Discovering pre-entry knowledge complexity with patent topic modeling and the post-entry growth of Italian firms

Innovation studies have largely recognized the role of knowledge in fostering innovation and growth of entrants. Previous literature has focused on entrepreneurial and managerial capabilities and education and knowledge incorporated in material and immaterial resources. We assume that new firms need to possess different pieces of knowledge, but beyond diversity, business performance relies also on knowledge distinctiveness. In other words, the complexity of a knowledge base is not simply the recombination of homogeneous pieces of knowledge but it also depends on the specific nature of each of them. This paper develops a new complexity indicator able to capture the complexity of the knowledge base by applying a topic modeling approach to the analysis of patent text. We explore the empirical relation between pre-entry complexity of knowledge, as measured by our complexity index, and post-entry growth performance of a sample of Italian firms entering the market in 2009-2011, which we then follow over the period 2012-2021. Baseline results show a significant and positive association between knowledge complexity and growth, even after controlling for firm characteristics and year, sector and region fixed-effects. Robustness analysis reveal this positive effect is stronger in the medium-long run while relatively weaker for innovative SMEs

The survival of start-ups in time of crisis. A machine learning approach to measure innovation

This paper shows how data science can contribute to improving empirical research in economics by leveraging on large datasets and extracting information otherwise unsuitable for a traditional econometric approach. As a test-bed for our framework, machine learning algorithms allow us to create a new holistic measure of innovation built on a 2012 Italian Law aimed at boosting new high-tech firms. We adopt this measure to analyse the impact of innovativeness on a large population of Italian firms which entered the market at the beginning of the 2008 global crisis. The methodological contribution is organised in different steps. First, we train seven supervised learning algorithms to recognise innovative firms on 2013 firmographics data and select a combination of those with best predicting power. Second, we apply the former on the 2008 dataset and predict which firms would have been labelled as innovative according to the definition of the law. Finally, we adopt this new indicator as regressor in a survival model to explain firms' ability to remain in the market after 2008. Results suggest that the group of innovative firms are more likely to survive than the rest of the sample, but the survival premium is likely to depend on location

Industrial Pattern and Robot Adoption in European Regions

Recent literature on the diffusion of robots mostly ignores the regional dimension. The contribution of this paper at the debate on Industry 4.0 is twofold. First, IFR (2017) data on acquisitions of industrial robots in the five largest European economies are rescaled at regional levels to draw a first picture of winners and losers in the European race for advanced manufacturing. Second, using an unsupervised machine learning approach to classify regions based on their composition of industries. The paper provides novel evidence of the relationship between industry mix and the regional capability of adopting robots in the industrial processes.Recent literature on the diffusion of robots mostly ignores the regional dimension. The contribution of this paper at the debate on Industry 4.0 is twofold. First, IFR (2017) data on acquisitions of industrial robots in the five largest European economies are rescaled at regional levels to draw a first picture of winners and losers in the European race for advanced manufacturing. Second, using an unsupervised machine learning approach to classify regions based on their composition of industries. The paper provides novel evidence of the relationship between industry mix and the regional capability of adopting robots in the industrial processes

Creative Clusters and Creative Multipliers: Evidence from UK Cities

Economic geographers have paid much attention to the cultural and creative industries, both for their propensity to cluster in urban settings, and their potential to drive urban economic development. However, evidence on the latter is surprisingly sparse. In this paper we explore the long-term, causal impacts of the cultural and creative industries on surrounding urban economies. Adapting Moretti’s local multipliers framework, we build a new 20-year panel of UK cities, using historical instruments to identify causal effects of creative activity on non-creative firms and employment. We find that each creative job generates at least 1.9 non-tradable jobs between 1998 and 2018. Prior to 2007, these effects seem more rooted in creative services employees’ local spending than visitors to creative amenities. Given the low numbers of creative jobs in most cities, the overall impact of the creative multiplier is small. On average, the creative sector is responsible for over 16% of non-tradable job growth in our sample, though impacts will be larger in bigger clusters. We do not find the same effects for workplaces, and find no causal evidence for spillovers from creative activity to other tradable sectors. In turn, this implies that ‘creative city’ policies will have partial, uneven local economic impacts. Given extensive urban clusters of creative activity in many countries, our results hold value beyond the UK setting