arXiv subjects

Massimo Riccaboni

Publications and source records attributed to Massimo Riccaboni.

At least 37 records · Page 2Linked to original sources

arXiv2020

Supervised learning for the prediction of firm dynamics

Thanks to the increasing availability of granular, yet high-dimensional, firm level data, machine learning (ML) algorithms have been successfully applied to address multiple research questions related to firm dynamics. Especially supervised learning (SL), the branch of ML dealing with the prediction of labelled outcomes, has been used to better predict firms' performance. In this contribution, we will illustrate a series of SL approaches to be used for prediction tasks, relevant at different stages of the company life cycle. The stages we will focus on are (i) startup and innovation, (ii) growth and performance of companies, and (iii) firms exit from the market. First, we review SL implementations to predict successful startups and R&D projects. Next, we describe how SL tools can be used to analyze company growth and performance. Finally, we review SL applications to better forecast financial distress and company failure. In the concluding Section, we extend the discussion of SL methods in the light of targeted policies, result interpretability, and causality.

econ.GN↗

arXiv2020

Talents from Abroad. Foreign Managers and Productivity in the United Kingdom

In this paper, we test the contribution of foreign management on firms' competitiveness. We use a novel dataset on the careers of 165,084 managers employed by 13,106 companies in the United Kingdom in the period 2009-2017. We find that domestic manufacturing firms become, on average, between 7% and 12% more productive after hiring the first foreign managers, whereas foreign-owned firms register no significant improvement. In particular, we test that previous industry-specific experience is the primary driver of productivity gains in domestic firms (15.6%), in a way that allows the latter to catch up with foreign-owned firms. Managers from the European Union are highly valuable, as they represent about half of the recruits in our data. Our identification strategy combines matching techniques, difference-in-difference, and pre-recruitment trends to challenge reverse causality. Results are robust to placebo tests and to different estimators of Total Factor Productivity. Eventually, we argue that upcoming limits to the mobility of foreign talents after the Brexit event can hamper the allocation of productive managerial resources.

econ.EM↗

arXiv2018

Parameters estimation in a 3-parameters p-star model

An important issue in social network analysis refers to the development of algorithms for estimating optimal parameters of a social network model, using data available from the network itself. This entails solving an optimization problem. In the paper, we propose a new method for parameters estimation in a specific social network model, namely, the so-called p-star model with three parameters. The method is based on the mean-field approximation of the moments associated with the three subgraphs defining the model, namely: the mean numbers of edges, two-stars, and triangles. A modified gradient ascent method is applied to maximize the log-likelihood function of the p-star model, in which the components of the gradient are computed using approximate values of the moments. Compared to other existing iterative methods for parameters estimation, which are computationally very expensive when the number of vertices becomes large, such as gradient ascent applied to maximum log-pseudo- likelihood estimation, the proposed approach has the advantage of a much cheaper cost per iteration, which is practically independent of the number of vertices.

math.ST↗

arXiv2018

The Network of U.S. Mutual Fund Investments: Diversification, Similarity and Fragility throughout the Global Financial Crisis

Network theory proved recently to be useful in the quantification of many properties of financial systems. The analysis of the structure of investment portfolios is a major application since their eventual correlation and overlap impact the actual risk diversification by individual investors. We investigate the bipartite network of US mutual fund portfolios and their assets. We follow its evolution during the Global Financial Crisis and analyse the interplay between diversification, as understood in classical portfolio theory, and similarity of the investments of different funds. We show that, on average, portfolios have become more diversified and less similar during the crisis. However, we also find that large overlap is far more likely than expected from models of random allocation of investments. This indicates the existence of strong correlations between fund portfolio strategies. We introduce a simplified model of propagation of financial shocks, that we exploit to show that a systemic risk component origins from the similarity of portfolios. The network is still vulnerable after crisis because of this effect, despite the increase in the diversification of portfolios. Our results indicate that diversification may even increase systemic risk when funds diversify in the same way. Diversification and similarity can play antagonistic roles and the trade-off between the two should be taken into account to properly assess systemic risk.

