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Explaining the dynamics of relatedness: The role of co‐location and complexity

2020; Elsevier BV; Volume: 100; Issue: 1 Linguagem: Inglês

10.1111/pirs.12567

1435-5957

Sándor Juhász, Tom Broekel, Ron Boschma,

Regional resilience and development

Relatedness has become a key concept for studying the diversification of firms, regions and countries. However, studies tend to treat relatedness as being time-invariant or, alternatively, consider its evolution as exogenously given. This study argues that relatedness is inherently dynamic and endogenous to technological and economic developments. Using patent data, we test the extent to which relatedness between technologies developed along co-location and differences in technological complexity in 1980–2010. Our results show that co-located technologies are more likely to become related over time. Moreover, our results suggest that co-location and complexity of technologies are conducive to the intensification of relatedness over time. El grado de relación se ha convertido en un concepto clave para el estudio de la diversificación de las empresas, las regiones y los países. Sin embargo, los estudios sobre el tema tienden a tratar el grado de relación como invariable en el tiempo o, alternativamente, a considerar su evolución como algo exógeno. En este estudio se sostiene que el grado de relación es inherentemente dinámico y endógeno respecto a los avances tecnológicos y económicos. Mediante el uso de datos de patentes, este estudio comprueba hasta qué punto se ha desarrollado el grado de relación entre las tecnologías desarrolladas bajo una misma ubicación y bajo diferencias de complejidad tecnológica entre 1980 y 2010. Los resultados muestran que las tecnologías ubicadas en el mismo lugar tienen mayores probabilidades de estar relacionadas a medida que pasa el tiempo. Además, nuestros resultados sugieren que la coubicación y la complejidad de las tecnologías favorecen la intensificación del grado de relación a lo largo del tiempo. 関連性は、企業、地域、国の多様化を研究する上で重要な概念となっている。しかし、研究においては、関連性は時間的に不変であるか、あるいはその関連性の進化は外生的に与えられたものとして捉える傾向がある。本稿では、関連性は本質的に変化するものであり、技術的・経済的発展に対して内生的であることを論じる。特許データを用いて、コロケーション (co-location)とともに開発された技術間の関連性の程度と、1980~2010年における技術的な複雑さの違いを検証した。結果から、コロケーション技術は時間の経過とともに関連性が強くなる可能性が高いことが示された。さらに、結果から、コロケーションと技術の複雑さが時間の経過とともに関連性を強化することにつながることが示唆された。 The relatedness concept has drawn a lot of attention in management studies, economic geography, and innovation studies (Boschma, 2017; Hidalgo et al., 2018; Teece, Rumelt, Dosi, & Winter, 1994). Relatedness refers to the fact that two activities (technologies, industries, products, jobs) are not identical but share commonalities. Such commonalities may originate from two activities belonging to the same overarching technological or economic field, or they share complementarities and similarities (Broekel & Brachert, 2015; Nooteboom, 2000). There is ample evidence that relatedness of technologies conditions whether a technology will be invented or adopted by a firm, region or country. Moreover, relatedness has been shown to influence performance and diversification of firms (Fornahl, Broekel, & Boschma, 2011; Neffke, Henning, & Boschma, 2012), regions (Frenken, van Oort, & Verburg, 2007; Neffke, Henning, & Boschma, 2011), and nations (Hidalgo, Klinger, Barabási, & Hausmann, 2007). Recently, relatedness has also found its way into policy as an evaluation and benchmarking dimension (Balland, Boschma, Crespo, & Rigby, 2019; Fitjar, Benneworth, & Asheim, 2019). However, few studies have analysed relatedness as a dependent variable, and how relatedness changes and evolves over time. In fact, in most of the empirical studies, relatedness is either treated as time-invariant, or as an exogenously given component. While one might argue that relatedness is rather stable in the short run, this is less likely to be the case in the long-run (Balland, Boschma, & Frenken, 2015; Boschma, 2017; Broekel, 2015). Consequently, so far we know little about how (technological) relatedness comes into existence, and how it develops over time (Menzel, 2015). This gap motivates the present paper studying the emergence and evolution of technological relatedness. More precisely, we evaluate two dimensions' relations with the emergence of technological relatedness and its intensification over time: co-location and complexity. Making use of patent data, our empirical findings suggest that co-location of technology pairs support the emergence of relatedness in Europe during the period 1980–2010. Moreover, both co-location and the level of complexity are correlated to the intensification of relatedness over time. The paper is organized as follows. Section 2 provides a short introduction to the concept of relatedness and its possible interplay with co-location and complexity. Section 3 introduces the data, key variables, and modelling approaches. Section 4 presents and discusses the empirical findings, while Section 5 contains several robustness checks. The paper concludes with a discussion that outlines limitations, implications, and possibilities for future research. Relatedness has become a key input to outline possible technological and economic re-combination and diversification opportunities in particular. There is a consensus that the probabilities of firms, regions and countries to enter new and specific