Reference Pricing as a Deterrent to Entry: Evidence from the European Pharmaceutical Market

External reference pricing (ERP), the practice of benchmarking domestic drug prices to foreign prices, generates an incentive for firms to withhold products from low-income countries. Using a novel moment inequality approach, we estimate a structural model to measure how ERP policies affect access to innovative drugs across Europe. We find that ERP increases entry delays in eight low-income European countries by up to one year per drug. The European Union could remove these delays without replacing ERP by compensating firms through lump-sum transfers at the cost of around €18 million per drug

Predicting economic resilience of territories in Italy during the COVID-19 first lockdown

This paper aims to predict the economic resilience to crises of territories based on local pre-existing socioeco-nomic characteristics. Specifically, we consider the case of Italian municipalities during the first wave of the COVID-19 pandemic, leveraging a large-scale dataset of cardholders performing transactions in Point-of-Sales. Based on a set of machine learning classifiers, we show that network-based measures and variables related to the social, economic, demographic and environmental dimensions are relevant predictors of the economic resilience of Italian municipalities to the crisis. In particular, we find accurate classification performance both in balanced and un-balanced scenarios, as well as in the case we restrict the analysis to specific geographical areas. Our analysis predicts that territories with larger income per capita, soil consumption, concentration of real estate activities and commuting network centrality in terms of closeness and Pagerank constitute the set of most affected areas, experiencing the strongest reduction of economic activities during the COVID-19 pandemic. Overall, we provide an application of an early-warning system able to provide timely evidence to policymakers about the detrimental effects generated by natural disasters and severe crisis episodes, thus contributing to optimize public decision support systems

A Generalized Preferential Attachment Model for Business Firms Growth Rates: II. Mathematical Treatment

We present a preferential attachment growth model to obtain the distribution $P(K)$ of number of units $K$ in the classes which may represent business firms or other socio-economic entities. We found that $P(K)$ is described in its central part by a power law with an exponent $\phi=2+b/(1-b)$ which depends on the probability of entry of new classes, $b$. In a particular problem of city population this distribution is equivalent to the well known Zipf law. In the absence of the new classes entry, the distribution $P(K)$ is exponential. Using analytical form of $P(K)$ and assuming proportional growth for units, we derive $P(g)$, the distribution of business firm growth rates. The model predicts that $P(g)$ has a Laplacian cusp in the central part and asymptotic power-law tails with an exponent $\zeta=3$. We test the analytical expressions derived using heuristic arguments by simulations. The model might also explain the size-variance relationship of the firm growth rates.Comment: 19 pages 6 figures Applications of Physics in Financial Analysis, APFA

Persistence and Uncertainty in the Academic Career

Understanding how institutional changes within academia may affect the overall potential of science requires a better quantitative representation of how careers evolve over time. Since knowledge spillovers, cumulative advantage, competition, and collaboration are distinctive features of the academic profession, both the employment relationship and the procedures for assigning recognition and allocating funding should be designed to account for these factors. We study the annual production n_{i}(t) of a given scientist i by analyzing longitudinal career data for 200 leading scientists and 100 assistant professors from the physics community. We compare our results with 21,156 sports careers. Our empirical analysis of individual productivity dynamics shows that (i) there are increasing returns for the top individuals within the competitive cohort, and that (ii) the distribution of production growth is a leptokurtic "tent-shaped" distribution that is remarkably symmetric. Our methodology is general, and we speculate that similar features appear in other disciplines where academic publication is essential and collaboration is a key feature. We introduce a model of proportional growth which reproduces these two observations, and additionally accounts for the significantly right-skewed distributions of career longevity and achievement in science. Using this theoretical model, we show that short-term contracts can amplify the effects of competition and uncertainty making careers more vulnerable to early termination, not necessarily due to lack of individual talent and persistence, but because of random negative production shocks. We show that fluctuations in scientific production are quantitatively related to a scientist's collaboration radius and team efficiency.Comment: 29 pages total: 8 main manuscript + 4 figs, 21 SI text + fig

The endless frontier? The recent increase of R&D productivity in pharmaceuticals

Background: Studies on the early 2000s documented increasing attrition rates and duration of clinical trials, leading to a representation of a "productivity crisis" in pharmaceutical research and development (R&D). In this paper, we produce a new set of analyses for the last decade and report a recent increase of R&D productivity within the industry. Methods: We use an extensive data set on the development history of more than 50,000 projects between 1990 and 2017, which we integrate with data on sales, patents, and anagraphical information on each institution involved. We devise an indicator to quantify the novelty of each project, based on its set of mechanisms of action. Results: First, we investigate how R&D projects are allocated across therapeutic areas and find a polarization towards high uncertainty/high potential reward indications, with a strong focus on oncology. Second, we find that attrition rates have been decreasing at all stages of clinical research in recent years. In parallel, for each phase, we observe a significant reduction of time required to identify projects to be discontinued. Moreover, our analysis shows that more recent successful R&D projects are increasingly based on novel mechanisms of action and target novel indications, which are characterized by relatively small patient populations. Third, we find that the number of R&D projects on advanced therapies is also growing. Finally, we investigate the relative contribution to productivity variations of different types of institutions along the drug development process, with a specific focus on the distinction between the roles of Originators and Developers of R&D projects. We document that in the last decade Originator-Developer collaborations in which biotech companies act as Developers have been growing in importance. Moreover, we show that biotechnology companies have reached levels of productivity in project development that are equivalent to those of large pharmaceutical companies. Conclusions: Our study reports on the state of R&D productivity in the bio-pharmaceutical industry, finding several signals of an improving performance, with R&D projects becoming more targeted and novel in terms of indications and mechanisms of action

Evolution of controllability in interbank networks

The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected "hub" institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies