Emanuele Pugliese | Scuola Superiore Sant'Anna (original) (raw)

Papers by Emanuele Pugliese

UTIP—UNIDO inequality measure versus CRRD

Pooling of countries and years for the four time intervals: 1995–1997, 1998–2000, 2001–2... more Pooling of countries and years for the four time intervals: 1995–1997, 1998–2000, 2001–2004 and 2005–2008. The colored lines show a non-parametric kernel estimation of UTIP-UNIDO expected values. The shape shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182774#pone.0182774.g004" target="_blank">Fig 4</a> panel (b) is preserved over the chosen time intervals.</p

Cornell University - arXiv, Jun 1, 2022

The evolution of economic and innovation systems at the national scale is shaped by a complex dyn... more The evolution of economic and innovation systems at the national scale is shaped by a complex dynamics, the footprint of which is the nested structure of the activities in which different countries are competitive. Nestedness is a persistent feature across multiple kinds (layers) of activities related to the production of knowledge and goods: scientific research, technological innovation, industrial production and trade. We observe that in the layers of innovation and trade the competitiveness of countries correlates unambiguously with their diversification, while the science layer displays some peculiar feature. The evolution of scientific domains leads to an increasingly modular structure, in which the most developed nations become less competitive in the less advanced scientific domains, where they are replaced by the emerging countries. This observation is in line with a capability-based view of the evolution of economic systems, but with a slight twist. Indeed, while the accumulation of specific know-how and skills is a fundamental step towards development, resource constraints force countries to acquire competitiveness in the more complex research fields at the price of losing ground in more basic, albeit less visible (or more crowded), fields. This tendency towards a relatively specialized basket of capabilities leads to a trade-off between the need to diversify in order to evolve and the need to allocate resources efficiently. Collaborative patterns among developed nations reduce the necessity to be competitive in the less sophisticated fields, freeing resources for the more complex domains.

Economic growth and development of a country involves accumulation of knowledge and dynamic capab... more Economic growth and development of a country involves accumulation of knowledge and dynamic capabilities (Cimoli et al., 2009). Past research has begun to investigate the capability accumulation and macro-economic development of countries and sectors (Dosi et al., 1990), also by means of introduction of new products (Hausmann and Rodrik, 2003). In this work, recognizing that firms are the actual domain in which production takes place, we focus on the firm-level process of capability accumulation and diversification in a developing country. We investigate the relationship between diversification (and coherent diversification) and firm performance by employing an extensive database of Indian manufacturing firms with detailed information on product mix of firms. We claim that such an understanding of firms' incentives to diversify is relevant not only for the corporate management, but also for the diversification of countries and thereby its development. First, we explore the reaso...

The three figures refer to the US counties in 1998 and are realized through considering data on wages and employment from the QCEW data-set

In this case, sectors are categorized according to the NAICS industry classification at ... more In this case, sectors are categorized according to the NAICS industry classification at 3-digit aggregation level, with Ncounties = 2805 and Nsectors = 89. (a): The binary county-sector matrix is built from the volume of sectoral wages. By ranking the columns of the matrix by increasing County Fitness and the rows by increasing Sector Complexity, assumes a triangular-like shape, as it did in the analysis of international export. Hence, the path towards diversification in the production of goods and services is not only taken, as we have seen in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182774#pone.0182774.g001" target="_blank">Fig 1</a>, by high Fitness countries but also by the most complex and diversified US counties. (b): The relation between the average wage and the Fitness of counties. The red line represents a kernel estimation of Relative County Average Wage versus FCOUNTY and the green shadowed area shows a 90% confidence interval of the expected value computed with bootstrap. On a country level the monetary counterpart of Fitness was the GDP per capita, while here we adopt the average aage as a proxy of the wealth produced by labor in a county. The trend is concordant with the one found comparing countries, in the sense that a non-linear relationship holds between the two variables: as FCOUNTY grows the Average Wage of the county increases; except for some deviations of Fitness from the monetary metric which are highly informative on a the potential performances of counties. (c): The relationship between national Sector Average Wage and Sector Complexity. As in the previous case, the red line is a kernel estimation of Sector Average Wage versus QSECTOR and the green area is a 90% confidence interval computed with bootstrap. As it seems reasonable, Sector Average Wage grows with Sector Complexity.</p

Economic Development and Inequality: a complex system analysis

By borrowing methods from complex system analysis, in this paper we analyze the features of the c... more By borrowing methods from complex system analysis, in this paper we analyze the features of the complex relationship that links the development and the industrialization of a country to economic inequality. In order to do this, we identify industrialization as a combination of a monetary index, the GDP per capita, and a recently introduced measure of the complexity of an economy, the Fitness. At first we explore these relations on a global scale over the time period 1990--2008 focusing on two different dimensions of inequality: the capital share of income and a Theil measure of wage inequality. In both cases, the movement of inequality follows a pattern similar to the one theorized by Kuznets in the fifties. We then narrow down the object of study ad we concentrate on wage inequality within the United States. By employing data on wages and employment on the approximately 3100 US counties for the time interval 1990--2014, we generalize the Fitness-Complexity algorithm for counties an...

