New trajectories of the Hungarian regional development: balanced and rush growth of territorial capital

The basic assumption of the paper is that numerous similarities exist between the patterns of economic growth and territorial capital growth. The rush economic growth and rush growth of territorial capital are compared empirically at Hungarian micro-regional level from 2004 until 2010. After normalizing the dataset, a very novel spatial econometric method is applied, called a penalty for bottleneck. The results show that the constant rush growth of territorial capital is as harmful as economic recession. On the other hand, the decrease of infrastructural and social capital caused the rush growth of territorial capital in this period. Moreover, the key findings of two case studies suggest that the balanced growth of territorial capital will be created by the falling social inequalities and increasing infrastructural capita

Employment mobility in high-technology agglomerations: the cases of Oxfordshire and Cambridgeshire

This paper examines labour market behaviour of the highly skilled in high-tech local economies, taking the UK examples of Oxfordshire and Cambridgeshire as case studies. It reports on data from a survey of members of three scientific institutes to compare rates of employee mobility in the two locations and considers the likely explanations and implications of those patterns

Technological diversification and new innovators in European regions:evidence from patent data

This paper assesses the impact of regional technological diversification on the emergence of new innovators across EU regions. Integrating analyses from regional economics, economic geography and technological change literatures, we explore the role that the regional embeddedness of actors characterised by diverse technological competencies may have in fostering novel and sustained interactions leading to new technological combinations. In particular, we test whether greater technological diversification improve regional ‘combinatorial’ opportunities leading to the emergence of new innovators. The analysis is based on panel data obtained merging regional economic data from Eurostat and patent data from the CRIOS-PATSTAT database over the period 1997–2006, covering 178 regions across 10 EU Countries. Accounting for different measures of economic and innovative activity at the NUTS2 level, our findings suggest that the regional co-location of diverse technological competencies contributes to the entry of new innovators, thereby shaping technological change and industry dynamics. Thus, this paper brings to the fore a better understanding of the relationship between regional diversity and technological change

Development and Validation of a Comprehensive Model to Estimate Early Allograft Failure among Patients Requiring Early Liver Retransplant

Importance: Expansion of donor acceptance criteria for liver transplant increased the risk for early allograft failure (EAF), and although EAF prediction is pivotal to optimize transplant outcomes, there is no consensus on specific EAF indicators or timing to evaluate EAF. Recently, the Liver Graft Assessment Following Transplantation (L-GrAFT) algorithm, based on aspartate transaminase, bilirubin, platelet, and international normalized ratio kinetics, was developed from a single-center database gathered from 2002 to 2015. Objective: To develop and validate a simplified comprehensive model estimating at day 10 after liver transplant the EAF risk at day 90 (the Early Allograft Failure Simplified Estimation [EASE] score) and, secondarily, to identify early those patients with unsustainable EAF risk who are suitable for retransplant. Design, Setting, and Participants: This multicenter cohort study was designed to develop a score capturing a continuum from normal graft function to nonfunction after transplant. Both parenchymal and vascular factors, which provide an indication to list for retransplant, were included among the EAF determinants. The L-GrAFT kinetic approach was adopted and modified with fewer data entries and novel variables. The population included 1609 patients in Italy for the derivation set and 538 patients in the UK for the validation set; all were patients who underwent transplant in 2016 and 2017. Main Outcomes and Measures: Early allograft failure was defined as graft failure (codified by retransplant or death) for any reason within 90 days after transplant. Results: At day 90 after transplant, the incidence of EAF was 110 of 1609 patients (6.8%) in the derivation set and 41 of 538 patients (7.6%) in the external validation set. Median (interquartile range) ages were 57 (51-62) years in the derivation data set and 56 (49-62) years in the validation data set. The EASE score was developed through 17 entries derived from 8 variables, including the Model for End-stage Liver Disease score, blood transfusion, early thrombosis of hepatic vessels, and kinetic parameters of transaminases, platelet count, and bilirubin. Donor parameters (age, donation after cardiac death, and machine perfusion) were not associated with EAF risk. Results were adjusted for transplant center volume. In receiver operating characteristic curve analyses, the EASE score outperformed L-GrAFT, Model for Early Allograft Function, Early Allograft Dysfunction, Eurotransplant Donor Risk Index, donor age × Model for End-stage Liver Disease, and Donor Risk Index scores, estimating day 90 EAF in 87% (95% CI, 83%-91%) of cases in both the derivation data set and the internal validation data set. Patients could be stratified in 5 classes, with those in the highest class exhibiting unsustainable EAF risk. Conclusions and Relevance: This study found that the developed EASE score reliably estimated EAF risk. Knowledge of contributing factors may help clinicians to mitigate risk factors and guide them through the challenging clinical decision to allocate patients to early liver retransplant. The EASE score may be used in translational research across transplant centers

Linking learning with governance in networks and clusters: key issues for analysis and policy

In this paper we analyse the relationship between governance and learning in clusters and networks. In particular, we see these two key elements as interdependent, suggesting that, under particular circumstances, such interdependence may drive clusters and networks towards a dynamic development trajectory. A pure ‘governance perspective’ makes the development of any locality dependent on the system of powers which exists within the locality or across the global value chain. In parallel, a pure ‘competence-based approach’ focuses mainly on the capabilities of actors to learn and undertake activities. In contrast, we open the prospects for an interdependent relation that may change the actual competences of actors as well as the governance settings and dynamics in networks and clusters. When supported by public policies, the learning process may have the potential to modify the governance environment. Simultaneously, the learning process is intrinsically influenced by economic power, which may seriously affect the development prospects of clusters and networks. This is why an intertwined consideration of both aspects is necessary to promote specific approaches to learning and to design appropriate policies. In this paper we offer two preliminary case studies to clarify some of these dynamics: the first taken from the computers cluster in Costa Rica and the second from an Italian bio-pharmaceutical firm and its production network. The first case study refers to the software cluster that was created from scratch in Costa Rica thanks to an enlightened government policy in coordination with new local enterprises and an important foreign direct investor; while the second reflects on the ability of an individual company to create a network of relationships with large transnational companies in order to acquire new competences without falling into a subordinate position with respect to its larger partners