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This document illustrates the results of a discussion of two multi-disciplinary expert panels on pricing and reimbursement of medicines. Experts work in different organizations. The discussion focused on comparator(s), endpoint(s), negotiation of prices of new medicines and/or indications to include in the List 648, as well as the role of cost-effectiveness in the price and reimbursement negotiation. The debate took place during the fourth edition of the Seminari di Mogliano, organized on the 30th of September/1st of October, 2021. The two panels agreed on a general need to enhance interaction among the different stakeholders, in the early assessment and negotiation phases, and to increase the transparency/reproducibility of the decisions taken. The experts have also emphasized the need (i) to improve clarity in the evaluation of additional therapeutic value and the place in therapy with respect to comparators and how comparators are identified; (ii) to create work groups to identify the most appropriate endpoint(s), for each therapeutic area and level of unmet needs; (iii) to provide for a systematic use of cost-effectiveness when an added therapeutic value is delivered by a new medicine. With regard to the 648 List, the experts advocated for an overall reorganization of the current rules governing the special uses of drugs.

This paper investigates the unequal effect of Airbnb on the spatial organisation of economic activity in Madrid, Spain. Using establishment-level data from Madrid City Council and consumer-facing information from this short-term rental company, we find that Airbnb reshapes the urban space by encouraging tourist-oriented businesses, defined as businesses where tourists spend more than locals, at the expense of businesses primarily oriented to locals. These findings prove that short-term rentals do displace not only the local population but also resident-oriented businesses. Ultimately, our findings remain solid regardless of how we measure short-term rental activity and are not influenced by the growth of e-commerce or gentrification.

We present a one principal/two agents model of price-volume agreement, which involves one buyer (the principal) and competition between two manufacturers (the two agents). A theoretical analysis is presented, showing that, in accordance with empirical evidence from the literature on price-volume agreements in the pharmaceutical sector, manufacturers’ competition in a price-volume agreement scheme can decrease buyer’s expenditure especially when the second manufacturer, which has the possibility to enter (or not enter) later, has sufficiently small marginal production cost. Moreover, it is shown that the adoption of a price-volume agreement scheme for the first manufacturer can benefit the buyer with respect to the case in which single-price contracts are proposed to the former. The paper contributes to fill a gap in the literature about theoretical models of price-volume agreements, which are still scarce according to a recent review.

Social soft skills are crucial for workers to perform their tasks, yet it is hard to train people on them and to readapt their skill set when needed. In the present work, we analyze the possible effects of the COVID-19 pandemic on social soft skills in the context of Italian occupations related to 88 economic sectors and 14 age groups. We leverage detailed information coming from ICP (i.e. the Italian equivalent of O*Net), provided by the Italian National Institute for the Analysis of Public Policy, from the microdata for research on the continuous detection of labor force, provided by the Italian National Institute of Statistics (ISTAT), and from ISTAT data on the Italian population. Based on these data, we simulate the impact of COVID-19 on workplace characteristics and working styles that were more severely affected by the lockdown measures and the sanitary dispositions during the pandemic (e.g. physical proximity, face-to-face discussions, working remotely). We then apply matrix completion—a machine-learning technique often used in the context of recommender systems—to predict the average variation in the social soft skills importance levels required for each occupation when working conditions change, as some changes might be persistent in the near future. Professions, sectors, and age groups showing negative average variations are exposed to a deficit in their social soft-skills endowment, which might ultimately lead to lower productivity.

With the increasing pervasiveness of ICTs in the fabric of economic activities, the corporate digital divide has emerged as a new crucial topic to evaluate the IT competencies and the digital gap between firms and territories. Given the scarcity of available granular data to measure the phenomenon, most studies have used survey data. To bridge the empirical gap, we scrape the website homepage of 182 705 Italian firms, extracting ten features related to their digital footprint characteristics to develop a new corporate digital assessment index. Our results highlight a significant digital divide across dimensions, sectors and geographical locations of Italian firms, opening up new perspectives on monitoring and near-real-time data-driven analysis.

