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In the recent past, annual CO 2 emissions at the international level were examined from various perspectives, motivated by rising concerns about pollution and climate change. Nevertheless, to the best of the authors' knowledge, the problem of dealing with the potential inaccuracy/missingness of such data at the country and economic sector levels has been overlooked. Thereby, in this article we apply a supervised machine learning technique called Matrix Completion (MC) to predict, for each country in the available database, annual CO 2 emissions data at the sector level, based on past data related to all the sectors, and more recent data related to a subset of sectors. The core idea of MC consists in the formulation of a suitable optimization problem, namely the minimization of a proper trade-off between the approximation error over a set of observed elements of a matrix (training set) and a proxy of the rank of the reconstructed matrix, e.g., its nuclear norm. In the article, we apply MC to the imputation of (artificially) missing elements of country-specific matrices whose elements come from annual CO 2 emission levels related to different sectors, after proper pre-processing at the sector level. Results highlight typically a better performance of the combination of MC with suitably-constructed baseline estimates with respect to the baselines alone. Potential applications of our analysis arise in the prediction of currently missing elements of matrices of annual CO 2 emission levels and in the construction of counterfactuals, useful to estimate the effects of policy changes able to influence the annual CO 2 emission levels of specific sectors in selected countries. [ABSTRACT FROM AUTHOR]

This paper sheds new light on the dynamic effects of inter-firm network agreements on firm performance and investigates whether the specific combination of partner profiles triggers heterogeneous causal effects. Using a staggered difference-in-differences design, we find that participation in network agreements has a persistent impact on firms' revenues, value added, and EBITDA that is amplified at least through the third year of collaboration. Our results show that micro firms benefit more from collaboration in network agreements, especially when they enter into relationships with larger partners. In addition, companies benefit more from network ties when most of their partners belong to the same travel-to-work area. Plain English Summary: Participating in a network agreement is a valuable solution for improving long-term revenues and operating margins. Our analysis shows that network agreements introduced into the Italian legal system in 2009 yield increasing benefits that become larger in the third year of cooperation and stabilize thereafter. Moreover, the interaction of partners' characteristics affects the magnitude of the effects. Micro firms benefit more from formal networks, especially when they collaborate with larger partners. Moreover, network agreements are highly beneficial when most network members operate in the same area. The Italian policy on network agreements has proven to be a "best practice" under the Small Business Act. However, since it takes some time for the results of network agreements to reach their peak, institutional communication should be improved to raise the right expectations about their potential. From a management perspective, partner selection is a critical step, as a good match of partner characteristics can increase the value creation potential of formal networks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the increasing pervasiveness of Information and Communication Technology (ICT) in the fabric of economic activities, the corporate digital divide has become a crucial issue for the assessment of Information Technology (IT) competencies and the digital gap between firms and territories. With little granular data available to measure the phenomenon, most studies have used survey data. To address this empirical gap, we scanned the homepages of 182,705 Italian companies and extracted ten characteristics related to their digital footprint to develop a new index for the corporate digital assessment. Our results show a significant digital divide between Italian companies according to size, sector and geographical location, opening new perspectives for monitoring and data-driven analysis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This document provides concise summaries of several research studies related to medical imaging techniques and their applications in diagnosing and treating various diseases. The studies cover a wide range of topics, including the use of AI algorithms in breast cancer detection, the effectiveness of diffusion tensor imaging in diagnosing brain tumors, and the use of PET imaging in detecting neurodegenerative diseases. The studies offer valuable insights into the potential benefits and limitations of these imaging techniques, but further research and validation are needed to confirm these findings. [Extracted from the article]

Multi-regional input–output (I/O) matrices provide the networks of within- and cross-country economic relations. In the context of I/O analysis, the methodology adopted by national statistical offices in data collection raises the issue of obtaining reliable data in a timely fashion and it makes the reconstruction of (parts of) the I/O matrices of particular interest. In this work, we propose a method combining hierarchical clustering and matrix completion with a LASSO-like nuclear norm penalty, to predict missing entries of a partially unknown I/O matrix. Through analyses based on both real-world and synthetic I/O matrices, we study the effectiveness of the proposed method to predict missing values from both previous years data and current data related to countries similar to the one for which current data are obscured. To show the usefulness of our method, an application based on World Input–Output Database (WIOD) tables—which are an example of industry-by-industry I/O tables—is provided. Strong similarities in structure between WIOD and other I/O tables are also found, which make the proposed approach easily generalizable to them. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Focuses on how technological and relational regimes have shaped the growth of the network of research and development (R & D) collaborative agreements in pharmaceuticals in the 1990s. Review of related literature on industry networks; Analysis of the nature of technological regimes and the growth of an extensive system of innovative labor in the industry; Topological properties of the R & D networks and the processes underlying its growth.