q-fin.ST↗

arXiv2017

The Indirect Effects of FDI on Trade: A Network Perspective

The relationship between international trade and foreign direct investment (FDI) is one of the main features of globalization. In this paper we investigate the effects of FDI on trade from a network perspective, since FDI takes not only direct but also indirect channels from origin to destination countries because of firms' incentive to reduce tax burden, to minimize coordination costs, and to break barriers to market entry. We use a unique data set of international corporate control as a measure of stock FDI to construct a corporate control network (CCN) where the nodes are the countries and the edges are the corporate control relationships. Based on the CCN, the network measures, i.e., the shortest path length and the communicability, are computed to capture the indirect channel of FDI. Empirically we find that corporate control has a positive effect on trade both directly and indirectly. The result is robust with different specifications and estimation strategies. Hence, our paper provides strong empirical evidence of the indirect effects of FDI on trade. Moreover, we identify a number of interplaying factors such as regional trade agreements and the region of Asia. We also find that the indirect effects are more pronounced for manufacturing sectors than for primary sectors such as oil extraction and agriculture.

q-fin.GN↗

arXiv2016

Homophily and Triadic Closure in Evolving Social Networks

We present a new network model accounting for multidimensional assortativity. Each node is characterized by a number of features and the probability of a link between two nodes depends on common features. We do not fix a priori the total number of possible features. The bipartite network of the nodes and the features evolves according to a stochastic dynamics that depends on three parameters that respectively regulate the preferential attachment in the transmission of the features to the nodes, the number of new features per node, and the power-law behavior of the total number of observed features. Our model also takes into account a mechanism of triadic closure. We provide theoretical results and statistical estimators for the parameters of the model. We validate our approach by means of simulations and an empirical analysis of a network of scientific collaborations.

cs.SI↗

arXiv2015

Disambiguation of Patent Inventors and Assignees Using High-Resolution Geolocation Data

Patent data represent a significant source of information on innovation and the evolution of technology through networks of citations, co-invention and co-assignment of new patents. A major obstacle to extracting useful information from this data is the problem of name disambiguation: linking alternate spellings of individuals or institutions to a single identifier to uniquely determine the parties involved in the creation of a technology. In this paper, we describe a new algorithm that uses high-resolution geolocation to disambiguate both inventor and assignees on more than 3.6 million patents found in the European Patent Office (EPO), under the Patent Cooperation treaty (PCT), and in the US Patent and Trademark Office (USPTO). We show that our algorithm has both high precision and recall in comparison to a manual disambiguation of EPO assignee names in Boston and Paris, and show it performs well for a benchmark of USPTO inventor names that can be linked to a high-resolution address (but poorly for inventors that never provided a high quality address). The most significant benefit of this work is the high quality assignee disambiguation with worldwide coverage coupled with an inventor disambiguation that is competitive with other state of the art approaches. To our knowledge this is the broadest and most accurate simultaneous disambiguation and cross-linking of the inventor and assignee names for a significant fraction of patents in these three major patent collections.

cs.DL↗

arXiv2015

The Similarity of Global Value Chains: A Network-Based Measure

International trade has been increasingly organized in the form of global value chains (GVCs) where different stages of production are located in different countries. This recent phenomenon has substantial consequences for both trade policy design at the national or regional level and business decision making at the firm level. In this paper, we provide a new method for comparing GVCs across countries and over time. First, we use the World Input-Output Database (WIOD) to construct both the upstream and downstream global value networks, where the nodes are individual sectors in different countries and the links are the value-added contribution relationships. Second, we introduce a network-based measure of node similarity to compare the GVCs between any pair of countries for each sector and each year available in the WIOD. Our network-based similarity is a better measure for node comparison than the existing ones because it takes into account all the direct and indirect relationships between country-sector pairs, is applicable to both directed and weighted networks with self-loops, and takes into account externally defined node attributes. As a result, our measure of similarity reveals the most intensive interactions among the GVCs across countries and over time. From 1995 to 2011, the average similarity between sectors and countries have clear increasing trends, which are temporarily interrupted by the recent economic crisis. This measure of the similarity of GVCs provides quantitative answers to important questions about dependency, sustainability, risk, and competition in the global production system.

