The Italian Start Up Act: a microeconometric program evaluation.

This paper analyzes 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 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.

Talent goes to global cities: The world network of scientists' mobility.

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.

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 Ci of each scientist as his/her reputation measure. We find a citation crossover c×, which distinguishes the strength of the reputation effect. For publications with c < c×, 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 Ci. However, the reputation effect becomes negligible for highly cited publications meaning that, for c ≥ c×, 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.

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. Because 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. 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.

The impact of the European Union emissions trading system on carbon dioxide emissions: a matrix completion analysis.

Despite the negative externalities on the environment and human health, today's economies still produce excessive carbon dioxide emissions. As a result, governments are trying to shift production and consumption to more sustainable models that reduce the environmental impact of carbon dioxide emissions. The European Union, in particular, has implemented an innovative policy to reduce carbon dioxide emissions by creating a market for emission rights, the emissions trading system. The objective of this paper is to perform a counterfactual analysis to measure the impact of the emissions trading system on the reduction of carbon dioxide emissions. For this purpose, a recently-developed statistical machine learning method called matrix completion with fixed effects estimation is used and compared to traditional econometric techniques. We apply matrix completion with fixed effects estimation to the prediction of missing counterfactual entries of a carbon dioxide emissions matrix whose elements (indexed row-wise by country and column-wise by year) represent emissions without the emissions trading system for country-year pairs. The results obtained, confirmed by robust diagnostic tests, show a significant effect of the emissions trading system on the reduction of carbon dioxide emissions: the majority of European Union countries included in our analysis reduced their total carbon dioxide emissions (associated with selected industries) by about 15.4% during the emissions trading system treatment period 2005-2020, compared to the total carbon dioxide emissions (associated with the same industries) that would have been achieved in the absence of the emissions trading system policy. Finally, several managerial/practical implications of the study are discussed, together with its possible extensions.

Predicted Expenditure for Prescription Drugs for Multiple Sclerosis in the Italian Market Between 2023 and 2028: Results of the Oracle Project.

INTRODUCTION: Multiple sclerosis (MS) is a chronic neurodegenerative disease that leads to impaired cognitive function and accumulation of disability, with significant socioeconomic burden. Serious unmet need in the context of managing MS has given rise to ongoing research efforts, leading to the launch of new drugs planned for the near future, and subsequent concerns about the sustainability of healthcare systems. This study assessed the changes in the Italian MS market and their impact on the expenditures of the Italian National Healthcare Service between 2023 and 2028. METHODS: A horizon-scanning model was developed to estimate annual expenditure from 2023 to 2028. Annual expenditure for MS was calculated by combining the number of patients treated with each product (clinical inputs) and the yearly costs of therapy (economic inputs). Baseline inputs (2020-2022) were collected from IQVIA® real-world data, while input estimation for the 5-year forecast was integrated with analog analyses and the insights of clinicians and former payers. RESULTS: The number of equivalent patients treated in 2028 in Italy was estimated at around 67,000, with an increase of 10% versus 2022. In terms of treatment pattern evolution, first-line treatments are expected to reduce their shares from 47% in 2022 to 27% in 2028, and Bruton tyrosine kinase inhibitors are expected to reach 23% of patient shares. Overall, expenditure for MS is estimated to decrease from €721 million in 2022 to €551 million in 2028, mainly due to losses of exclusivity and renegotiation of drug prices. CONCLUSION: Despite the increase in the number of patients treated for MS and the launch of new molecules that will reach high market penetration, the model confirmed sustainability for the Italian National Healthcare Service.

The emergence of social soft skill needs in the post COVID-19 era.

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.

Predicting choice behaviour in economic games using gaze data encoded as scanpath images.

Eye movement data has been extensively utilized by researchers interested in studying decision-making within the strategic setting of economic games. In this paper, we demonstrate that both deep learning and support vector machine classification methods are able to accurately identify participants' decision strategies before they commit to action while playing games. Our approach focuses on creating scanpath images that best capture the dynamics of a participant's gaze behaviour in a way that is meaningful for predictions to the machine learning models. Our results demonstrate a higher classification accuracy by 18% points compared to a baseline logistic regression model, which is traditionally used to analyse gaze data recorded during economic games. In a broader context, we aim to illustrate the potential for eye-tracking data to create information asymmetries in strategic environments in favour of those who collect and process the data. These information asymmetries could become especially relevant as eye-tracking is expected to become more widespread in user applications, with the seemingly imminent mass adoption of virtual reality systems and the development of devices with the ability to record eye movement outside of a laboratory setting.