activities is a function of the number of the related activities they are specialized in (Boschma, Balland, & Kogler, 2015; Hidalgo et al., 2018; Neffke et al., 2011). Similarly, regions and countries with access to related variety tend to outperform economically those that are either highly specialized or overly diversified (Fornahl et al., 2011; Frenken et al., 2007; Hidalgo et al., 2007). Thereby, relatedness refers to two activities (e.g., technologies, industries, occupations, research fields) being based on similar underlying knowledge, skills or other inputs (Boschma, 2017; Hidalgo et al., 2018). The present paper focuses on technological relatedness, which is defined as the degree to which two technologies are proximate in a technological or cognitive dimension (Boschma et al., 2015; Breschi, Lissoni, & Malerba, 2003; Kogler, Rigby, & Tucker, 2013; Rigby, 2015). In the quickly growing literature using (technological) relatedness as a key explanatory variable, most studies put little emphasis on relatedness as an evolving property. In fact, we know surprisingly little about how relatedness comes into existence, and how it develops over time (Balland et al., 2015; Menzel, 2015). In most studies on (related) diversification, relatedness is being treated as time-invariant or exogenously given. For instance, relatedness is frequently added to the empirical models as an independent variable that is constant over the observed time period (e.g., Breschi et al., 2003; Frenken et al., 2007; Neffke et al., 2011). This assumption seems reasonable when considering short time-periods, but it is much less likely to be the case over the long run (Balland et al., 2015; Boschma, 2017; Cowan, Jonard, & Zimmermann, 2007), and in particular when technological paradigms shift (Dosi, 1982) and thereby reshuffle the technology space (Rigby, 2015). This is exemplified by the rise of electronics and biotech industries in recent years (Krafft, Quatraro, & Saviotti, 2011, 2014). Notably, in a number of studies, relatedness is modelled as being time-variant, that is, it may change between time periods (e.g., Boschma et al., 2015; Mewes & Broekel, 2020; Rigby, 2015). However, none of these studies systematically discuss how relatedness emerges and evolves over time. Nevertheless, there are several ideas that give a hint about what factors and conditions might be important for the emergence and development of relatedness. The relevance of geographical proximity is frequently highlighted to enhance knowledge spillovers between economic agents (Audretsch & Feldman, 1996; Jaffe, Trajtenberg, & Henderson, 1993). Geographical co-location of actors facilitates localized learning processes and lays the foundation for technological (re-)combinations. Consequently, co-located actors with expertise in different technologies are more likely to engage in knowledge exchange. It increases the likelihood that they learn from each other, which may eventually (in some instances) even lead to the (re-)combination of distinct technological knowledge. In the latter case, their geographical proximity may have helped in growing the relatedness of their respective fields of expertise (technologies). These arguments fit well those of Jacobs (1969), who argues that the diversity of ideas, knowledge, and technologies in cities (i.e., within the geographic vicinity) enables and triggers cross-fertilization and knowledge re-combination. Put differently, the co-location of experts with specific knowledge on technologies increases the chances that connections may be discovered between previously unrelated technologies and that complementarities are identified, which in turn create or intensify relatedness (Boschma, 2017; Broekel & Brachert, 2015; Desrochers & Leppälä, 2011). This is summarized in the following two hypotheses: Hypothesis 1A.Co-location enhances the emergence of relatedness between technologies. Hypothesis 1B.: Co-location enhances the intensification of relatedness between technologies. In addition to geographical proximity, we argue that another dimension which is of relevance in this context is complexity, as it is likely to influence knowledge combinatorial processes. The complexity of a technology is often understood as a function of the number of its (sub-)components and their interdependencies (Fleming & Sorenson, 2001; Simon, 1962). Combining two technologies requires a basic understanding and the mastering of both. Gaining such an understanding is more difficult the more complex technologies are (Balland & Rigby, 2017; Fleming & Sorenson, 2001; Hidalgo & Hausmann, 2009; Sorenson & Fleming, 2004). When combining multiple technologies, these difficulties will scale up with the number of technologies. It can also be expected that it is more difficult to combine two complex technologies than it is to combine a simple technology with a complex one. One reason is that to combine multiple technologies, mastery in all involved is required, implying that the necessary learning efforts are cumulative. Another reason is that the result of the combinatorial activity, that is, an innovation or new technology, is most probably at least as complex as the most complex technology it is based upon. Consequently, combining two technologies and mastering the result thereof, go beyond the sum of the efforts required to obtain mastery in its constitutive technological elements. Significantly, this