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Interregional Knowledge Spillovers and Economic Growth: The Role of Relational Proximity

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Drivers of Innovation, Entrepreneurship and Regional Dynamics

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

Standard neoclassical growth models (Solow 1956; Mankiw et al. 1992) implicitly assume that the technological progress is characterized by a worldwide global interdependence between economies without frictions. In contrast, recent mainstream contributions to the economic growth literature (Lòpez-Bazo et al. 2004; Ertur and Koch 2007) support the idea that technological interdependence is not homogenous across economies (countries or regions) and depends on their geographical connectivity scheme with other economies, which adds to reflections already envisaged in previous studies (Acs et al. 1994; Anselin et al. 2000). An important feature of technology is its aptitude to spread across borders (Coe and Helpman 1995, and Eaton and Kortum 1996, among others). However, the spatial diffusion of technological knowledge may be geographically bounded, so that the stock of knowledge in one region may spill over into other regions with an intensity which decreases with geographical distance (the so-called “spatial friction” hypothesis).

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Notes

  1. 1.

    A collective learning process of this kind was first hypothesized by the GREMI group (Camagni 1991; Perrin 1995) and subsequently widely adopted as a sound theoretical concept for the interpretation of knowledge-based development and innovation (Keeble and Wilkinson 1999, 2000; Capello 1999; Cappellin 2003).

  2. 2.

    Namely, sector DA (food products, beverages and tobacco), DB (textile and textile products), DC (leather and leather products), DD (wood and wood products), DE (pulp, paper and paper products; publishing and printing), DF (coke, refined petroleum products and nuclear fuel), DG (chemicals, chemicals products, and man-made fibres), DH (rubber and plastic products), DI (other non-metallic mineral products), DJ (basic metals and fabricated metal products), DK (machinery and equipment n.e.c.), DL (electrical and optical equipment), DM (transport equipment) and DN (manufacturing n.e.c.).

  3. 3.

    Since the Balassa index follows an asymmetric distribution (with a fixed lower bound, 0, and a variable upper bound, \( E/{E_i} \)), its median turns out to be the most appropriate indicator of the distribution position. When the median is low, an economy shows a comparative advantage in a large share of sectors and its productive structure is therefore diversified, and vice versa. So, we use the median as a direct measure of diversification.

  4. 4.

    EVS is among the widest surveys comprising statistical information from individual questionnaires on the values of European citizens. This paper uses its 1990 wave, which perfectly matches the initial year of our estimations. More information can be found on www.europeanvalues.nl

  5. 5.

    This amounts to 37,107 cases, with just 1,106 individuals missing; hence, the question had a reply rate of about 97% of the individuals interviewed. The EVS sample was drawn from the population of adult citizens over 18 years of age. In some countries, random sampling was applied, in others quota sampling. The samples were weighted to correct for gender and age: the survey, therefore, correctly represents the population of each region.

  6. 6.

    The actual range of the variable goes from 0.03 (recorded for Sardinia) to 0.64 (corresponding to Sydsverige).

  7. 7.

    Glaeser et al. (2000) reviews the use of the EVS trust question. They find the question may also capture the level of trustworthiness of individuals, while also detecting high correlations among the EVS trust level measured within the survey and the outcome of two experiments aiming at identifying trust behaviors.

  8. 8.

    Notice that, as easily detectable from the Figure, and clear from the social distance formula, the indicator takes on value zero (whatever the sum of trust levels in the regions) when the numerator is zero. This may, however, happen for all the regions sharing the same level of trust. For such regions, it does not matter whether they share a high, medium or low level of trust: our indicator scores zero anyway. This is shown in Figure 1 with the dots on the xy plane.

  9. 9.

    The same conclusions could be obtained mathematically. In fact, both the numerator as well as the denominator of the social distance measure are of first order, thus converge asymptotically with the same speed; besides, they both map on the positive half of the real numbers. This line of reasoning is behind the shape of the plot depicted in Fig. 1.

  10. 10.

    Moran’s I tests have been performed using distance-based binary spatial weights matrices. Many distance cut-offs, ranging from 420 km (the minimum distance which allows all regions to have at least one neighbour) to 1,020 km with a step of 50 km, have been adopted. All corresponding spatial weights matrices yield significant values of Moran’s I. The highest standardized Moran’s I value occurred in correspondence to the minimum distance.

  11. 11.

    In linear spatial regression analysis, Kelejian and Prucha (1998) have proposed a 2SLS procedure to estimate the spatial autocorrelation regression model and have suggested using spatial lags of the strictly exogenous variables as instruments.

  12. 12.

    Prior to the main tested hypotheses, we adopted a from-particular-to-general specification strategy to choose the most suitable specification. The first step entails estimating a basic human-capital augmented neoclassical model à la Mankiw et al. (1992). Next, the hypothesis of linearity and of spatial independence is relaxed, as residuals of the first OLS estimates display spatial autocorrelation. We therefore estimated a spatial Durbin model (and a spatial lag) model, à la Ertur and Koch (2005, 2007) and Basile (2008, 2009). Finally, we augment the spatial lag specification by incorporating social proximity effects. This section presents the econometric results of the preliminary two steps, while the effect of social proximity is analyzed in Sect. 4.2.

  13. 13.

    The plots of these smooth terms are not reported in the paper, but they are available upon request.

  14. 14.

    Keywords on DG Regios’ website as of May 5, 2010 include the following terms: “Beneficiaries”, “Future of Cohesion Policy”, “Territorial Cohesion”, “Territorial Co-operation”, “Closure 2006”, “RegioStars”, “Economic crisis”, “Cohesion reports”, “Danube strategy”, and “Ex Post Evaluation 2000–2006”.

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Authors and Affiliations

  1. ISAE (Institute for Studies and Economic Analyses), Piazza dell’Indipendenza, 4, 00185, Rome, Italy

    Roberto Basile

  2. Dipartimento BEST, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy

    Roberta Capello & Andrea Caragliu

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  1. Roberto Basile
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  2. Roberta Capello
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  3. Andrea Caragliu
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Correspondence to Andrea Caragliu .

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Editors and Affiliations

  1. , Department of Spatial Economics, VU University Amsterdam, De Boelelaan 1105, Amsterdam, 1081 HV, Noord-Holland, Netherlands

    Karima Kourtit

  2. , Department of Spatial Economics, VU University Amsterdam, De Boelelaan 1105, Amsterdam, 1081 HV, Noord-Holland, Netherlands

    Peter Nijkamp

  3. School of Public Policy, George Mason University, University Drive 4400, Fairfax, 22030, Virgin Islands, USA

    Roger R. Stough

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Basile, R., Capello, R., Caragliu, A. (2011). Interregional Knowledge Spillovers and Economic Growth: The Role of Relational Proximity. In: Kourtit, K., Nijkamp, P., Stough, R. (eds) Drivers of Innovation, Entrepreneurship and Regional Dynamics. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17940-2_2

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