MNCs often engage in international research collaborations with foreign universities through one of their central RD laboratories (at headquarters or elsewhere) even though they operate a local RD unit close to that university, and hence forego the benefits of geographic proximity and local collaboration. Drawing on the knowledge-based theory of the firm, we hypothesize that the choice between distant and local collaboration systematically relates to the knowledge capabilities of the firms' RD units, the characteristics of the focal knowledge, and local knowledge leakage risks. Analysis of close to 13,000 research collaborations with foreign universities by the world's major biopharmaceutical firms (1995-2015) confirms that collaboration at distance occurs if this allows the firm to benefit from scale and knowledge diversity advantages, if the central unit has strong basic research capabilities, and if collaboration is in a core research domain of the MNC while rival firms are locally present. Maturity of the focal research domain is associated with local collaboration. Our findings qualify the common arguments in favor of collaboration in proximity and suggest that (distant) central RD units are important orchestrators of research collaboration with universities around the globe.Les entreprises multinationales s'engagent souvent dans des collaborations de recherche internationales avec des universités étrangères par le biais de l'un de leurs laboratoires centraux de RD (au siège ou ailleurs) même si elles exploitent une unité de RD locale, proche de cette université. Elles renoncent ainsi aux avantages de la proximité géographique et de la collaboration locale. Nous appuyant sur la théorie de la firme fondée sur la connaissance, nous développons l’hypothèse que le choix entre les collaborations distantes et locales soit systématiquement lié aux capacités en matière de connaissances des unités de RD des entreprises, aux caractéristiques des connaissances focales et aux risques de fuites de connaissances locales. L'analyse de près de 13000 collaborations de recherche des principales entreprises biopharmaceutiques (1995-2015) avec des universités étrangères confirme que la collaboration à distance se produira si cela permet à l'entreprise de bénéficier des avantages d'échelle et de diversité des connaissances, si l'unité centrale dispose de solides capacités de recherche fondamentale, et si la collaboration s’inscrit dans un domaine de recherche stratégique des multinationales alors que des entreprises concurrentes sont localement présentes. La maturité du domaine de recherche focal est associée à la collaboration locale. Nos résultats nuancent l'argumentation courante en faveur de la collaboration à proximité, et suggèrent que les unités centrales (et distantes) de RD soient d'importants orchestrateurs de la collaboration de recherche avec les universités du monde entier.Las empresas multinacionales con frecuencia participan en colaboraciones de investigación internacionales con universidad extranjeras mediante uno de sus laboratorios de I+D centrales (en la casa matriz o en otra parte) aún cuando operan una unidad de I+D local cerca a esa universidad, y de ahí que proceden los beneficios de la proximidad geográfica y la colaboración local. Basándonos en la teoría de la empresa basada en el conocimiento, planteamos la hipótesis de que la elección entre la colaboración a distancia y la local está sistemáticamente relacionada con las capacidades de conocimiento de las unidades de I+D de las empresas, las características del conocimiento focal y los riesgos de fuga de conocimiento local. Una análisis de cerca de 13.000 colaboraciones de investigación con universidades extranjeras por las empresas biofarmacéuticas más importantes del mundo (1995-2015) confirma que la colaboración a distancia se produce si esto permite a la empresa beneficiarse de las ventajas de escala y de la diversidad de conocimientos, si la unidad central tiene fuertes capacidades de investigación básica, y si la colaboración se produce en un dominio de investigación central de la empresa multinacional mientras que las empresas rivales están presentes a nivel local. La madurez del ámbito de investigación central está asociada a la colaboración local. Nuestros hallazgos habilitan la argumentación común a favor de la colaboración en proximidad y sugieren que las unidades centrales de I+D (distantes) son importantes orquestadoras de la colaboración en investigación con las universidades de todo el mundo.MNCs frequentemente se envolvem em colaborações de pesquisa internacionais com universidades estrangeiras por meio de um de seus laboratórios centrais de RD (na sede ou em outro lugar), embora operem uma unidade local de RD perto dessa universidade e, portanto, renunciam aos benefícios da proximidade geográfica e colaboração local. Com base na teoria baseada no conhecimento da firma, formulamos a hipótese de que a escolha entre colaboração distante e local se relaciona sistematicamente com as capacidades de conhecimento das unidades de RD das empresas, as características do conhecimento focal e os riscos locais de vazamento de conhecimento. Análise de cerca de 13.000 colaborações de pesquisa com universidades estrangeiras pelas principais empresas biofarmacêuticas do mundo (1995-2015) confirma que a colaboração à distância ocorre se isso permite que a firma se beneficie de vantagens de escala e diversidade de conhecimento, se a unidade central tiver fortes capacidades básicas de pesquisa , e se a colaboração está em um domínio de pesquisa central da MNC enquanto empresas rivais estão localmente presentes. A maturidade do domínio de pesquisa focal está associada à colaboração local. Nossas descobertas qualificam a argumentação comum em favor da colaboração na proximidade e sugerem que as unidades centrais (distantes) de RD são importantes orquestradoras da colaboração em pesquisa com universidades ao redor do mundo.跨国公司(MNC)经常通过其中央研发实验室中的一个 (在总部或其它地方) 来与外国大学开展国际研究合作, 即使它们在该大学附近设有本地研发机构, 也会放弃地理位置接近和本地合作的益处。借鉴企业知识基础观, 我们假设在远程合作与本地合作之间进行的选择与企业研发机构的知识能力、焦点知识的特征以及本地知识泄漏风险有关。对世界主要生物制药公司与国外大学的近13,000项研究合作进行的分析 (1995-2015年) 证实, 远程合作在如下情况下发生: 如果这可以使公司从规模和知识多样化的优势中受益、如果中央机构具有强大的基础研究能力, 以及如果进行的合作是该MNC的核心研究领域而竞争公司在本地存在。焦点研究领域的成熟度与本地合作有关。我们的发现证实了支持近距离合作的普遍论点, 并表明 (远程) 中央研发机构是与全球大学开展研究合作的重要协调者。.