will even hold when the combinatorial process is conducted by a team. Members of the team must have a basic understanding of the technologies involved, which, as argued above, is more difficult in the cases of more complex technologies. Moreover, complex technologies are made of heterogeneous and relatively unrelated (sub-)components implying they are characterized by a greater knowledge diversity (Broekel, 2019). This implies that larger cognitive distances need to be bridged within the team. In turn, it makes co-ordination, communication and learning harder, which translates into greater efforts and higher costs of collaboration (Gross & McMullen, 1982; Lundvall, 1993; von Hippel, 1987). Consequently, due to its "cost-and-efforts" driving nature, the complexity of technologies will have a negative impact on the likelihood and frequency of the technologies being combined. Yet, this relation might be countered by economic reasons. Technologies have a value that reflects their supply and demand. Technologies that are simple to learn can diffuse easily between economic agents through re-invention, reverse engineering, or copying. Hence, they offer little potential for competitive advantage and consequently they have relatively little economic value (Maskell & Malmberg, 1999). Concomitantly, technologies that are more difficult to replicate and imitate – which particularly applies to complex technologies – will not diffuse easily. Complex technologies therefore offer a high potential for competitive advantage and thus, a higher level of socio-economic value (Fleming & Sorenson, 2001). In addition, Hidalgo and Hausmann (2009) argue that complex technologies are crucial stepping stones for acquiring additional technological competences in the future. This adds further to their value. Accordingly, while complex technologies and their combinations are more likely to be associated with higher economic rewards (Dalmazzo, 2002; Hidalgo & Hausmann, 2009), they are also characterized by greater difficulties in being understood and combined. Considering this, we expect complexity to act as an (initial) barrier to technological combinations and thereby to the emergence of relatedness. When two technologies have not previously been combined, the (economic) potential of their combination is unknown, which will lower the actors' willingness and likelihood to explore it. This will change once this barrier has been overcome and the hidden potential becomes clearer. In this case, the potentially higher social-economic benefits associated with combinations of complex technologies should attract the interest of the actors and motivate them to invest. Consequently, there will be greater exploration of such combinations and quicker convergence of the two technologies in terms of their relatedness. In sum, complexity represents a dimension of technologies that is likely to impact the likelihood and frequency of their (re-)combination and consequently to shape the evolution of their relatedness. Our second set of hypotheses summarizes this: Hypothesis 2A.More complex technologies are less likely to become related. Hypothesis 2B.Once they have become related, the relatedness of two complex technologies is more likely to intensify than that between two simple technologies. As pointed out above, despite the great interest in relatedness and complexity, little is known about their relation and their relation over time. The following empirical investigations seek to shed some light on this for the first time. Following much of the literature, we rely on patent data as our primary data source (Breschi et al., 2003). More precisely, we use the OECD REGPAT dataset (version 2018) covering patents registered by the European Patent Office (EPO). It contains detailed information on patents' application year, technology classes, inventors and inventor locations since 1976. The pros and cons of patent data have been discussed extensively, so we refrain from this and refer to the relevant literature (Desrochers, 1998; Griliches, 1990). We focus on the period 1976–2010, as this period offers reliable patent information. Figure 1(a) illustrates the growth of the number of patent applications during this period. To track the evolution of technological relatedness, we constructed seven non-overlapping 5-year time periods from 1976 to 2010, with the first covering the years of 1976–1980, the second 1981–1985, and so on. By pooling the data of multiple periods, we increase the stability of our measures, as patent numbers tend to fluctuate strongly between years. This is particularly the case when they are aggregated at the regional or technological level (Buerger, Broekel, & Coad, 2012). (A) Number of patents in each year, 1976–2010, (B) Number of different four-digit Cooperative Patent Classification (CPC) technology classes that appear on patents in each year, 1976–2010, (C) Number of observed distinct technological combinations. We measure technologies as four-digit CPC technology classes and by combination we count the co-occurrence of 2 technologies on patents in the selected 5-year long period, (D) Distribution of combination frequencies in the full sample over all the periods. For this chart, we excluded combinations with 0 frequency Source: Authors' own construction based on OECD REGPAT 2018. Notes: The x axis is on a log10 scale. Figures are based on the