Nico Pintar (p), Thomas Scherngell, Jürgen Essletzbichler

Nanditha Mathew

Economic development is uneven among as well as within countries. In addition to differences
in economic development between countries, we also observe wide disparities in economic (mis)fortunes between subnational regions. This
variation is often explained by productivity differences which allow some countries (or regions) to
prosper while others fall behind. Even though these differences in productivity are driven by a large number of characteristics of
the economy, technological progress is considered as the most essential factor for
productivity gains and economic growth.
However, it is clear that not all knowledge has the same quality or value. In an industrial/innovation policy sense, knowledge or technologies that are harder to be imitated and diffused in geographical space offer more sustained competitive advantage for the innovating firms and regions. In this context,
the concept of knowledge complexity has been developed to empirically approach the
elusive notion of knowledge quality.
In this paper we explore the link between regional knowledge complexity and total factor productivity (TFP) by adopting a spatial
econometric modelling approach. The modelling approach is inspired by the regional knowledge capital model (KCM) that relates knowledge to regional TFP. As the qualitative dimension of knowledge has been neglected so far, we augment the regional KCM with a knowledge complexity measure.
We employ an empirical model in the form of a (fixed effects) dynamic Spatial Durbin Model which allows to identify short- and long-term direct and
spillover effects of knowledge complexity on regional productivity.
This is needed to both take into account the potentially very localised productivity effects of complex knowledge as well as
the non-trivial time horizon at which such effects may take form.
In line with the literature, we use patent data to proxy regional knowledge production. To best approximate functional economic regions, we
adopt EUROSTATs metropolitan regions.
From recent related literature we can distinguish at least three popular complexity measures that have been put forward to inform regional innovation policy but produce
differing results which are the economic complexity index (ECI), the economic fitness complexity index (EFC) and the structural diversity index (SDI).
Consequently, we consult a recent report by Pintar and Essletzbichler (2022) that serves as a guide to dismiss certain versions
of the indices beforehand as well as identify promising ones.
The paper at hand aims to substantiate academic and policy interest in the topic by focusing on direct productivity effects of regional specialisation into more complex knowledge.

Stefano Costa, Stefano De Santis, Giovanni Dosi, Roberto Monducci, Angelica Sbardella (p), Maria Enrica Virgillito

Nico Pintar

This paper is one of the first attempts at empirically identifying organisational capabilities – in this work concerning Italian firms. Together, it proposes new evidence on the link between capabilities and economic performances. In order to do so, we employ the Indagine Multiscopo del Censimento Permanente delle Imprese (IMCPI), a survey carried out by the Italian Statistical Office (ISTAT) in 2019, covering the three-year period 2016–2018, addressing a wide range of organizational characteristics including various organizational routines, human resource management, internationalisation strategies and many others. Our contribution is threefold: first, we aim at detecting what practices and combinations of them result in underlying different capabilities; second, we propose a taxonomy of the production system, both at firm- and sectorlevel based on the mapping of such capabilities, third we study the performance outcomes of different capability-taxa in terms of productivity growth.

Dario Mazzilli (p), Aurelio Patelli, Manuel Sebastian Mariani, Flaviano Morone

Angelica Sbardella

We uncover the connection between the Fitness-Complexity algorithm, developed in the economic complexity field, and the Sinkhorn-Knopp algorithm, widely used in diverse domains ranging from computer science and mathematics to economics.
Despite minor formal differences between the two methods, both converge to the same fixed-point solution up to normalization.
The discovered connection allows us to derive a rigorous interpretation of the Fitness and the Complexity metrics as the potentials of a suitable energy function.
Under this interpretation, high-energy products are unfeasible for low-fitness countries, which explains why the algorithm is effective at displaying nested patterns in bipartite networks.
We also show that the proposed interpretation reveals the scale invariance of the Fitness-Complexity algorithm, which has practical implications for the algorithm's implementation in different datasets.
Further, analysis of empirical trade data under the new perspective reveals three categories of countries that might benefit from different development strategies.
This interpretation can be framed in all the empirical application of the FC algorithm, such as technology, science and green innovation.
This new description is useful to quantify, in terms of 'energy' requirement and efficiency in resources allocation, the feasibility of strategies that aim to establish new production, technology's development or researches.
We briefly discuss how the Optimal Transport framework may allow an important extension of this work and represent a new powerful ingredient for Economic Complexity.

Simone Sasso (p), Emanuele Pugliese, Dario Diodato

Dario Mazzilli

There is a considerable academic research and policy interest in innovative entrepreneurship and start-ups. However, few studies have analysed the territorial determinants of start-up creation and there is virtually no evidence on the relationship between pre-existing territorial innovative capabilities and start-up formation in rural areas. In this paper, we contribute to address this gap by studying the relationship between regional (economic, scientific, and technological) specialisation and start-up emergence in rural areas, through the lenses of economic complexity. With the aim to shed light on the potential sources of knowledge that are used by start-ups, we rely on recent economic complexity techniques (Pugliese et al., 2019) which allow analysing multilayer networks and combining data on startups, trade, technological and scientific outputs. The main objective of the study is to shade light on the relevant sources of knowledge for start-ups and better inform policy initiatives supporting innovative entrepreneurship in European rural contexts.

Nanditha Mathew (p)

Simone Sasso

Our study investigates the heterogeneity of skill demands within occupations among Indian firms using a unique matched database of firm-level data and online job vacancy data. We employ an innovative skill taxonomy and use a multi-level machine learning and econometric empirical approach to investigate the characteristics of Indian firms that are associated with demand for high skill diversification and various combination of skill sets. Our empirical analysis provides compelling evidence of significant heterogeneity in skill requirements across firms within the same occupations. Additionally, we find that firms that demand diverse skill sets differ from their counterparts. Firms involved in complex activities, such as innovation or foreign market participation, require digital skills and specific combinations of digital skills with other skills. Our findings highlight the crucial role played by firms in defining the nature of work, moving beyond the simplistic discussion of being skilled/unskilled or doing routine/non-routine activities, often seen in the broader literature on the future of jobs and skill demand.