physics.soc-ph↗

arXiv2015

Identifying Geographic Clusters: A Network Analytic Approach

In recent years there has been a growing interest in the role of networks and clusters in the global economy. Despite being a popular research topic in economics, sociology and urban studies, geographical clustering of human activity has often studied been by means of predetermined geographical units such as administrative divisions and metropolitan areas. This approach is intrinsically time invariant and it does not allow one to differentiate between different activities. Our goal in this paper is to present a new methodology for identifying clusters, that can be applied to different empirical settings. We use a graph approach based on k-shell decomposition to analyze world biomedical research clusters based on PubMed scientific publications. We identify research institutions and locate their activities in geographical clusters. Leading areas of scientific production and their top performing research institutions are consistently identified at different geographic scales.

physics.soc-ph↗

arXiv2014

Global Value Trees

The fragmentation of production across countries has become an important feature of the globalization in recent decades and is often conceptualized by the term, global value chains (GVCs). When empirically investigating the GVCs, previous studies are mainly interested in knowing how global the GVCs are rather than how the GVCs look like. From a complex networks perspective, we use the World Input-Output Database (WIOD) to study the global production system. We find that the industry-level GVCs are indeed not chain-like but are better characterized by the tree topology. Hence, we compute the global value trees (GVTs) for all the industries available in the WIOD. Moreover, we compute an industry importance measure based on the GVTs and compare it with other network centrality measures. Finally, we discuss some future applications of the GVTs.

q-fin.GN↗

arXiv2014

Reputation and Impact in Academic Careers

Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here we develop an original framework for measuring how a publication's citation rate $Δc$ depends on the reputation of its central author $i$, in addition to its net citation count $c$. To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly-cited scientists, using the total citations $C_{i}$ of each scientist as his/her reputation measure. We find a citation crossover $c_{\times}$ which distinguishes the strength of the reputation effect. For publications with $c < c_{\times}$, the author's reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in $C_{i}$. However, the reputation effect becomes negligible for highly cited publications meaning that for $c\geq c_{\times}$ the citation rate measures scientific impact more transparently. In addition we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science.

physics.soc-ph↗

arXiv2014

World Input-Output Network

Economic systems, traditionally analyzed as almost independent national systems, are increasingly connected on a global scale. Only recently becoming available, the World Input-Output Database (WIOD) is one of the first efforts to construct the multi-regional input-output (MRIO) tables at the global level. By viewing the world input-output system as an interdependent network where the nodes are the individual industries in different economies and the edges are the monetary goods flows between industries, we study the network properties of the so-called world input-output network (WION) and document its evolution over time. We are able to quantify not only some global network properties such as assortativity, clustering coefficient, and degree and strength distributions, but also its subgraph structure and dynamics by using community detection techniques. Over time, we detect a marked increase in cross-country connectivity of the production system, only temporarily interrupted by the 2008-2009 crisis. Moreover, we find a growing input-output regional community in Europe led by Germany and the rise of China in the global production system. Finally, we use the network-based PageRank centrality and community coreness measure to identify the key industries and economies in the WION and the results are different from the one obtained by the traditional final-demand-weighted backward linkage measure.

physics.soc-ph↗

arXiv2014

Cluster analysis of weighted bipartite networks: a new copula-based approach

In this work we are interested in identifying clusters of "positional equivalent" actors, i.e. actors who play a similar role in a system. In particular, we analyze weighted bipartite networks that describes the relationships between actors on one side and features or traits on the other, together with the intensity level to which actors show their features. The main contribution of our work is twofold. First, we develop a methodological approach that takes into account the underlying multivariate dependence among groups of actors. The idea is that positions in a network could be defined on the basis of the similar intensity levels that the actors exhibit in expressing some features, instead of just considering relationships that actors hold with each others. Second, we propose a new clustering procedure that exploits the potentiality of copula functions, a mathematical instrument for the modelization of the stochastic dependence structure. Our clustering algorithm can be applied both to binary and real-valued matrices. We validate it with simulations and applications to real-world data.

physics.data-an↗

arXiv2014

The Rise of China in the International Trade Network: A Community Core Detection Approach