entire dataset with no geographical restrictions To empirically represent technological relatedness, we first need to define the technologies. We follow existing studies and consider technologies being represented by four-digit CPC classes (Balland et al., 2019; Broekel, 2019). We exclude technologies starting with the letter Y, indicating cross-technological patents, 11 The 'Y' CPC class is only a technical category with significant overlap to other classes. that is, patents that could not be clearly assigned to other classes. This leaves 645 distinct technologies. Figure 1(b) illustrates that almost all of these technologies contributed patents to the data set since 1980. Accordingly, four-digit CPC classes provide a consistent representation of technologies over this period. There are many ways in which technological proximity can be estimated. Many studies use the information on technologies jointly appearing on patents, so-called co-occurrences, as an indication of combinatorial innovation processes and technological distance (Breschi et al., 2003). We follow this literature and concentrate on the joint appearance of technologies (four-digit CPC) classes on patents. The 645 technologies translate into 207,690 potential technological combinations (excluding self-combinations), which serve as units of observation. For each of these pairs, we count the number of occurrences on patent documents within each of the seven time periods with no geographical restriction (including all the patents in OECD REGPAT from all over the world). More precisely, we count how frequently different four-digit CPC classes are combined on patents during the given period, thus abstracting from the same combinations of four-digit classes on the same patent for the purpose of simplification. Figure 1(c) visualizes the number of distinct technological combinations for each five-year period. Their number is increasing linearly over time. This growth is both in absolute and relative terms. The share of realized combinations of technologies (appearing on at least one patent) on the potential number of combinations grows from around 5% to 18%. This can be explained by the strongly growing numbers of patents and the more or less constant number of technologies (four-digit CPC classes). Figure 1(d) highlights there are few technology combinations that appear in high frequencies (>100 times), while the vast majority are realized in significantly smaller numbers ( 0) and those that never occur (RELATEDNESS = 0) during the examined periods. The disappearing dummy is then constructed such that it is one if in any of the periods we observe a positive number of co-occurrences and simultaneously, in a subsequent period, none. In these models, we find the coefficient of COAGGLOM to be significantly positive. However, its level of significant is smaller than in the case of the first-co-occurrence model. More importantly, the coefficient of COMPLEX_SUM is significantly negative. This suggests that the more complex two technologies are, the less likely it is that their combinations will disappear. This is in line with our arguments that once complex technologies are combined and their relatedness is discovered, they represent economically valuable technology combinations that tend to be used continuously in the future. In an alternative specification, we combine the two models (emergence and intensification of relatedness) into a zero-inflated negative binomial (ZINB) regressions model, which addresses the issue of the access of zeros in our dependent variable (Figure 1(c)). The result of the ZINB regression are shown in Table A4 in the Appendix. As expected, they confirm the earlier findings. Even though our main variables and estimations are based on different (temporal) subsets of the patent database, endogeneity is a crucial concern. Frequently, this is addressed by means of an instrumental variable regression. However, our empirical observations are based on technology pairs, which complicates the identification of proper instrumental variables. Nevertheless, to gain a better understanding of the potential causal directions of the observed relations, we follow the approach of Broekel (2015) who applies an empirical strategy in a similar setting that is popular in the firm growth literature (see, e.g., Coad, 2009). In Table A5 in the Appendix, we transform our central variables (RELATEDNESS, COAGGLOM, COMPLEX_SUM and COMPLEX_ABSDIFF) into annual growth rates and use these as explanatory and dependent variables in different OLS regression settings. More precisely, we regress onto a focal variable's growth in t, its level in t, and the growth rates and the respective levels of all the other variables in t. We find that while RELATEDNESS explains COAGGLOM and both complexity variables in all models, co-location and the complexity of technology pairs seems to be not linked in most models. Notably, none of the models contradicts our previous findings, which highlight their robustness. However, they contain some additional insights, which we will address in more detail in the concluding section. This paper seeks to explore the evolution of technological relatedness over time and to identify what factors contribute to this. Thereby, we shed light on an issue that received little attention so far in studies on relatedness in economic geography and innovation studies: relatedness