Laura Magazzini

Within the European Union (EU), pricing and reimbursement decisions for new medicines are the responsibility of individual Member States. The presence of many regulated prices for medicines within the EU single market drives strategic decisions by payers and manufacturers, resulting, among other things, in launch delays and unequal patient access across European countries. To increase equity in patient access, we propose strengthening collaboration on medicines procurement among Member States beyond emergency situations. Joint initiatives undertaken by groups of countries have demonstrated their potential to deliver benefits, and the need for common procurement initiatives has become apparent during the coronavirus disease 2019 (COVID-19) pandemic. We present our proposal within the context of the current European regulatory framework and the recently proposed "Critical Medicines Act", emphasising the key legislative provisions that can be invoked to support a common procurement initiative. We also provide relevant, actionable options for designing such initiatives. Strong political commitment and agreement on equity and solidarity principles would be needed for the successful implementation of the proposal.

Firms are more likely to adopt open-sourcing strategies when they operate in environments dense with other innovative firms, allowing reciprocal learning and knowledge exchange.Building on the literature linking sectoral openness to productivity growth, we examine whether firms’ propensity to publish scientific work amplifies the productivity returns to R&D investment.We find that adopting an open-sourcing strategy is positively associated with labour productivity among R&D-intensive firms, controlling for unobserved heterogeneity at the firm and sector level.This result is consistent with theories emphasizing both the direct effects of R&D on innovation output and its indirect effects through knowledge diffusion and absorptive capacity. Methodologically, the paper extends Hausman–Taylor-type approaches for the identification of time-invariant variables in a fixed effect framework. Monte Carlo simulations show that this approach reliably identifies the impact of these regressors on productivity.

We propose a new framework for testing the “mean stationarity” assumption in dynamic panel data models, required for the consistency of the system GMM estimator. In our set up the assumption is obtained as a parametric restriction in an extended set of moment conditions, allowing the use of a LM test to check its validity. Our framework provides a ranking in terms of power of the analyzed test statistics, in which our approach exhibits better power than the difference-in-Sargan/Hansen test that compares system GMM and difference GMM, that is, on its turn, more powerful than the Sargan/Hansen test based on the system GMM moment conditions.