The dynamics of created advantages i , London, Pinter Publishers, 1992 L'accumulazione delle capacita' tecnologiche nell'industria italiana (19869-84) (with F. Malerba), in I "Innovazione tecnologica e servizi alle imprese", i edited by C. Filippini, F. Angeli, Milano, 1992 La competitivita' tecnologica internazionale dell'industria italiana negli anni ottanta (with F. Malerba e G. Antonel), in Ministero del Commercio Estero e Istituto Nazionale per il Commercio estero, I Conferenza nazionale sul commercio estero. Essays in honour of R. Cyert i , Edward Elgar London 2001 Modelli History friendly della evoluzione delle industrie: il caso dell'industria dei computer (with F. Malerba), I Economia e Politica Industriale, i 2001 Product Diversification in a "History-Friendly Model of the Evolution of the Computer Industry (with Franco Malerba, Richard Nelson, and Sidney Winter), in E. Larsen and A. Lomi (eds.), Due to this revolution, an industry dominated by a stable core of large firms moved to a market structure in which a division of scientific, innovative and production labour has taken place between large incumbent firms and innovative start-ups. On the contrary, in industries in which innovation is relevant, exit takes place both among infant as well as mature firms and does not necessarily involve only the smaller firms. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we build a cumulative innovation model to understand the role of both success and failure in the learning dynamics that characterize pharmaceutical R&D. We test the prediction of our model by means of a unique dataset that combines patent information with R&D projects, thus distinguishing patents related to successfully marketed products from those covering candidate drugs that failed in clinical trials. Results confirm model predictions showing that patents associated with successfully completed projects receive more citations than those associated with failed projects. However, we also show that failed projects can be in turn cited more often than patents lacking clinical or preclinical information. We further explore the 'black box' of innovation, providing evidence that both successes and failures contribute to R&D investment decisions and knowledge dynamics in science-driven sectors. [ABSTRACT FROM AUTHOR]

Chorus is a small, operationally independent clinical development organization within Eli Lilly and Company that specializes in drug development from candidate selection to clinical proof of concept. The mission of Chorus is to achieve proof of concept rapidly and at a low cost while positioning successful projects for 'pharma-quality' late-stage development. Chorus uses a small internal staff of experienced drug developers and a network of external vendors to design and implement chemistry, manufacturing and control processes, preclinical toxicology and biology, and Phase I/II clinical trials. In the decade since it was established, Chorus has demonstrated substantial productivity improvements in both time and cost compared to traditional pharmaceutical research and development. Here, we describe its development philosophy, organizational structure, operational model and results to date. [ABSTRACT FROM AUTHOR]

This paper uses network analysis to study how employment intermediaries have influenced inter-firm worker mobility in a region of Italy, in response to a 1997 reform that introduced temporary employment agencies. Worker reallocations from a matched employer-employee dataset are mapped onto a directed graph where the vertices are firms and the links denote transfers of workers between firms. Temporary employment agencies significantly improve network integration and practicability, while rapidly increasing the control over mobility channels. The trade off inherent in intermediation activity is captured and discussed. The potential of network analysis as a tool for monitoring regional labor markets is highlighted. [ABSTRACT FROM AUTHOR]

Experience and socialization are key factors in customer commitment and defection decisions. To study the effect of experience and social relationships on customer retention, we analyze a reality-mined co-presence network of health club members over a period of 4 years. Since central customers in the network have more social ties they will lose if they defect, we use centrality as a proxy for customer relationship switching costs. We find that long-standing customers do have a lower chance of renewing their contracts. However, in line with theoretical predictions (Burnham et al., Journal of the Academy of Marketing Science 31(2):109-126, ), the consumer's centrality in the network (reflecting a social cost of defection) reduces customer churn rate. This study's results indicate that the inclusion of social effects increases the predictive power of the customer churn model (Nitzan and Libai, Journal of Marketing 75(6):24-38, ), thus contributing to our understanding of the role social networks play in customer decisions. [ABSTRACT FROM AUTHOR]