Data from: Technology networks: the autocatalytic origins of innovation

We analyse the autocatalytic structure of technological networks and evaluate its significance fo... more We analyse the autocatalytic structure of technological networks and evaluate its significance for the dynamics of innovation patenting. To this aim, we define a directed network of technological fields based on the International Patents Classification, in which a source node is connected to a receiver node via a link if patenting activity in the source field anticipates patents in the receiver field in the same region more frequently than we would expect at random. We show that the evolution of the technology network is compatible with the presence of a growing autocatalytic structure, i.e. a portion of the network in which technological fields mutually benefit from being connected to one another. We further show that technological fields in the core of the autocatalytic set display greater fitness, i.e. they tend to appear in a greater number of patents, thus suggesting the presence of positive spillovers as well as positive reinforcement. Finally, we observe that core shifts take place whereby different groups of technology fields alternate within the autocatalytic structure; this points to the importance of recombinant innovation taking place between close as well as distant fields of the hierarchical classification of technological fields

Industrial Innovation for Transformation: 7th European Conference on Corporate R&D and Innovation CONCORDi 2019. Background Note

Key science and EU policy topics on 'Innovation for industrial transformation', the focus... more Key science and EU policy topics on 'Innovation for industrial transformation', the focus of CONCORDi 2019 Conference, are addressed in this background report such as: how manage an inclusive and sustainable industrial transformation, establishing European firms as globally competitive; how to advise industrial and innovation policy on how to take into account specific territorial needs to guarantee fair and sustainable competitiveness and job creation across the whole EU; and what are the main challenges and opportunities that EU industry transformation due to innovation will face. Addressing these compelling topics is not an easy task and requires us to consider different dimensions of analysis in parallel. The report first points out what are the research and policy challenges ahead, and then introduces expected contribution from CONCORDi 2019 by the academia providing a short literature background in the main corresponding areas, by policy-makers and by other distinguish...

The goal of the paper is to elaborate an empirical overview of green technological development in... more The goal of the paper is to elaborate an empirical overview of green technological development in European regions. This is a timely pursuit considering the ambitious commitments stipulated in the recent European Green Deal to achieve climate neutrality by 2050. Our analysis is organised in three steps. First, we map the geographical distribution of innovative activities in Europe and profile regions in terms of technological capabilities. Second, we elaborate a metric to identify regions’ green innovation potential. Third, we check whether possessing comparative advantage in specific technological domains is associated with a region’s capacity to develop green technologies. JEL classification: O32, Q01, Q55

We investigate the convergence properties of an algorithm which has been recently proposed to mea... more We investigate the convergence properties of an algorithm which has been recently proposed to measure the competitiveness of countries and the quality of their exported products. These quantities are called respectively Fitness F and Complexity Q. The algorithm was originally based on the adjacency matrix M of the bipartite network connecting countries with the products they export, but can be applied to any bipartite network. The structure of the adjacency matrix turns to be essential to determine which countries and products converge to non zero values of F and Q. Also the speed of convergence to zero depends on the matrix structure. A major role is played by the shape of the ordered matrix and, in particular, only those matrices whose diagonal does not cross the empty part are guaranteed to have non zero values as outputs when the algorithm reaches the fixed point. We prove this result analytically for simplified structures of the matrix, and numerically for real cases. Finally, ...

Determining how scientific achievements influence the subsequent process of knowledge creation is... more Determining how scientific achievements influence the subsequent process of knowledge creation is a fundamental step in order to build a unified ecosystem for studying the dynamics of innovation and competitiveness. Relying separately on data about scientific production on one side, through bibliometric indicators, and about technological advancements on the other side, through patents statistics, gives only a limited insight on the key interplay between science and technology which, as a matter of fact, move forward together within the innovation space. In this paper, using citation data of both research papers and patents, we quantify the direct influence of the scientific outputs of nations on further advancements in science and on the introduction of new technologies. Our analysis highlights the presence of geo-cultural clusters of nations with similar innovation system features, and unveils the heterogeneous coupled dynamics of scientific and technological advancements. This st...

