What does patent data reveal about economic fitness?

Businesses that grow in size in terms of sales, costs, and profits are subject to scaling rules. However, when businesses expand, they frequently tend to concentrate on short-term objectives and become less varied, which lowers their likelihood of long-term survival.

What does patent data reveal about economic fitness?
Scaling rules outline how different characteristics of a system change as it gets bigger. Scaling laws explain how socioeconomic numbers in cities—like the number of petrol stations, the length of pipelines, the number of roadways, and the number of wires—increase as the city's population rises. Scale demonstrates growth and decay. 
Businesses that grow in size in terms of sales, costs, and profits are subject to scaling rules. However, when businesses expand, they frequently tend to concentrate on short-term objectives and become less varied, which lowers their likelihood of long-term survival. Only a small fraction of businesses continue for a very long period; the majority fail within a few years.
As companies grow, they tend to add more rules and regulations, limiting their ability to innovate and diversify. This lack of diversity and R&D can lead to reduced resilience and eventual failure. The companies that survive the longest tend to be specialised and operate in niche markets, producing high-quality products for a small, dedicated clientele.
In contrast, cities tend to be more resilient and can recover from damage more easily.
The COVID-19 pandemic has highlighted the need to rethink the role of cities in urban planning and policy decisions. The use of patent data to monitor technological innovation is well established in the literature, and the Economic Fitness and Complexity approach is used to measure urban technological innovation and investigate its correlation with economic growth in cities.