Considerable efforts have been made in recent years to produce detailed topologies of the Internet, but so far these data have been overlooked by economists. In this paper, we suggest that such information could be used to characterize both the size of the digital economy and outsourcing at country level. We analyse the topological structure of the network of trade in digital services (trade in bits) and compare it with the more traditional flow of manufactured goods across countries. To perform meaningful comparisons across networks with different characteristics, we define a stochastic benchmark for the number of connections among each country-pair, based on hypergeometric distribution. Original data are filtered so that we only focus on the strongest, i.e. statistically significant, links. We find that trade in bits displays a sparser and less hierarchical network structure, which is more similar to trade in high-skill manufactured goods than total trade. Moreover, distance plays a more prominent role in shaping the network of international trade in physical goods than trade in digital services. [ABSTRACT FROM AUTHOR]

During the last 30 years, health care expenditure (HCE) has been growing much more rapidly than GDP in OECD countries. In this paper, we review the determinants of HCE dynamics in Europe, taking into account the role of income, aging population, technological progress, female labor participation and public budgetary variables. We show that HCE is a multifaceted phenomenon where demographic, social, economic, technological and institutional factors all play an important role. The comparison of total, public and private HCE reveals an imbalance of European welfare toward the care of the elderly. European Governments should increasingly rely on pluralistic systems to balance sustainability and access and equilibrate the distribution of resources across the functions of the public welfare system. [ABSTRACT FROM AUTHOR]

We develop a model of scientific creativity and test it in the field of rare diseases. Our model is based on the results of an in-depth case study of the Rett Syndrome. Archival analysis, bibliometric techniques and expert surveys are combined with network analysis to identify the most creative scientists. First, we compare alternative measures of generative and combinatorial creativity. Then, we generalize our results in a stochastic model of socio-semantic network evolution. The model predictions are tested with an extended set of rare diseases. We find that new scientific collaborations among experts in a field enhance combinatorial creativity. Instead, high entry rates of novices are negatively related to generative creativity. By expanding the set of useful concepts, creative scientists gain in centrality. At the same time, by increasing their centrality in the scientific community, scientists can replicate and generalize their results, thus contributing to a scientific paradigm. [ABSTRACT FROM AUTHOR]

Advances in the understanding of the molecular basis of diseases have expanded the number of plausible therapeutic targets for the development of innovative agents in recent decades. However, although investment in pharmaceutical research and development (R&D) has increased substantially in this time, the lack of a corresponding increase in the output in terms of new drugs being approved indicates that therapeutic innovation has become more challenging. Here, using a large database that contains information on R&D projects for more than 28,000 compounds investigated since 1990, we examine the decline of R&D productivity in pharmaceuticals in the past two decades and its determinants. We show that this decline is associated with an increasing concentration of R&D investments in areas in which the risk of failure is high, which correspond to unmet therapeutic needs and unexploited biological mechanisms. We also investigate the potential variations in productivity with regard to the regional location of companies and find that although companies based in the United States and Europe differ in the composition of their R&D portfolios, there is no evidence of any productivity gap. [ABSTRACT FROM AUTHOR]

This paper investigates patterns of industrial dynamics and competition in the pharmaceutical industry, with particular reference to the consequences of patent expiry in different countries. We focus on the competition at the level of single chemical entities, distinguishing between original brands and generic products. Quarterly data, spanning from July 1987 to December 1998, on sales of pharmaceutical products in four countries (USA, UK, Germany, and France) constitute the basis of our analysis. All the products containing major molecules whose patent expiration date lies between 1986 and 1996 are included in our sample. We show how diffusion of generics is linked to the characteristics of the market and investigate how price dynamics of original products are affected by generic competition. Our empirical investigation shows that the dynamics of drug prices and the competition by generic drugs vary significantly across countries. This heterogeneity notwithstanding, a clear distinction seems to emerge. On the one hand, systems that rely on market-based competition in pharmaceuticals promote a clear distinction between firms that act as innovators and firms that act as imitators after patent expiry. Here, original products enjoy premium prices and exclusivity profits under patent protection, and face fierce price competition after patent expiry. On the other hand, in systems that rely on administered prices, penetration by generic drugs tends to be rather limited. Its descriptive and preliminary nature notwithstanding, our analysis seems to have relevant implications at different levels of generality, especially for Europe. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses various reports within the issue including "Science Models as Value-Added Services for Scholarly Information Systems," by Peter Mutschke and colleagues, " Tailor Based Allocations for Multiple Authorship: A Fractional gh-Index," by Serge Galam, and "Community Structure and Patterns of Scientific Collaboration in Business and Management," by Timothy S. Evans and colleagues.