This paper investigates how guests' preferences in peer-to-peer (P2P) accommodations changed during the COVID-19 summer season. Specifically, we test the importance of attributes that better allow ...

This paper investigates the key driving features of the evolving long-term division of innovative labor in biotechnology and pharmaceuticals from 1981 to 2012. Our main goal is to find if technological trajectories and mechanisms discovered by Orsenigo et al. (Res Policy 30(3): 485–508, 2001) as the main drivers of the structural configuration of the network of collaborative alliances have been at work in the long-term evolution of the industry. We extensively analyze the evolving dynamics of the degree distribution and the higher order properties of the R&D network. As in Orsenigo et al. (Res Policy 30(3): 485–508, 2001), we find that polarization through preferential attachment driven by large pharmaceutical companies as Developers and by the entry of new specialized biotechnology companies acting as Originators of new R&D opportunities dominated the early stages of the biotechnology revolution. Later on the evolution of the collaborative network has been shaped by roles’ transitions between Originators and Developers of innovative ideas. Against this background, we introduce parsimonious model of network formation and evolution is introduced, to account for some essential features of the data generating processes underlying the evolution of the network.

Corporate groups or networks, which are defined as collections of two or more legally independent firms connected through ownership ties, are prevalent in both developed and developing countries. The separate effects of firm centrality and corporate group size on firm performance have been largely discussed in the literature. However, the effect of their interaction remains underexplored. This paper attempts to fill the gap by introducing a new normalized measure of firm centrality independent of corporate group size. Our empirical analysis is based on a set of global corporate networks encompassing 17.8 million firms and the ownership ties between them extracted from the ORBIS database in 2014. We further investigate the firm performance in the corporate networks above by gathering financial information on 483,835 Italian firms from the AIDA database from 2014 to 2016. We find a positive relationship between firm centrality and firm performance, but the significance of the relationship decreases as corporate group size increases.

This paper contributes to the literature on the relationship between innovation and exports by developing a model that emphasizes the role of product innovation in explaining heterogeneity in export behaviour both across and within firms. The dynamic model assumes that firms invest to maintain and increase the portfolio of products they sell: innovation is a stochastic process whereby the probability to capture new business opportunities is a function of the number of goods already sold. Crucially, the model assumes two independent mechanisms to drive the extensive and the intensive margins of a firm's export. The resulting lack of (built‐in) correlation between the two margins is well reflected in the data and represents the main contribution of our theoretical framework. The model is consistent with several other empirical regularities that characterize multi‐product firms, such as the heavy tail in the distribution of the number of products exported by each firm, the strict hierarchy in the sales of products across markets, the substantial degree of product churning and the highly skewed distributions of export sales. Resume Innovation, commerce et entreprises pluriproductrices. En matiere de rapport entre innovation et exportation, et afin d’expliquer l’heterogeneite des comportements d’exportations a la fois entre et au sein meme des entreprises, notre article apporte une contribution a la litterature existante en developpant un modele soulignant l’importance de l’innovation. Ce modele dynamique suppose que les entreprises investissent dans le but de maintenir et d’accroitre leur gamme de produits: l’innovation est donc un processus stochastique ou la probabilite de saisir de nouveaux debouches commerciaux depend du volume de produits deja vendus. Dans une large mesure, ce modele suppose deux mecanismes independants permettant de generer les marges intensives et extensives des exportations d’une entreprise. L’absence de lien entre les deux marges transparait dans nos donnees, constituant ainsi la contribution principale a notre cadre theorique. Ce modele est conforme a d’autres regularites empiriques caracteristiques des entreprises pluriproductrices, notamment une distribution a queue lourde par rapport au nombre de produits exportes par chaque entreprise, une stricte hierarchie des ventes de produits sur les marches, un degre eleve d’introduction de nouveaux produits et une distribution tres inegale des ventes a l’exportation.

Technological progress has been recently associated with a crowding-out of cognitive-skill intensive jobs in favour of jobs requiring soft skills, such as ones related to social intelligence, flexibility and creativity. The nature of soft skills makes them hardly replaceable by machine work and among subsets of soft skills, creativity is one of the hardest to define and codify. Therefore, creativity-intensive occupations have been shielded from automation. Given this framework, our study contributes to a nascent field on interdisciplinary research to predict the impact of artificial intelligence on work activities and future jobs using machine learning. In our work, we focus on creativity, starting from its possible definitions, then we get significant insights on creativity patterns and dynamics in the Italian labour market, using a machine learning approach. We make use of the INAPP-ISTAT Survey on Occupations (ICP), where we identify 25 skills associated with creativity. Then, we apply matrix completion—a machine learning technique which is often used by recommender systems—to predict the average importance levels of various creative skills for each profession, showing its excellent prediction capability for the specific problem. We also find that matrix completion typically underestimates the average importance levels of soft skills associated with creativity, especially in the case of professions belonging to the major group of legislators, senior officials and managers, as well as intellectual professionals. Conversely, overestimates are typically obtained for other professions, which may be associated with a higher risk of being automated.