is usually treated as independent, almost exogenous factor shaping the co-location of technologies (e.g., Kogler et al., 2013; Rigby, 2015), rather than a endogenous structure that requires explanation in itself. We argue that geographical co-concentration of technologies provides mechanisms that impact the evolution of their relatedness. We provide empirical support for this and show that the more the spatial distributions of two technologies overlap, the more likely they become related, and the more this relatedness tends to grow in the future. This suggests that mechanisms such as local knowledge spillovers enhance the emergence and intensification of technological relatedness. This finding is important for the interpretation of outcomes in studies on related diversification, which suggest an impact of relatedness on the location of industrial, technological, and economic activities (Kogler et al., 2013; Neffke et al., 2011). The present study emphasizes that this process is most likely bi-directional: co-location drives the development of relatedness, and vice versa. Knowledge spillovers stimulated by co-location contribute to (re-)combinatorial invention processes, which facilitate technologies converging in terms of relatedness. A somewhat different role seems to be played by technological complexity in this context. In contrast to geographic co-location, it we do not find indications of its influence on the emergence of relatedness. However, once complementarity between technologies has been achieved (i.e., a minimum level of relatedness has been reached), we show that complex technologies are more likely to be combined subsequently. We interpret this is to be driven by the higher economic incentives associated to the combination of complex technologies. Our paper has several implications for future research. First, while our study makes a first step to increase our understanding of how technological relatedness emerges and evolves over time, we need more research to fully comprehend the evolution of technologies and their relations over time (Castaldi, Frenken, & Los, 2015; Desrochers & Leppälä, 2011). This includes gaining more knowledge about where and when re-combinatorial processes create new patterns of relatedness and what type of benefits are related to this (Pinheiro, Alshamsi, Hartmann, Boschma, & Hidalgo, 2018). Second, our study has shown that differences in technologies' levels of complexity can act as barriers to the development of technological relatedness, while two highly complex technologies are more likely to intensify their level of relatedness over time as compared to two less complex ones. Still, these are early insights requiring further validation in future research. For instance, it might be the case that simple and complex technologies represent different underlying knowledge infrastructures and human capital, which restrict their complementarity and consequently their integration. In this sense, complexity might reflect fundamental differences between technologies. Put differently, in the extremes, it might be another way of identifying unrelatedness. This certainly calls for additional research. Third, our study points towards endogenous relations shaping the interplay of relatedness, co-location, and complexity of technologies. For instance, our results indicate that the relation between co-location and relatedness is bi-directional: co-location is linked to the emergence and intensification of relatedness, and vice versa. Our study thereby complements the rich literature on relatedness that has identified relatedness as being a crucial driver behind the spatial distribution of industries and technologies (e.g., Hidalgo et al., 2007; Neffke et al., 2011; Rigby, 2015). Our results suggest that this relationship is more complex and characterized by the existence of co-evolutionary dynamics between the spatial concentration, complexity, and relatedness of different technologies. This has hardly been addressed in the literature. However, the empirical approaches employed do not provide causal evidence on which dimension is driving which. To establish this, future research needs a more elaborate approach utilizing causal modelling strategies. While we have made an attempt to get first insights into the magnitude and direction of these relations, we are far from disentangling the interplay of complexity, co-location, and relatedness. Undoubtedly, such endeavours are crucial, as all these dimensions are applied and used to an increasing extent in current regional innovation policies (Balland et al., 2019; Fitjar et al., 2019). Finally, while the paper provides insights on the evolution of relatedness and explored the role of several factors therein, it leaves a universe of other dimensions unexplored. The consideration of other factors such as the role of institutions or policy interventions will surely add to our understanding of the underlying mechanisms. Moreover, our empirical study relies exclusively on patent data. Despite the many advantages of this approach, it is undoubtedly advisable to repeat and extend the analysis using other types of data in the future (Boschma, 2017). Sándor Juhász acknowledges financial help from the Hungarian Scientific Research Fund (OTKA K-129207). [Correction added on 7 October 2020, after first online publication: funding information has been added to this article.]