Theory of complex networks proved successful in the description of a variety of static networks ranging from biology to computer and social sciences and to economics and finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995-2011. We find rich dynamics over time both inter- and intra-communities. Most importantly, we have a multilevel description of the evolution where the global dynamics (i.e., communities disappear or reemerge) tend to be correlated with the regional dynamics (i.e., community core changes between community members). In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. Moreover, simulation results show that the global dynamics can be generated by a preferential attachment mechanism both inter- and intra-communities.

physics.soc-ph↗

arXiv2014

Network communities within and across borders

We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technological policy-making, highlighting the interplay between pressure for the internationalization to lead towards a global innovation system and the administrative borders imposed by the national and regional institutions. In this study we introduce an outreach index to quantify the impact of borders on the community structure and apply it to the case of the European and US patent co-inventors networks. We find that (a) the US connectivity decays as a power of distance, whereas we observe a faster exponential decay for Europe; (b) European network communities essentially correspond to nations and contiguous regions while US communities span multiple states across the whole country without any characteristic geographic scale. We confirm our findings by means of a set of simulations aimed at exploring the relationship between different patterns of cross-border community structures and the outreach index.

physics.soc-ph↗

arXiv2013

The Relation Between Global Migration and Trade Networks

In this paper we develop a methodology to analyze and compare multiple global networks. We focus our analysis on the relation between human migration and trade. First, we identify the subset of products for which the presence of a community of migrants significantly increases trade intensity. To assure comparability across networks, we apply a hypergeometric filter to identify links for which migration and trade intensity are both significantly higher than expected. Next we develop an econometric methodology, inspired by spatial econometrics, to measure the effect of migration on international trade while controlling for network interdependencies. Overall, we find that migration significantly boosts trade across sectors and we are able to identify product categories for which this effect is particularly strong.

physics.soc-ph↗

arXiv2013

Is Europe Evolving Toward an Integrated Research Area?

An integrated European Research Area (ERA) is a critical component for a more competitive and open European R&D system. However, the impact of EU-specific integration policies aimed at overcoming innovation barriers associated with national borders is not well understood. Here we analyze 2.4 x 10^6 patent applications filed with the European Patent Office (EPO) over the 25-year period 1986-2010 along with a sample of 2.6 x 10^5 records from the ISI Web of Science to quantitatively measure the role of borders in international R&D collaboration and mobility. From these data we construct five different networks for each year analyzed: (i) the patent co-inventor network, (ii) the publication co-author network, (iii) the co-applicant patent network, (iv) the patent citation network, and (v) the patent mobility network. We use methods from network science and econometrics to perform a comparative analysis across time and between EU and non-EU countries to determine the "treatment effect" resulting from EU integration policies. Using non-EU countries as a control set, we provide quantitative evidence that, despite decades of efforts to build a European Research Area, there has been little integration above global trends in patenting and publication. This analysis provides concrete evidence that Europe remains a collection of national innovation systems.

physics.soc-ph↗

arXiv2012

Persistence and Uncertainty in the Academic Career

Understanding how institutional changes within academia may affect the overall potential of science requires a better quantitative representation of how careers evolve over time. Since knowledge spillovers, cumulative advantage, competition, and collaboration are distinctive features of the academic profession, both the employment relationship and the procedures for assigning recognition and allocating funding should be designed to account for these factors. We study the annual production n_{i}(t) of a given scientist i by analyzing longitudinal career data for 200 leading scientists and 100 assistant professors from the physics community. We compare our results with 21,156 sports careers. Our empirical analysis of individual productivity dynamics shows that (i) there are increasing returns for the top individuals within the competitive cohort, and that (ii) the distribution of production growth is a leptokurtic "tent-shaped" distribution that is remarkably symmetric. Our methodology is general, and we speculate that similar features appear in other disciplines where academic publication is essential and collaboration is a key feature. We introduce a model of proportional growth which reproduces these two observations, and additionally accounts for the significantly right-skewed distributions of career longevity and achievement in science. Using this theoretical model, we show that short-term contracts can amplify the effects of competition and uncertainty making careers more vulnerable to early termination, not necessarily due to lack of individual talent and persistence, but because of random negative production shocks. We show that fluctuations in scientific production are quantitatively related to a scientist's collaboration radius and team efficiency.

physics.soc-ph↗