We design an experiment to study investment behavior in different repeated contest settings, varying the uncertainty of the outcomes and the number of participants in contests. We find decreasing over-expenditures and a higher rate of ‘dropout’ in contests with high uncertainty over outcomes (winner-take-all contests), while we detect a quick convergence toward equilibrium predictions and a near to full participation when this type of uncertainty vanishes (proportional-prize contests). These results are robust to changes in the number of contestants. A learning parameter estimation using the experience-weighted attraction (EWA) model suggests that subjects adopt different learning modes across different contest structures and helps to explain expenditure patterns deviating from theoretical predictions.

The debate around unmet clinical need (UCN) is still very much alive. How do we define UCN? How does it influence the definition of clinically relevant outcomes in a therapeutic area? Who defines UCN? What are the consequences of recognizing different grading of UCN? In this paper we will address these questions and finally formulate proposals for the Italian context. The paper is based on a discussion within a panel of experts. This topic is even more stimulating as this work takes place in a historical period which, on the one hand, sees the start of a new course of negotiation rules recently published by AIFA and, on the other hand, poses unprecedented challenges that emerged during the pandemic crisis. The working group formulated suggestions and proposals to further enhance the role of the UCN in decision-making processes, also in the light of the new negotiation procedure, and to help refine the tools for grading the UCN and the value of medicines in the interests of patients and society as a whole.

This paper analyses the impact of the Italian Start Up Act which entered into force in October 2012. This public policy provides a unique bundle of benefits, such as tax incentives, public loan guarantees, and a more flexible labor law, for firms registered as “innovative startups” in Italy. This legislation has been implemented by the Italian government to increase innovativeness of small and young enterprises by facilitating improved access to (external) capital and (high-skilled) labor. Consequently, the goal of our evaluation is to assess the impact of the policy on equity, debt and employment. Using various conditional difference-in-difference models, we find that the Italian innovative startup policy has met its primary objectives. The econometric results strongly suggest that Italian innovative startups are more successful in obtaining equity and debt capital and they also hire more employees because of the program participation. ispartof: Small Business Economics vol:58 issue:3 pages:1699-1720 status: published

The world’s premier metropolitan areas (‘global cities’) are key nodes in international business networks and function as important international innovation hubs. They are prominent spaces for knowledge exchange and collaboration on innovation yet their specific and changing role in global innovation networks has not received due attention. This chapter contributes an analysis of the changing role of global cities in global collaborative innovation networks, 2000–2014, by examining co-invention linkages across 125 global cities in forty-six countries. The international linkages of global cities have increased substantially over the period. Growth has been most pronounced in a number of Asian cities that rank among the top cities in the world in the most recent period. The patterns attest to the growing importance of international collaboration for innovation and the premier position of global cities as spaces facilitating such collaboration.

The COVID-19 outbreak has posed an unprecedented challenge to humanity and science. On the one side, public and private incentives have been put in place to promptly allocate resources toward research areas strictly related to the COVID-19 emergency. However, research in many fields not directly related to the pandemic has been displaced. In this paper, we assess the impact of COVID-19 on world scientific production in the life sciences and find indications that the usage of medical subject headings (MeSH) has changed following the outbreak. We estimate through a difference-in-differences approach the impact of the start of the COVID-19 pandemic on scientific production using the PubMed database (3.6 Million research papers). We find that COVID-19-related MeSH terms have experienced a 6.5 fold increase in output on average, while publications on unrelated MeSH terms dropped by 10 to 12%. The publication weighted impact has an even more pronounced negative effect (-16% to -19%). Moreover, COVID-19 has displaced clinical trial publications (-24%) and diverted grants from research areas not closely related to COVID-19. Note that since COVID-19 publications may have been fast-tracked, the sudden surge in COVID-19 publications might be driven by editorial policy.