The article "Urban economic fitness and complexity from patent data" by Matteo Straccamore, Matteo Bruno, Bernardo Monechi, and Vittorio Loreto, published in Nature on 4 March 2023, focuses on the relationship between innovation, economic growth, and urbanisation. The authors use patent data to measure the level of economic fitness and complexity of different cities, and how these factors impact their economic growth.
The authors highlight cities' importance in driving economic growth, innovation, and technological progress. They argue that cities are not only places where ideas are generated, but also where they are tested, refined, and turned into commercial products. The authors begin by discussing the concept of economic fitness, which is the ability of an economic system to create and maintain a diverse set of products and services. They argue that cities with higher economic fitness are more likely to have sustained economic growth over time. They also introduce the concept of complexity, which is a measure of the interconnectedness and diversity of economic activities within a city.
The authors argue that urban areas that are economically fit and complex are more likely to generate innovations, as measured by the number of patents filed. They use patent data as a proxy for innovation, as patents are a measure of the originality and novelty of an idea or invention, and the ability to generate patents is closely related to the complexity of the economic environment in which they are generated.  The authors use patent data to measure cities' economic fitness and complexity. Patents are a measure of innovation and are an important indicator of economic activity. The authors argue that the number of patents filed in a city is a good indicator of its economic fitness and the complexity of its economy.
The author outlines the research questions addressed in their study, which focus on understanding the technological production of metropolitan areas. They aim to use the Economic Fitness and Complexity framework to analyse the complexity and technological endowment of different cities and investigate whether they tend to specialise or diversify their production of patents. They also aim to identify clusters of cities with similar technological baskets. The paper is organised into sections describing the data used, the methodologies employed, the results obtained, and a discussion of the contributions and future work.
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The paper describes a method they used to analyse data on metropolitan areas (MAs) and technologies. They created bipartite networks of MAs and technologies for each 5-year window  and then projected these networks to create monopartite similarity networks. They used community detection to identify groups of similar products within these networks. The resulting networks are shown in Figure 2.
The technology network in Figure 2a shows different communities of technologies but does not have a strong modular structure. The communication and information community contains the most complex technologies, and not all MAs can patent in this area.
The metropolitan area network in Figure 2b shows how MAs can be related from the point of view of technological production, and both the location and the development of cities can be a factor in their similarity. They found well-defined communities of MAs, including Chinese, emerging countries, Euro + US, Japanese + Korean MAs, car-related, and high-tech. The high fitness cluster related to high-tech production contains MAs such as London, New York, or San Jose, while the community made by Western car manufacturing MAs includes Turin, Detroit, and Stuttgart. The Japanese & Korean cluster shares connections with both the Western, the Chinese, and the emerging countries clusters. The Supplementary Information contains a complete table of MAs with their class.
The author presents the results of their analysis of the relationship between the technology basket of metropolitan areas (MAs) and their Gross Domestic Product per capita (GDPpc). They use the Fitness and Complexity algorithm to calculate the complexity of technologies at the country level and then compute the exogenous Fitness of the MAs. The results are presented in three different representations of the GDPpc-Fitness plane. 
In the first representation, the trajectories of some MAs from 1990 to 2010 are shown. The results suggest that metropolitan areas with high Fitness are generally more likely to have a more significant increase in GDPpc. The second representation shows the average vector field of the trajectories from 1995 to 2005, which indicates that metropolitan areas with a high Fitness generally show an increase in GDPpc, except for those that already have a very high GDPpc. In the third representation, the overall trend of all MAs whose trajectories are coloured according to the community of belonging is shown. The results suggest that the Fitness trends of all clusters are decreasing, except for the Emerging and Chinese clusters.
The author notes that their approach is a "phenotypic" one, which means that they only consider information about metropolitan areas and patents, and do not perform a "genotypic" analysis, which would require knowledge of the capabilities to make the technologies and the processes by which MAs can use their abilities to produce them. The author acknowledges this limitation but suggests that their measure needs little data to be computed.
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The author presents a graph (Figure 4) showing the Fitness rankings of metropolitan areas from 1990 to 2020. Fitness refers to the number of patents filed by inventors in a metropolitan area per year, normalised by the population of that area. The graph shows a significant increase in the number of patents filed by Chinese metropolitan areas from 1990 to 2020, with many Chinese cities dominating the top 30 rankings by 2010. In 2020, Suzhou in China was at the top of the rankings, followed by other Chinese cities like Nantong. Only two non-Chinese metropolitan areas, both from Korea, were in the top 30 rankings in 2020. The author suggests that this surge in patent filings by Chinese metropolitan areas was part of a coordinated strategy to rapidly develop and modernise the country. The author also notes that there have been some studies that have criticised the increase in China's patenting activities.
The authors discuss the results of their analysis of coherent diversification in technological production. They present the results in Fig. 5, which shows the correlation between coherence, fitness, and the change in GDPpc (Gross Domestic Product per capita) for metropolitan areas. They observe that coherence correlates with a positive change in GDPpc better than fitness. This means that the coherence of technological products is a better predictor of economic growth than a metropolitan area's complexity.
Furthermore, the authors note that out of the top 100 MAs ranked by coherence, 79 are Chinese. This result supports the idea that China has a coherent diversification strategy in technological production. To test the robustness of this result, the authors perform a test without Chinese MAs and find similar results. They also show that high coherence is not related to low diversification.
Overall, the authors suggest that their findings can help policymakers and business leaders make informed decisions about technological diversification strategies. By understanding the importance of coherence in technological production for economic growth, decision-makers can design more effective policies and investment strategies.
The author discusses a study on technological innovation in metropolitan areas based on data on patent production. They focus on the signals of specialisation and diversification by applying the Fitness and Complexity framework and novel methods for bipartite networks to the technological production of metropolitan areas. The findings indicate that metropolitan areas tend to specialise in technology sectors, particularly for some technological categories, such as cars or electronics, but the biggest ones are able to diversify, and some manage to be more generalists, although their focus shifts to complex technologies.
Chinese metropolitan areas give the best example of similar metropolitan areas in a single country, and they are organised into three coherent clusters specialised in similar technological baskets. The coherent diversification strategies of China are in line with previous results analysing technology spillovers in Chinese regions. The author also highlights similarities between emerging metropolitan areas and highly technological metropolitan areas. Interestingly, the network of similarities among metropolitan areas shows a clear geographical boundary between highly developed Asian and Western (European/American) metropolitan areas.
The author applies the Fitness and Complexity framework to understand the evolution of the quality in technological innovation of metropolitan areas and their clusters. In line with previous results, a high Fitness can be correlated with a high GDP per capita. Metropolitan areas with a complex technological basket show higher increases in GDP per capita in the following years than metropolitan areas developing more basic technologies. The complexity of innovation in Chinese metropolitan areas is very high, and their GDP per capita displays rapid growth. Chinese metropolitan areas are not only able to diversify their innovation patterns by aiming for a more complex technological basket but also do this in a coherent and coordinated way.
The authors suggest in conclusion several potential future applications of their theoretical framework. They propose that the methods could be used to assess the best diversification strategy for metropolitan areas at different scales and capabilities and to forecast future technology production. The authors also highlight the importance of coherent diversification and suggest that their findings on the strategy of Chinese metropolitan areas could be used to inform policy in other countries. Additionally, the authors note the unique signal from metropolitan areas focused on car production and suggest that future studies could focus on optimal diversification strategies and forecasting technology production in these areas, especially in light of the forthcoming changes in the automotive industry due to the shift towards electric cars. Overall, the authors' work provides insights into the patterns of technological production and diversification across metropolitan areas, and their findings could have practical implications for policymakers and businesses.
Written by: Shivani
Visualisations by: Nature