On the Relationship between R&D and Productivity: a Treatment Effect Analysis

This study uses firm level data from two detailed surveys of Italian manufacturing firms to study the relationship between R&D expenditures and productivity growth. The analysis considers the different contributions of various forms of R&D (product, process, internal, external in collaboration with universities, research centres and other firms) to Total Factor Productivity (TFP). Thus, this paper answers the call for more research on the links between a firm's external R&D and its productivity. In the cross-section econometric analysis, we estimate a Treatment Effects model based on the assumption that the decision to carry out R&D is endogenous. We found evidence supporting such a methodological approach. The main results reveal a positive and statistically significant relationship between the detailed measures of R&D and TFP. It is noteworthy that among external R&D investments, only expenditures for projects run in collaboration with other firms turn out to be highly significant, while cooperation in R&D with universities does not seem to lead to productivity enhancements. Because of the public good nature of research, firms may resort to do R&D within laboratories run by universities only when the outcome of the research does not have important strategic consequences.

Assessing the Returns to Collaborative Research: Firm-Level Evidence from Italy

We use firm-level data from Italian manufacturing firms to assess the relationship between various types of R&D and total factor productivity growth, including collaborative research with other firms and universities. A novel twist to our empirical analysis is that we estimate a treatment effects model, which enables us to treat the decision to conduct R&D as endogenous. We find strong evidence of positive returns to collaborative research with companies, while collaborative research with universities does not appear to enhance productivity. This result implies that firms may conduct R&D with universities when appropriability conditions are weak and the outcomes of such research projects do not yield direct strategic benefits.

The port attractiveness index: application on African ports

The overall operational reputation of a port is based on objective factors, including infrastructure endowments and efficiency in the logistics chain as well as on perceived subjective factors such as reliability, and level of corruption. In this work we analyze the concept of port attractiveness, starting with the hypothesis that subjective port determinants (i.e., user perception) and objective/endogenous and exogenous factors can be quantified together. We thus determine the Port Attractiveness Index and test it using 41 container ports of 23 African countries for the period 2006-2010. We apply a bottom-up approach to investigate the structural relationships among the three sets of determinants (endogenous, exogenous and subjective) that impact on port attractiveness. Our methodological approach employs structural equation modeling. Results indicate that subjective factors are indeed influential variables for port attractiveness. Moreover, when examining port attractiveness and investment strategies, we demonstrate that in many cases in African ports governments should implement soft infrastructure as a first step rather than investing in hard infrastructures

Implementing the “Best Template Searching” tool into Adenosiland platform

Background: Adenosine receptors (ARs) belong to the G protein-coupled receptors (GCPRs) family. The recent release of X-ray structures of the human A2A AR (h A2A AR ) in complex with agonists and antagonists has increased the application of structure-based drug design approaches to this class of receptors. Among them, homology modeling represents the method of choice to gather structural information on the other receptor subtypes, namely A1, A2B, and A3 ARs. With the aim of helping users in the selection of either a template to build its own models or ARs homology models publicly available on our platform, we implemented our web-resource dedicated to ARs, Adenosiland, with the “Best Template Searching” facility. This tool is freely accessible at the following web address: http://mms.dsfarm.unipd.it/Adenosiland/ligand.php. Findings: The template suggestions and homology models provided by the “Best Template Searching” tool are guided by the similarity of a query structure (putative or known ARs ligand) with all ligands co-crystallized with hA2A AR subtype. The tool computes several similarity indexes and sort the outcoming results according to the index selected by the user. Conclusions: We have implemented our web-resource dedicated to ARs Adenosiland with the “Best Template Searching” facility, a tool to guide template and models selection for hARs modelling. The underlying idea of our new facility, that is the selection of a template (or models built upon a template) whose co-crystallized ligand shares the highest similarity with the query structure, can be easily extended to other GPCRs

An Interdependent Multi-Layer Model: Resilience of International Networks

Trade flows are characterised by interdependent economic networks such as the global supply chain, international bilateral agreements, trans-national credit, and foreign direct investments, as well as non-economic components (i.e. infrastructures, cultural ties and spatial barriers). We construct an Interdependent Multi-layer Model (IMM), which is rooted in the theoretical concept of spatial interaction, in order to identify the links within these networks and trace their impacts on trade flows. In our aim to investigate horizontal and vertical interdependency among networks we calibrate the interaction model (IMM) for a set of 40 countries, and thereafter examine the influence of shocks such as economic downturns upon the interdependent networks, which in our model are represented as economic, socio-cultural and physical layers. Most importantly, the model allows us to understand the propagation of cascading effects (both positive and negative) at national and global scales

Macromolecular-scale resolution in biological fluorescence microscopy

We demonstrate far-field fluorescence microscopy with a focal-plane resolution of 15–20 nm in biological samples. The 10- to 12-fold multilateral increase in resolution below the diffraction barrier has been enabled by the elimination of molecular triplet state excitation as a major source of photobleaching of a number of dyes in stimulated emission depletion microscopy. Allowing for relaxation of the triplet state between subsequent excitation–depletion cycles yields an up to 30-fold increase in total fluorescence signal as compared with reported stimulated emission depletion illumination schemes. Moreover, it enables the reduction of the effective focal spot area by up to ≈140-fold below that given by diffraction. Triplet-state relaxation can be realized either by reducing the repetition rate of pulsed lasers or by increasing the scanning speed such that the build-up of the triplet state is effectively prevented. This resolution in immunofluorescence imaging is evidenced by revealing nanoscale protein patterns on endosomes, the punctuated structures of intermediate filaments in neurons, and nuclear protein speckles in mammalian cells with conventional optics. The reported performance of diffraction-unlimited fluorescence microscopy opens up a pathway for addressing fundamental problems in the life sciences