Policies for new path development: the case of Oxfordshire

This chapter reflects on how evolutionary economic geography (EEG) can be extended to incorporate public policy in its explanations of path development. A weakness of EEG is the poor conceptualisation of the role of the state (central, regional, local) in regional path development. It is therefore argued that a multi-scalar perspective of policy is required and that a large set of policies deserve attention. Oxfordshire in the UK is used to explore the link between public policy and path development

Unveiling relationships between crime and property in England and Wales via density scale-adjusted metrics and network tools

Scale-adjusted metrics (SAMs) are a significant achievement of the urban scaling hypothesis. SAMs remove the inherent biases of per capita measures computed in the absence of isometric allometries. However, this approach is limited to urban areas, while a large portion of the world’s population still lives outside cities and rural areas dominate land use worldwide. Here, we extend the concept of SAMs to population density scale-adjusted metrics (DSAMs) to reveal relationships among different types of crime and property metrics. Our approach allows all human environments to be considered, avoids problems in the definition of urban areas, and accounts for the heterogeneity of population distributions within urban regions. By combining DSAMs, cross-correlation, and complex network analysis, we find that crime and property types have intricate and hierarchically organized relationships leading to some striking conclusions. Drugs and burglary had uncorrelated DSAMs and, to the extent property transaction values are indicators of affluence, twelve out of fourteen crime metrics showed no evidence of specifically targeting affluence. Burglary and robbery were the most connected in our network analysis and the modular structures suggest an alternative to "zero-tolerance" policies by unveiling the crime and/or property types most likely to affect each other

Location determinants of green technological entry: evidence from European regions

In this paper, we explore the spatial distribution and the location determinants of new green technology-based firms across European regions. Integrating insights from evolutionary economic geography and the literature on knowledge spillovers, we study the importance of new knowledge creation and the conditioning role played by regional technological relatedness in fostering combinatorial opportunities underlying the process of green technological entry. The analysis is based on a dataset covering over 900 NUTS3 regions for 15 European countries obtained merging economic data from ESPON-Eurostat and patent information from the PATSTAT-CRIOS database for the period 1996–2006. Our results show that the geographical distribution of green technological entry across European regions is not evenly distributed, offering evidence of spatial path dependence. In line with this, we find evidence of a significant role played by the characteristics of the regional innovation system. New green innovators are more likely to develop in regions defined by higher levels of technological activity underlying knowledge spillovers and more dynamism in technological entry. Moreover, our findings point to an inverted-U relationship between regional technological relatedness and green technological entry. Regions whose innovation activity is defined by cognitive proximity to environmental technologies support interactive learning and knowledge spillovers underlying entrepreneurship in this specific area. However, too much relatedness may cause technological lock-ins and reduce the set of combinatorial opportunities

Regional labour market mobility. A network analysis of inter-firm relatedness

Labour market rigidity is known to hamper the proper adjustment of an economy, thus making it less resilient to shocks. This paper investigates the characteristics and resilience of the regional labour flow network in Veneto, a region famous for its industrial districts and the expertise of its workforce. A unique database of inter-firm worker mobility is used and the made-in-Italy relatedness to other industries is quantified. Descriptive results suggest that permanent-contract workers are more mobile within-sector than fixed-term contractors. The latter are more mobile across sectors. A finer disaggregation of the made-in-Italy industries shows that textile, food and woodwork are highly related to leisure-retail, logistics-wholesale and agriculture. These results can orient policy-making in getting faster labour reallocation. Network analysis establishes a number of stylised facts about labour flow networks, in particular, a hierarchical organisation of flows and a preference for workers to move from low-connected to high-connected firms and vice-versa, i.e. disassortativity. Unlike previous research, this paper identifies clusters of a non-spatial nature, that is, based on the intensity of labour flows. Regression analysis shows that labour mobility, both in and out, is beneficial for firms. However, being located inside labour clusters negatively affects firm performance. Interestingly, when these clusters include MNEs, the firm benefits. These results combined suggest that variety of connections prevails over standardisation

Das Gesetz des iterierten Logarithmus fuer kumulative Prozesse

SIGLEDEGerman