The impact of the COVID-19 pandemic on scientific research in the life sciences.

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.

Pharmaceutical net price transparency across european markets: Insights from a multi-agent simulation model.

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.

A machine learning approach to economic complexity based on matrix completion.

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.

The Evolution of Psychological and Behavioral Consequences of Self-Isolation During Lockdown: A Longitudinal Study Across United Kingdom and Italy.

Introduction: Several 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. Aims: This 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. Methods: To 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]. Results: We 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. Conclusion: Recognizing 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.

Emergent treatments for ß-thalassemia and orphan drug legislations.

In many countries, ß-thalassemia (ß-THAL) is not uncommon; however, it qualifies as a rare disease in the US and in European Union (EU), where thalassemia drugs are eligible for Orphan Drug Designation (ODD). In this paper, we evaluate all 28 ODDs for ß-THAL granted since 2001 in the US and the EU: of these, ten have since been discontinued, twelve are pending, and six have become licensed drugs available for clinical use. The prime mover for these advances has been the increasing depth of understanding of the pathophysiology of ß-THAL; at the same time, and even though only one-fifth of ß-THAL ODDs have become licensed drugs, the ODD legislation has clearly contributed substantially to the development of improved treatments for ß-THAL.

[Unmet clinical need and new therapeutic options.] / Unmet clinical need e nuovi farmaci.

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.

The size variance relationship of business firm growth rates.

The relationship between the size and the variance of firm growth rates is known to follow an approximate power-law behavior sigma(S) approximately S(-beta(S)) where S is the firm size and beta(S) approximately 0.2 is an exponent that weakly depends on S. Here, we show how a model of proportional growth, which treats firms as classes composed of various numbers of units of variable size, can explain this size-variance dependence. In general, the model predicts that beta(S) must exhibit a crossover from beta(0) = 0 to beta(infinity) = 1/2. For a realistic set of parameters, beta(S) is approximately constant and can vary from 0.14 to 0.2 depending on the average number of units in the firm. We test the model with a unique industry-specific database in which firm sales are given in terms of the sum of the sales of all their products. We find that the model is consistent with the empirically observed size-variance relationship.

Early warnings of COVID-19 outbreaks across Europe from social media.

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.

COVID-19 vaccination and unemployment risk: lessons from the Italian crisis.

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.

A social network analysis of the organizations focusing on tuberculosis, malaria and pneumonia.

In this paper,we present an original study on the use of social media data to analyze the structure of the global health networks (GHNs) relative to health organizations targeted to malaria, tuberculosis (TBC) and pneumonia as well as twitter popularity, evaluating the performance of their strategies in response to the arising health threats. We use a machine learning ensemble classifier and social network analysis to discover the Twitter users that represent organizations or groups active for each disease. We have found evidence that the GHN of TBC is the more mature, active and global. Meanwhile, the networks of malaria and pneumonia are found to be less connected and lacking global coverage. Our analysis validates the use of social media to analyze GHNs and to propose these networks as an important organizational tool in mobilizing the community versus global sustainable development goals.

Whither geographic proximity? Bypassing local R&D units in foreign university collaboration.

MNCs often engage in international research collaborations with foreign universities through one of their central R&D laboratories (at headquarters or elsewhere) even though they operate a local R&D 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' R&D 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 R&D 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 R&D (au siège ou ailleurs) même si elles exploitent une unité de R&D 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 R&D 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 R&D 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 R&D (na sede ou em outro lugar), embora operem uma unidade local de R&D 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 R&D 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 R&D são importantes orquestradoras da colaboração em pesquisa com universidades ao redor do mundo.

Systemic risk from investment similarities.

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 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 study the diversification, as understood in modern portfolio theory, and the 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 benchmark models of random allocation of investments. This indicates the existence of strong correlations between fund investment strategies. We exploit a deliberately simplified model of shock propagation to identify a systemic risk component stemming from the similarity of portfolios. The network is still partially vulnerable after the crisis because of this effect, despite the increase in the diversification of multi asset portfolios. Diversification and similarity should be taken into account jointly to properly assess systemic risk.