IntroductionSeveral countries imposed nationwide or partial lockdowns to limit the spread of COVID-19 and avoid overwhelming hospitals and intensive care units. Lockdown may involve restriction of movement, stay-at-home orders and self-isolation, which may have dramatic consequences on mental health. Recent studies demonstrated that the negative impact of lockdown restrictions depends on a wide range of psychological and socio-demographic factors.AimsThis longitudinal study aimed to understand how internal factors such as personality and mindfulness traits, and external factors, such as daily habits and house features, affect anxiety, depression and general wellbeing indicators, as well as cognitive functions, during the course of a lockdown.MethodsTo address these questions, 96 participants in Italy and the United Kingdom filled out a survey, once a week for 4 weeks, during the first-wave lockdowns. The survey included questions related to their habits and features of the house, as well as validated questionnaires to measure personality traits, mindful attitude and post-traumatic symptoms. Indicators of wellbeing were the affective state, anxiety, stress and psychopathological indices. We also measured the emotional impact of the pandemic on cognitive ability by using two online behavioral tasks [emotional Stroop task (EST) and visual search].ResultsWe found that internal factors influenced participants’ wellbeing during the first week of the study, while external factors affected participants in the last weeks. In the first week, internal variables such as openness, conscientiousness and being non-judgmental toward one’s own thoughts and emotions were positively associated with wellbeing; instead, neuroticism and the tendency to observe and describe one’s own thoughts and emotions had detrimental effects on wellbeing. Toward the end of the study, external variables such as watching television and movies, browsing the internet, walking the dog, and having a balcony showed a protective value, while social networking and engaging in video calls predicted lower values of wellbeing. We did not find any effects of wellbeing on cognitive functioning.ConclusionRecognizing specific traits and habits affecting individuals’ wellbeing (in both short and long terms) during social isolation is crucial to identify people at risk of developing psychological distress and help refine current guidelines to alleviate the psychological consequences of prolonged lockdowns.

This work applies Matrix Completion (MC) – a class of machine-learning methods commonly used in recommendation systems – to analyze economic complexity. In this paper MC is applied to reconstruct the Revealed Comparative Advantage (RCA) matrix, whose elements express the relative advantage of countries in given classes of products, as evidenced by yearly trade flows. A high-accuracy binary classifier is derived from the MC application to discriminate between elements of the RCA matrix that are, respectively, higher/lower than one. We introduce a novel Matrix cOmpletion iNdex of Economic complexitY (MONEY) based on MC and related to the degree of predictability of the RCA entries of different countries (the lower the predictability, the higher the complexity). Differently from previously-developed economic complexity indices, MONEY takes into account several singular vectors of the matrix reconstructed by MC. In contrast, other indices are based only on one/two eigenvectors of a suitable symmetric matrix derived from the RCA matrix. Finally, MC is compared with state-of-the-art economic complexity indices, showing that the MC-based classifier achieves better performance than previous methods based on the application of machine learning to economic complexity.

This paper analyzes the impact of mobility contraction on employee furlough and excess deaths in Italy during the COVID-19 crisis. Our approach exploits rainfall patterns across Italian administrative regions as a source of exogenous variation in human mobility to pinpoint the causal effect of mobility restrictions on excess deaths and furlough workers. Results confirm that the first countrywide lockdown has effectively curtailed the COVID-19 epidemics restricting it mainly to the northern part of the country, with the drawback of a countrywide increase in unemployment risk. Our analysis points out that a mobility contraction of 1% leads to a mortality reduction of 0.6%, but it induces an increase of 10% in Wage Guarantee Funds allowed hours. We discuss return-to-work policies and prioritizing policies for administering COVID-19 vaccines in the most advanced stage of a vaccination campaign when the healthy active population is left to be vaccinated.

This paper investigates the relations between festivals and their institutional settings, and how these relations shape festival management. We analyze institutional logics and work practices in a complex project, Lucca Comics & Games, which is the biggest festival dedicated to fantasy culture in Italy. Our results, based on archival research, media coverage and interview data, reveal the recursive interplay between institutional logics and work practices for institutional maintenance. We argue that the study of festivals is essential to understanding key aspects of project-based organizing more generally. In this regard, the maintaining of institutions must be distinguished from stability or the absence of change and consideration of historical and contextual analysis, and multiple tensions that occur due to competing institutional logics can open up new ways of thinking for prospective research. We also demonstrate that future research can use institutional work practices for understanding institutional maintenance in recurring temporary organizations.

With pharmaceutical health policy striving for fair and sustainable pricing under increasing budgetary pressures, public stakeholders are more and more willing to be involved in transparent access decision-making related to novel medicines, considered by them to be a societal good. Full net price transparency (NPT) is believed by many to promote price competition and to increase equity by making pharmaceutical products accessible to all. Using agent-based simulations, we find that a full NPT system implemented across EU countries would not be viable. This while, acting as rational economic agents, a group of middle- and lower-income countries would not be willing to give up their confidential agreements with the pharmaceutical industry. Even partial NPT would delay access predominantly in middle- to lower-income countries. Hence, we conclude that implementing net price transparency across Europe would be challenging to reach from a political perspective. Especially in lower-income countries there would remain a plea to be left free to negotiate confidential discounts with drug manufacturers. This while, counterintuitively, in those countries NPT will be seen to be unjust while violating Ramsey pricing and distributive justice principles.

