Quarterly Journal of Capello, R., & Lenzi, C. (2015). Knowledge, innovation and pro- Economics, 114(1), 83–116. doi:10.1162/003355399555954 ductivity gains across European regions. Regional Studies, Henderson, J. V., Kuncoro, A., & Turner, M. (1995). Industrial 49(11), 1788–1804. doi:10.1080/00343404.2014.917167 development in cities. Journal of Political Economy, 103(5), Caselli, F. (2005). Accounting for cross-country income differences. 1067–1090. doi:10.1086/262013 In P. Aghion & S. N. Durlauf (Eds.), Handbook of economic Hsieh, C.-T. (2002). What explains the industrial revolution in East growth (vol. 1A, pp. 679–741). Amsterdam: Elsevier. Asia? Evidence from the factor markets. American Economic Charron, N., Dijkstra, L., & Lapuente, V. (2015). Mapping the Review, 92(3), 502–526. doi:10.1257/00028280260136372 regional divide in Europe: A measure for assessing quality of gov- Hsieh, C.-T., & Klenow, P. J. (2010). Development accounting. ernment in 206 European regions. Social Indicators Research, American Economic Journal: Macroeconomics, 2(1), 207–223. 122(2), 315–346. doi:10.1007/s11205-014-0702-y doi:10.1257/mac.2.1.207 Ciccone, A. (2002). Agglomeration effects in Europe. European Huber, F. (2012). On the role and interrelationship of spatial, social Economic Review, 46(2), 213–227. doi:10.1016/S0014-2921 and cognitive proximity: Personal knowledge relationships of (00)00099-4 R&D workers in the Cambridge information technology cluster. Ciccone, A., & Hall, R. E. (1996). Productivity and the density of Regional Studies, 46(9), 1169–1182. doi:10.1080/00343404. economic activity. American Economic Review, 86(1), 54–70. 2011.569539 Corrado, L., Martin, R., & Weeks, M. (2005). Identifying and interpret- Inklaar, R., & Timmer, M. P. (2013). Capital, labor and TFP in ing regional convergence clusters across Europe. Economic Journal, PWT8.0 (Mimeo). Groningen: University of Groningen. 115(502), C133–C160. doi:10.1111/j.0013-0133.2005.00984.x Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic Davis, D. R., & Weinstein, D. E. (2002). Bones, bombs, and localization of knowledge spillovers as evidenced by patent cita- break points: The geography of economic activity. American tions. Quarterly Journal of Economics, 108(3), 577–598. doi:10. Economic Review, 92(5), 1269–1289. doi:10.1257/ 2307/2118401 000282802762024502 Jorgenson, D. W., & Stiroh, K. J. (2000). Raising the speed limit: U.S. Dettori, B., Marrocu, E., & Paci, R. (2012). Total factor productivity, economic growth in the information age. Brookings Papers on intangible assets and spatial dependence in the European regions. Economic Activity, 2000(1), 125–210. doi:10.1353/eca.2000.0008 REGIONAL STUDIES 476 Sjoerd Beugelsdijk et al. Keller, W. (2004). International technology diffusion. Journal of Monetary Economics, 55(2), 234–250. doi:10.1016/j.jmoneco. Economic Literature, 42(3), 752–782. doi:10.1257/ 2007.11.006 0022051042177685 Rice, P., Venables, A. J., & Patacchini, E. (2006). Spatial determi- Klenow, P. J., & Rodríguez-Clare, A. (1997). The neoclassical revival nants of productivity: Analysis for the regions of Great Britain. in growth economics: Has it gone too far? In B. S. Bernanke & J. Regional Science and Urban Economics, 36(6), 727–752. doi:10. J. Rotemberg (Eds.), NBER macroeconomics annual (Vol. 12, pp. 1016/j.regsciurbeco.2006.03.006 73–103). Cambridge, MA: MIT Press. Rodríguez-Pose, A. (1999). Innovation prone and innovation averse Knack, P., & Keefer, P. (1997). Does social capital have an economic societies: Economic performance in Europe. Growth and payoff? A cross country investigation. Quarterly Journal of Change, 30(1), 75–105. doi:10.1111/0017-4815.00105 Economics, 112(4), 1251–1288. Rodríguez-Pose, A., & Crescenzi, R. (2008). Research and develop- Krugman, P. R. (1991). Increasing returns and economic geography. ment, spillovers, innovation systems, and the genesis of regional Journal of Political Economy, 99(3), 483–499. doi:10.1086/261763 growth in Europe. Regional Studies, 42(1), 51–67. doi:10.1080/ Krugman, P. R. (1993). First nature, second nature, and metropolitan 00343400701654186 location. Journal of Regional Science, 33(2), 129–144. doi:10.1111/ Rodríguez-Pose, A., & Garcilazo, E. (2015). Quality of government j.1467-9787.1993.tb00217.x and the returns of investment: Examining the impact of cohesion Marrocu, E., Paci, R., & Usai, S. (2013). Productivity growth in the expenditure in European regions. Regional Studies, 49(8), 1274– old and new Europe: The role of agglomeration externalities. 