PROMOTING REGIONAL GROWTH AND INNOVATION: RELATEDNESS, REVEALED COMPARATIVE ADVANTAGE AND THE PRODUCT SPACE

We adapt the product-space approach of Hausmann–Hidalgo et al. to the case of Italian provinces, examining the extent to which network connectedness and centrality of a province’s exports is related to its economic performance. We construct a new Product Space Position (PSP) index which retains many of the Hausmann–Hidalgo et al. features but which is also much better suited to handling regional and provincial data. The PSP index is found to outperform other indices. Our comparison throws light on fundamental aspects of network-cognitive-distance-trade arguments. A better positioning in the export-network product space is indeed associated with better local economic outcomes.

1. Introduction

The centrality, positioning and connectedness of a nation’s tradeable sectors within global trade patterns are argued to be critical for a country’s growth trajectories (Hausmann and Klinger, 2006; Hausmann et al., 2007; Hidalgo et al., 2007; Hidalgo and Hausmann, 2009), and similar arguments have also been put forward at the regional scale (Neffke et al., 2011). The underpinnings of this Hausmann–Hidalgo approach are based on widely held principles evident in fields such as economics, strategic management, international business and economic geography. Yet, while these approaches are useful for distinguishing between the development performance of rich, middle-income and poor countries, as we will demonstrate in this paper, the existing approaches not only have very limited powers to distinguish empirically between the development trajectories of different rich countries but they are even less well-adapted to examining the case of diversified regions within advanced economies. This would suggest that for such an approach to make a contribution to regional analysis in advanced economies, at the very least it would need to be adapted in a way which keeps the main underlying principles but does so in a more appropriate manner. Our research question is therefore, is the Hausmann–Hidalgo type of approach to trade centrality and connectedness still useful for understanding the economic performance of advanced regions, and if so, can a significant adaptation of the existing frameworks better capture the economic performance of regions in advanced economies?

In order to answer this question, we investigated the development role played by the positioning and connectedness of a region’s export patterns within the overall international trade system, over and above standard economic geography variables. Using province-level data from Italy, our analysis demonstrates that the existing Hausmann–Hidalgo types of approaches which are used to examine the performance of countries are less effective when discussing sub-national regional profiles in advanced economies. We therefore put forward a method for modifying the existing Hidalgo–Hausmann national-level indicators of trade network-relatedness and centrality (Hausmann and Klinger, 2006,, 2007; Hidalgo et al., 2007; Hausmann et al., 2007; Hidalgo and Hausmann, 2009) in order to produce an index which is place-specific and much better suited to sub-national analyses. This new modified PSP index is shown to perform better than the existing Hausmann and Klinger (2006), Hausmann and Klinger (2007), Hidalgo et al. (2007), Hausmann et al. (2007) and Hidalgo and Hausmann (2009) indices, while still maintaining many of the features of the product-space method. Importantly, by using this new index we find that the original Hausmann–Hidalgo et al. type arguments do hold at the sub-national scale, even after controlling for more traditional regional growth factors.

This paper is structured as follows. Within the product space framework the next section discusses the interconnected ideas of relatedness, centrality and connectedness. By drawing on broader insights from other Hausmann–Hidalgo et al. papers we are then able to adapt and extend the methodological approach of Hausmann and Klinger (2006) to a wider context more suitable for addressing regional variations within advanced economies. We then apply our measure to an analysis of the economic and innovative performance of Italian provinces for the years 2007–12. Our analysis shows that in such a context this modified approach makes much more theoretical and empirical sense than the existing indices. Our findings demonstrate that a province’s good positioning in the export network product space is indeed associated with enhanced regional development, over and above other more traditional regional economic variables such as variety, diversity, human capital and density.

2. Product and technological relatedness and network centrality

The product and network space arguments of Hausmann and Klinger (2006), Hidalgo et al. (2007) and Hidalgo and Hausmann (2009) suggest that within the overall global networks of trade countries which are represented relatively more in centrally located export activities are more likely to exhibit stronger growth and developments trajectories than countries which are more represented by the exporting of more peripheral products. This product-space approach is common to the arguments of Hausmann and Klinger (2006), Hidalgo et al. (2007) and Hidalgo and Hausmann (2009) and the conceptual foundations of the Hausmann–Hidalgo approach are 2-fold.

To begin with, their analysis posits that where two products or services share most of the same requisite production assets and capabilities, countries that export one will also tend to export the other. By the same token, goods or services that do not share many capabilities are less likely to be co-exported. As with the related variety literature (Frenken et al., 2007; Boschma and Iammarino, 2009; Neffke et al., 2011) their fundamental conceptual ideas reflect the cognitive distance argument of Boschma (2005) in which it is assumed that greater cognitive proximity between products or services, defined in terms of the common production assets, competences and capabilities required, also offer greater possibilities for mutual technology transfer, learning and knowledge sharing. In turn all of these cognitive distance arguments originally derive from the various innovation-systems literatures (Iammarino and McCann, 2013). However, there are also fundamental differences in construction between the entropy-based related variety approach and the network-proximity approach of Hausmann–Hidalgo. The proximity indices measure the relatedness between two products by observing trade outcomes rather than the ex ante (sectoral classification) similarities between the products or inputs. Therefore, in contrast to the conventional related variety approach the new indicator is an ex post measure of relatedness, and should better capture all of the influences similarly affecting groups of industries. Indeed, the tentative evidence available suggests that the network proximity approach may actually perform better empirically than the conventional related variety approach (Boschma et al., 2012).

The product proximity index that Hausmann and Klinger (2006) propose is therefore a measure of the relatedness between pairs of products using cross-country export data. It is also a measure of the product-space distance between products, and one which avoids any priors as to the relevant dimensions of similarity. The similarity of requisite production assets and capabilities is revealed by the likelihood that where a country has a revealed comparative advantage (RCA) based on a Balassa Index (Balassa 1965) value of >1 in the exporting of one good, it will tend to have such an advantage in both goods.

Yet, these relatedness properties are themselves not sufficient to ensure strong development trajectories. Rather, the product-space framework also posits that countries with a revealed comparative advantage in groups of sectors which are centrally positioned within global trade networks will exhibit higher levels of economic development than those whose revealed comparative advantage is in sectors which are more peripherally positioned. The reason is that these products offer greater possibilities for technology transfer, learning and knowledge sharing. On average, core products are the most sophisticated and well-connected to the rest of the product space, and provide more opportunities to redeploy the capabilities that they embody, which facilitates the export of a large number of other products. The degree of centrality of a country’s related exports in global trade networks is therefore critical in determining its long-term development trajectory, and the more centrally positioned are a country’s exports the stronger will be its development trajectory.

Applying the Hausmann–Klinger (AVERAGE CENTRALITY) methodology to the Italian provinces data for 2012 yields results which are rather curious.² Using the AVERAGE CENTRALITY index, we see that Italian provinces with higher values tend to be higher GDP regions (ρ = 0.307, R² = 0.094), but the relationship is very weak indeed. Moreover, many poorer southern Italian regions are ranked above rich areas such as Bolzano. A low income province such as Teramo is ranked above a high income province such as Padua, but this cannot be due to different specialization patterns because the same strange rankings are evident even between regions showing RCA in the same number of export sectors such as high income La Spezia and low income Sassari. The same picture is evident for other years of data.