Recent studies on international networks have investigated the role of global cities as catalysts of knowledge flows. In this paper, we examine global cities in transnational networks of innovators. To assess the likelihood of scientists and inventors from different cities to take part in international teams, we propose a measure based on 3-hyperedges in the collaboration network constructed from scientific publications and patents. This measure is used to identify the most competitive global cities in the international network of innovators. To this end, we construct a null model based on the hypergeometric ensembles of random graphs. We find that five US cities play a leading role in transnational networks of innovators. Among them, San Francisco stands out as the most globalized city.

The COVID-19 outbreak has posed an unprecedented challenge to humanity and science. On the one side, public and private incentives have been put in place to promptly allocate resources toward research areas strictly related to the COVID-19 emergency. However, research in many fields not directly related to the pandemic has been displaced. In this paper, we assess the impact of COVID-19 on world scientific production in the life sciences and find indications that the usage of medical subject headings (MeSH) has changed following the outbreak. We estimate through a difference-in-differences approach the impact of the start of the COVID-19 pandemic on scientific production using the PubMed database (3.6 Million research papers). We find that COVID-19-related MeSH terms have experienced a 6.5 fold increase in output on average, while publications on unrelated MeSH terms dropped by 10 to 12%. The publication weighted impact has an even more pronounced negative effect (-16% to -19%). Moreover, COVID-19 has displaced clinical trial publications (-24%) and diverted grants from research areas not closely related to COVID-19. Note that since COVID-19 publications may have been fast-tracked, the sudden surge in COVID-19 publications might be driven by editorial policy.

Proponents describe stem Cell Replacement Therapy and related technologies to be a significant step forward for medicine. However, due to the inherent ethical problems in human Embryonic Stem Cell Research (hESC), it is strictly regulated around the world. The US has passed at the federal and state level, both supportive and restrictive laws over the years. The changing legislative environment at the state and federal levels has created a situation whereby researchers have to choose whether and where to carry out this research. By exploiting the temporal and spatial heterogeneity and legislative shocks, we assess if the affected scientists have voted with their feet, leaving the state or country imposing restrictive rules and whether hESC research has clustered geographically. We find that most of the hESC research is carried out in supportive states, and significant legislative changes have had a minor but noticeable effect on relocation choices. Most importantly, the research has moved to supportive states. This result suggests that several state-level interventions (supportive), which were opposed to federal laws (restrictive), have counteracted each other.

In pharmaceutical research, assessing drug candidates’ odds of success as they move through clinical research often relies on crude methods based on historical data. However, the rapid progress of machine learning offers a new tool to identify the more promising projects. To evaluate its usefulness, we trained and validated several machine learning algorithms on a large database of projects. Using various project descriptors as input data we were able to predict the clinical success and failure rates of projects with an average balanced accuracy of 83% to 89%, which compares favorably with the 56% to 70% balanced accuracy of the method based on historical data. We also identified the variables that contributed most to trial success and used the algorithm to predict the success (or failure) of assets currently in the industry pipeline. We conclude by discussing how pharmaceutical companies can use such model to improve the quantity and quality of their new drugs, and how the broad adoption of this technology could reduce the industry’s risk profile with important consequences for industry structure, R&D investment, and the cost of innovation

We propose an ensemble learning methodology to forecast the future US GDP growth release. Our approach combines a Recurrent Neural Network (RNN) with a Dynamic Factor model accounting for time-variation in the mean with a Generalized Autoregressive Score (DFM-GAS). We show how this combination improves forecasts in the aftermath of the 2008-09 global financial crisis, as well as in the latest COVID-19 recession, by reducing the root mean squared error for the short-term forecast horizon. Thus, we evaluate the predictive ability of each component of the ensemble by considering variations in the mean, as the latter are potentially caused by recessions affecting the economy. For our scope, we employ a set of predictors encompassing a wide range of variables measured at different time frequencies. Thus, we provide dynamic coefficients for predictors after an interpretable machine learning routine to assess how the model reflects the evolution of the business cycle.

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 chapter, 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 (1) startup and innovation, (2) growth and performance of companies, and (3) 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.