This note is a contribution to the debate about the optimal algorithm for Economic Complexity tha... more This note is a contribution to the debate about the optimal algorithm for Economic Complexity that recently appeared on ArXiv [1, 2] . The authors of [2] eventually agree that the ECI+ algorithm [1] consists just in a renaming of the Fitness algorithm we introduced in 2012, as we explicitly showed in [3]. However, they omit any comment on the fact that their extensive numerical tests claimed to demonstrate that the same algorithm works well if they name it ECI+, but not if its name is Fitness. They should realize that this eliminates any credibility to their numerical methods and therefore also to their new analysis, in which they consider many algorithms [2]. Since by their own admission the best algorithm is the Fitness one, their new claim became that the search for the best algorithm is pointless and all algorithms are alike. This is exactly the opposite of what they claimed a few days ago and it does not deserve much comments. After these clarifications we also present a constr...

We show that the space in which scientific, technological and economic developments interplay wit... more We show that the space in which scientific, technological and economic developments interplay with each other can be mathematically shaped using pioneering multilayer network and complexity techniques. We build the tri-layered network of human activities (scientific production, patenting, and industrial production) and study the interactions among them, also taking into account the possible time delays. Within this construction we can identify which capabilities and prerequisites are needed to be competitive in a given activity, and even measure how much time is needed to transform, for instance, the technological know-how into economic wealth and scientific innovation, being able to make predictions with a very long time horizon. Quite unexpectedly, we find empirical evidence that the naive knowledge flow from science, to patents, to products is not supported by data, being instead technology the best predictor for industrial and scientific production for the next decades.

code_rsos_gitCodes

The archive contains the data needed to reproduce the results presented in the paper. This archiv... more The archive contains the data needed to reproduce the results presented in the paper. This archive does not contain codes, which, however, can be downloaded from github (see 'code/download_istructions.txt' inside the archive for details).

Codes

The files contain all the necessary code to reproduce the results in the paper. We suggest placin... more The files contain all the necessary code to reproduce the results in the paper. We suggest placing them in the "code" folder contained in the data archive before running them.

We define a novel quantitative strategy inspired by the ecological notion of nestedness to single... more We define a novel quantitative strategy inspired by the ecological notion of nestedness to single out the scale at which innovation complexity emerges from the aggregation of specialized building blocks. Our analysis not only suggests that the innovation space can be interpreted as a natural system in which advantageous capabilities are selected by evolutionary pressure, but also that the emerging structure of capabilities is not independent of the scale of observation at which they are observed. Expanding on this insight allows us to understand whether the capabilities characterizing the innovation space at a given scale are compatible with a complex evolutionary dynamics or, rather, a set of essentially independent activities allowing to reduce the system at that scale to a set of disjoint non interacting sub-systems. This yields a measure of the innovation complexity of the system, i.e. of the degree of interdependence between the sets of capabilities underlying the system's ...

We introduce and study a model of an interacting population of agents who collaborate in groups w... more We introduce and study a model of an interacting population of agents who collaborate in groups which compete for limited resources. Groups are formed by random matching agents and their worth is determined by the sum of the efforts deployed by agents in group formation. Agents, on their side, have to share their effort between contributing to their group's chances to outcompete other groups and resource sharing among partners, when the group is successful. A simple implementation of this strategic interaction gives rise to static and evolutionary properties with a very rich phenomenology. A robust emerging feature is the cosxistence in the population of agents who invest mainly in the success of their group and agents who concentrate in getting the largest share of their group's profits.

epl draft Heterogeneous pair approximation for voter models on networks

Abstract.- For models whose evolution takes place on a network it is often necessary to augment t... more Abstract.- For models whose evolution takes place on a network it is often necessary to augment the mean-field approach by considering explicitly the degree dependence of average quantities (heterogeneous mean-field). Here we introduce the degree dependence in the pair approximation (heterogeneous pair approximation) for analyzing voter models on uncorrelated networks. This approach gives an essentially exact description of the dynamics, correcting some inaccurate results of previous approaches. The heterogeneous pair approximation introduced here can be applied in full generality to many other processes on complex networks. Introduction. – The voter model is one of the simplest possible models of nonequilibrium dynamics, as witnessed by the number of different contexts where it has been considered [1–3]. Each node of a graph is endowed with a binary variable

arXiv: Economics, 2017

Economic Complexity as a Determinant of Industrialization of Countries: The Case of India

Sustaining High Growth in India

In this work we use patent data from the European patent office (EPO) to assess the capabilities ... more In this work we use patent data from the European patent office (EPO) to assess the capabilities of EU regions in developing digital technologies especially focusing on those that are more closely related to the digital transformation. More specifically, we measure ICT patents by considering those containing digital codes, as defined by the OECD. The penetration of digital technologies in the development of innovative products is instead captured by the co-occurrence of digital and non-digital codes within patent documents; we call these patents ICT-combining patents.