1290. doi:10.1080/00343404.2015.1007933 Journal of Regional Science, 53(3), 418–442. doi:10.1111/jors. Sala-i-Martin, X. X. (1996). Regional cohesion: Evidence and the- 12000 ories of regional growth and convergence. European McCann, P., & Ortega-Argilés, R. (2015). Smart specialization, Economic Review, 40(6), 1325–1352. doi:10.1016/0014-2921 regional growth, and applications to European union cohesion (95)00029-1 policy. Regional Studies, 49(8), 1291–1302. doi:10.1080/ Tabellini, G. (2010). Culture and institutions: Economic develop- 00343404.2013.799769 ment in the regions of Europe. Journal of the European Neffke, F., Henning, M., & Boschma, R. A. (2011). How do regions Economic Association, 8(4), 677–716. doi:10.1111/j.1542-4774. diversify over time? Industry relatedness and the development of 2010.tb00537.x new growth paths in regions. Economic Geography, 87(3), 237– Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R., & de 265. doi:10.1111/j.1944-8287.2011.01121.x Vries, G. J. (2015). An illustrated user guide to the world Oliner, S. D., & Sichel, D. E. (2000). The resurgence of growth in input–output database: The case of global automotive production. the late 1990s: Is information technology the story? Journal of Review of International Economics, 23(3), 575–605. doi:10.1111/ Economic Perspectives, 14(4), 3–22. doi:10.1257/jep.14.4.3 roie.12178 Psacharopoulos, G. (1994). Returns to investment in education: A Van Ark, B., O’Mahony. M.., & Timmer, M. P. (2008). The pro- global update. World Development, 22(9), 1325–1343. doi:10. ductivity gap between Europe and the United States: Trends 1016/0305-750X(94)90007-8 and causes. Journal of Economic Perspectives, 22(1), 25–44. Quah, D. T. (1996). Empirics for economic growth and convergence. doi:10.1257/jep.22.1.25 European Economic Review, 40(6), 1353–1375. doi:10.1016/ Vieira, E., Neira, I., & Vázquez, E. (2011). Productivity and inno- 0014-2921(95)00051-8 vation economy: Comparative analysis of European NUTS II, Rauch, J. E. (1993). Productivity gains from geographic 1995–2004. Regional Studies, 45(9), 1269–1286. doi:10.1080/ concentration of human capital: Evidence from the cities. 00343404.2010.486781 Journal of Urban Economics, 34(3), 380–400. doi:10.1006/juec. Weidmann, N. B., Rod, J. K., & Cederman, L.-E. (2010). 1993.1042 Representing ethnic groups in space: A new dataset. Journal Redding, S., & Venables, A. J. (2004). Economic geography and of Peace Research, 47(4), 491–499. doi:10.1177/0022343310 international inequality. Journal of International Economics, 368352 62(1), 53–82. doi:10.1016/j.jinteco.2003.07.001 Young, A. (1995). The tyranny of numbers: Confronting the statisti- Restuccia, D., Yang, D. T., & Zhu, X. (2008). Agriculture and aggre- cal realities of the East Asian growth experience. Quarterly Journal gate productivity: A quantitative cross-country analysis. Journal of of Economics, 110(3), 641–680. doi:10.2307/2946695 REGIONAL STUDIES

Journal

Regional StudiesTaylor & Francis

Published: Apr 3, 2018

Keywords: regional economic development; total factor productivity; development accounting; European regions; 区域经济发展; 全要素生产力; 发展会计; 欧洲区域; aménagement du territoire; productivité globale des facteurs; comptabilité de développement; régions européennes; regionale Wirtschaftsentwicklung; Gesamtfaktorproduktivität; Entwicklungsbilanzierung; europäische Regionen; desarrollo económico regional; productividad total de los factores; contabilidad del desarrollo; regiones europeas; O18; O47; O52; R10

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