This work applies Matrix Completion (MC) -- a class of machine-learning methods commonly used in the context of recommendation systems -- to analyse economic complexity. MC is applied to reconstruct the Revealed Comparative Advantage (RCA) matrix, whose elements express the relative advantage of countries in given classes of products, as evidenced by yearly trade flows. A high-accuracy binary classifier is derived from the application of MC, with the aim of discriminating between elements of the RCA matrix that are, respectively, higher or lower than one. We introduce a novel Matrix cOmpletion iNdex of Economic complexitY (MONEY) based on MC, which is related to the predictability of countries' RCA (the lower the predictability, the higher the complexity). Differently from previously-developed indices of economic complexity, the MONEY index takes into account the various singular vectors of the matrix reconstructed by MC, whereas other indices are based only on one/two eigenvectors of a suitable symmetric matrix, derived from the RCA matrix. Finally, MC is compared with a state-of-the-art economic complexity index (GENEPY). We show that the false positive rate per country of a binary classifier constructed starting from the average entry-wise output of MC can be used as a proxy of GENEPY., Comment: The main paper contains 11 pages with the Appendix. Supplemental material is also reported

Global cities boast higher rates of innovation as measured through patent and scientific production. However, the source of the location advantage of innovation hubs is still debated in the literature, with arguments ranging from localized knowledge spillovers to network effects. Thanks to an extensive data set of individual scientist career paths, we shed new light on the role of scientist location choices in determining the superior innovative performance of global cities. We analyze the career paths of around two million researchers over a decade across more than two thousand cities around the globe. First, we show that scientists active in global cities are more productive in terms of citation weighted publications. We then show that this superior performance is in part driven by highly prolific scientists moving and remaining preferentially in global cities, i.e., central cities in the international scientist mobility network. The overall picture that emerges is that global cities are better positioned to attract and retain prolific scientists than more peripheral cities., Research Policy, 50 (1), ISSN:0048-7333, ISSN:1873-7625

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.

This paper starts by observing that many licensing contracts contain explicit organizational arrangements for transferring the licensed technology, involving repeated and close interaction between the licensing partners. We argue that these interactions provide opportunities for the licensor to learn from the licensee. Using data on 1,861 licensing deals of 254 pharmaceutical and biotech firms between 1995 and 2015, we show that licensors are more likely to cite the inventions from their licensing partner after an out-licensing deal than matched control firm-pairs that do not engage in licensing. The paper makes the following contributions: first, it demonstrates that not only licensees but also licensors can learn from licensing and that this reverse learning stems from the licensor-licensee relation. Second, it shows that firms can learn from directed outward knowledge transfers rather than non-deliberate knowledge spill-outs. Third, we show that the licensor's post-licensing behavior vis-à-vis the licensee reflects targeted learning by tapping into the most valuable components of the licensee's technology portfolio and those new to the licensor. Finally, the paper extends the theoretical framework behind strategic out-licensing decisions. We show that learning from out-licensing is an additional (positive) element in the trade-off faced by licensors in addition to short-term revenue generation and the risk of long-term rent dissipation. ispartof: Technovation vol:112 status: published

The issue of water access and security has been emphasized in the recent policy debate on sustainable development (Sustainable Development Goal No. 6) and adaptation to climate change (CoP21 in Paris, 2015). This study provides new evidence about the Blue Virtual Water Input-Output Network. The main novelty of our approach is the combination of Structural Decomposition Analysis (SDA) with Network Theory. SDA reveals that size-related, technological and structural components have contributed substantially to changes in virtual water use. Network analysis offers new insights about the vulnerability of the system to shocks through trade links across country-sector pairs. Our analysis highlights a possible trade-off in the increasing importance of virtual water trade: the efficiency improvement in granting access to virtual water might come at the cost of increasing systemic vulnerability. Overall, the great unbalance between water availability and usage combined with rigidity of global consumption and production networks and the risk of cascade effects imply increasing vulnerability of the virtual water network to shocks propagation.

This paper investigates the relationship between international trade and migration with the specific aim of estimating direct and indirect effect of the latter on cross-border flows of both homogeneous and differentiated goods. Adopting a spatial econometric approach along with a gravity model set-up, we account for the role of ethnic communities in neighbouring countries on trade, and we propose a new way to define neighbours based on the intensity of links in the migration network. Our approach is particularly well suited to measure the indirect effect stemming from the presence of significant ethnic communities on trade through a “market familiarization” effect. Using data covering all countries between 1970 and 2000, we find a significant indirect effect of migration on trade, that depends on the chosen weight matrix.

The paper investigates how the topological features of the virtual water (VW) network and the size of the associated VW flows are likely to change over time, under different socio-economic and climate scenarios. We combine two alternative models of network formation –a stochastic and a fitness model, used to describe the structure of VW flows- with a gravity model of trade to predict the intensity of each bilateral flow. This combined approach is superior to existing methodologies in its ability to replicate the observed features of VW trade. The insights from the models are used to forecast future VW flows in 2020 and 2050, under different climatic scenarios, and compare them with future water availability. Results suggest that the current trend of VW exports is not sustainable for all countries. Moreover, our approach highlights that some VW importers might be exposed to “imported water stress” as they rely heavily on imports from countries whose water use is unsustainable.

