Climbing the ladder of technological development

Abstract

Despite being the main thriving force behind economic growth and industrial development, technological innovation remains highly concentrated on a handful of countries. It is therefore of a great interest to know how countries accumulate and develop their innovative capabilities, what kind of obstacles they need to overcome, and whether it is possible to identify opportunities to develop new areas of technological specialization. In this paper we analyze countries' patterns of technological diversification and specialization along the development process. We provide evidence regarding the importance of existing technological capabilities and the relationship among technologies in shaping possible paths of technological development. We show that the likelihood of diversification is higher for those technologies that are related to countries' existing profile of competences. Moreover, we show this effect to be stronger at earlier stages of development. Additionally, we show that countries tend to follow clear patterns of specialization along the development path, by moving towards more complex and valuable technologies.

Optimal diversification strategies in the networks of related products and of related research areas

“…While many mechanisms have been proposed in the past to explain such agglomerations, knowledge diffusion has emerged as one of the main drivers of related activities at multiple scales, from cities, to regions [3], to countries [1]. The main finding supporting this idea is the fact that the probability of entering an activity increases with the number of related activities present in a location, or in nearby locations, probably because similar activities require similar inputs (which are most likely, knowledge based) [1,2,3,4,5,6,8,7,28,29,30,31]. Yet, despite the prevalence of this relationship, little is known about the strategies that are optimal to maximize diversification when knowledge diffusion is constrained by a network of related activities.…”

Related to what? Reviewing the literature on technological relatedness: Where we are now and where can we go?

“…Conversely, coordinated-market economies which follow more rigid guidelines typically diversify into related activities as these economies emulate what has proven to be successful in the past. This trend has also been shown to hold for higher income countries suggesting that when a country reaches a certain level of economic prosperity, they are then capable of diversifying into more unrelated activities (Petralia et al, 2017). Pinheiro et al (2018) find evidence to this effect demonstrating that it is only when a country reaches the intermediate stage of development can they diversify into unrelated products.…”

“…The writings on relatedness and regional diversification are currently fueling some of the most exciting debates within economic geography (Kogler, 2015), development economics (Petralia et al, 2017) and regional studies (Boschma, Coenen, Frenken, & Truffer, 2016). Treating relatedness as a spatial independent construct, the three relatedness measureshierarchical, co-occurrence and resource-basedand the four networks of space derived therefromproduct, knowledge, industry and skillhave served to greatly improve our understanding of how actors are cognitively predisposed to diversify into those areas that are related to their current specialization.…”

“…in the production of new knowledge (Petralia, Balland, & Morrison, 2017). The primacy of this divide has brought the inescapable importance of the region to the forefront of the debate, given that the ability of regions to successfully diversify and enter new economic activities is strongly conditioned by their existing knowledge base (Kogler, Essletzbichler, & Rigby, 2017;Kogler, Rigby, & Tucker, 2013;Rigby & Essletzbichler, 1997).…”

Knowledge networks and industrial structure for regional innovation: An analysis of patents collaborations in Italy

“…In fact, thanks to the work of Fleming et al (2007b) and Li et al (2014), it was possible to elaborate a disambiguation algorithm to uniquely identify individual inventors in the US. An increasing number of contributions then started to focus on the structure of knowledge networks of inventors in US cities (Petralia, Balland, & Morrison, 2017).…”