The proper characterization of the size distribution of business firms represents an important issue in economic literature, with the most common reference distribu- tions being the lognormal and the Pareto varieties. This analysis is related to some methodological issues that are rarely properly addressed in applied work, and may significantly affect the results: the major difficulties arise from low power of the tests caused by limited sample size and the common practice of binning the data. In this paper we contribute to this body of literature by analyzing the size distribu- tion of all French companies, strongly rejecting the hypothesis that it is a Pareto distribution. Moreover, we argue that the lognormal distribution is a more reason- able first-cut benchmark for the entire population of firms. This is especially true for single-product firms, while we show the emergence of a Zipf tail for the class of multi-product companies. Our findings are in strong agreement with some recent theoretical contributions, which predict that the size distribution depends on a set of industry specific determinants.

The relationship between international trade and foreign direct investment (FDI) is one of the main features of globalisation. 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 minimise 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. Network measures, as the shortest path length and the communicability, are then computed on the CCN 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 the manufacturing sector than for primary sectors such as oil extraction and agriculture.

We analyze data from Twitter to uncover early-warning signals of COVID-19 outbreaks in Europe in the winter season 2019-2020, before the first public announcements of local sources of infection were made. We show evidence that unexpected levels of concerns about cases of pneumonia were raised across a number of European countries. Whistleblowing came primarily from the geographical regions that eventually turned out to be the key breeding grounds for infections. These findings point to the urgency of setting up an integrated digital surveillance system in which social media can help geo-localize chains of contagion that would otherwise proliferate almost completely undetected., Comment: Work submitted to Scientific Reports

This paper analyses the impact of the Italian Startup Act which entered into force in December 2012. This public policy provides a unique bundle of benefits, such as tax incentives, public loan guarantees, and a more flexible labor law, for firms registered as “innovative startups” in Italy. This legislation has been implemented by the Italian government to increase innovativeness of small and young enterprises by facilitating improved access to (external) capital and (highskilled) labor. Consequently, the goal of our evaluation is to assess the impact of the policy on equity, debt and employment. Using various conditional difference-in-difference models, we find that the Italian startup policy has met its primary objectives. The econometric results strongly suggest that firms operating under this program are more successful in obtaining equity and debt capital and they also hire more employees because of the program participation.

Networks of portfolio holdings exemplify how interdependence both between the agents and their assets can be a source of systemic vulnerability. We study a real-world holdings network and compare it with various alternative scenarios from randomization and rebalancing of the original investments. Scenarios generation relies on algorithms that satisfy the global constraints imposed by the numbers of outstanding shares in the market. We consider fixed-diversification models and diversification-maximizing replicas too. We extensively analyze the interplay between portfolio diversification and differentiation, and how the outreach of exogenous shocks depends on these factors as well as on the type of shock and the size of the network with respect to the market. We find that real portfolios are poorly diversified but highly similar, that portfolio similarity correlates with systemic fragility and that rebalancing can come with an increased similarity depending on the initial network configuration. We show that a large diversification gain is achieved through rebalancing but, noteworthy, that makes the network vulnerable in front of unselective shocks. Also, while the network is riskier in the presence of targeted shocks, it is safer than its random counterparts when it is stressed by widespread price downturns.

With the increasing availability of online scholarly databases, publication records can be easily extracted and analysed. Researchers can promptly keep abreast of others' scientific production and, in principle, can select new collaborators and build new research teams. A critical factor one should consider when contemplating new potential collaborations is the possibility of unambiguously defining the expertise of other researchers. While some organisations have established database systems to enable their members to manually produce a profile, maintaining such systems is time-consuming and costly. Therefore, there has been a growing interest in retrieving expertise through automated approaches. Indeed, the identification of researchers' expertise is of great value in many applications, such as identifying qualified experts to supervise new researchers, assigning manuscripts to reviewers, and forming a qualified team. Here, we propose a network-based approach to the construction of authors' expertise profiles. Using the MEDLINE corpus as an example, we show that our method can be applied to a number of widely used data sets and outperforms other methods traditionally used for expertise identification., Journal of Economic Interaction and Coordination, 17 (2), ISSN:1860-711X, ISSN:1860-7128

In the current paper, we discuss the need for regulation at EU level of Connected and Automated Driving solutions (henceforth CAD) based on multiple considerations, namely (i) the need for uniformity of criteria across European Member States, and (ii) the impact that regulation—or the absence of it—has on the proliferation of specific technological solutions. The analysis is grounded on legal and economic considerations of possible interactions between vehicles with different levels of automation, and shows how the existing framework delays innovation. A Risk-Management Approach, identifying one sole responsible party ex ante (one-stop-shop), liable under all circumstances—pursuant to a strict, if not absolute liability rule—is to be preferred. We analyse the solution adopted by some Member States in light of those considerations and conclude that none truly corresponds to a RMA approach, and differences will also cause market fragmentation. We conclude that because legal rules determine what kind of technological application is favoured over others—and thence they are not technology-neutral—uniformity across MSs is of essential relevance, and discuss possible policy approaches to be adopted at European level.