The Significance of Identifying Industrial clusters The Case of Scotland

Industrial clustering policy is now an integral part of economic development planning in most advanced economies. However, there have been concerns in some quarters over the ability of an industrial cluster-based development strategy to deliver its promised economic benefits and this has been increasingly been blamed on the failure by governments to identify industrial clusters. In a study published in 2001, the DTI identified clusters across the UK based on the comparative scale and significance of industrial sectors. The study identified thirteen industrial clusters in Scotland. However the clusters identified are not a homogeneous set and they seem to vary in terms of their geographic concentration within Scotland. This paper examines the spatial distribution of industries within Scotland, thereby identifying more localised clusters. The study follows as closely as possible the DTI methodology which was used to identify such concentrations of economic activity with particular attention directed towards the thirteen clusters identified by the DTI. The paper concludes with some remarks of the general problem of identifying the existence of industrial clusters.

Where is the economics in spatial econometrics?

Spatial econometrics has been criticized by some economists because some model specifications have been driven by data-analytic considerations rather than having a firm foundation in economic theory. In particular this applies to the so-called W matrix, which is integral to the structure of endogenous and exogenous spatial lags, and to spatial error processes, and which are almost the sine qua non of spatial econometrics. Moreover it has been suggested that the significance of a spatially lagged dependent variable involving W may be misleading, since it may be simply picking up the effects of omitted spatially dependent variables, incorrectly suggesting the existence of a spillover mechanism. In this paper we review the theoretical and empirical rationale for network dependence and spatial externalities as embodied in spatially lagged variables, arguing that failing to acknowledge their presence at least leads to biased inference, can be a cause of inconsistent estimation, and leads to an incorrect understanding of true causal processes.

Regional productivity variation and the impact of public capital stock: an analysis with spatial interaction, with reference to Spain

In this paper we examine whether variations in the level of public capital across Spain‟s Provinces affected productivity levels over the period 1996-2005. The analysis is motivated by contemporary urban economics theory, involving a production function for the competitive sector of the economy („industry‟) which includes the level of composite services derived from „service‟ firms under monopolistic competition. The outcome is potentially increasing returns to scale resulting from pecuniary externalities deriving from internal increasing returns in the monopolistic competition sector. We extend the production function by also making (log) labour efficiency a function of (log) total public capital stock and (log) human capital stock, leading to a simple and empirically tractable reduced form linking productivity level to density of employment, human capital and public capital stock. The model is further extended to include technological externalities or spillovers across provinces. Using panel data methodology, we find significant elasticities for total capital stock and for human capital stock, and a significant impact for employment density. The finding that the effect of public capital is significantly different from zero, indicating that it has a direct effect even after controlling for employment density, is contrary to some of the earlier research findings which leave the question of the impact of public capital unresolved.

Multilevel Modelling with Spatial Effects

In multilevel modelling, interest in modeling the nested structure of hierarchical data has been accompanied by increasing attention to different forms of spatial interactions across different levels of the hierarchy. Neglecting such interactions is likely to create problems of inference, which typically assumes independence. In this paper we review approaches to multilevel modelling with spatial effects, and attempt to connect the two literatures, discussing the advantages and limitations of various approaches.

Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales.

Estimation of the Spatial Weights Matrix under Structural Constraints

While estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds de nition of spatial weights in most studies with cross-section dependence. We show that, in the spatial error model the spatial weights matrix is only partially identi ed, and is fully identifi ed under the structural constraint of symmetry. For the spatial error model, we propose a new methodology for estimation of spatial weights under the assumption of symmetric spatial weights, with extensions to other important spatial models. The methodology is applied to regional housing markets in the UK, providing an estimated spatial weights matrix that generates several new hypotheses about the economic and socio-cultural drivers of spatial di¤usion in housing demand.

Subcellular investigation of photosynthesis-driven carbon assimilation in the symbiotic reef coral Pocillopora damicornis.

Reef-building corals form essential, mutualistic endosymbiotic associations with photosynthetic Symbiodinium dinoflagellates, providing their animal host partner with photosynthetically derived nutrients that allow the coral to thrive in oligotrophic waters. However, little is known about the dynamics of these nutritional interactions at the (sub)cellular level. Here, we visualize with submicrometer spatial resolution the carbon and nitrogen fluxes in the intact coral-dinoflagellate association from the reef coral Pocillopora damicornis by combining nanoscale secondary ion mass spectrometry (NanoSIMS) and transmission electron microscopy with pulse-chase isotopic labeling using [(13)C]bicarbonate and [(15)N]nitrate. This allows us to observe that (i) through light-driven photosynthesis, dinoflagellates rapidly assimilate inorganic bicarbonate and nitrate, temporarily storing carbon within lipid droplets and starch granules for remobilization in nighttime, along with carbon and nitrogen incorporation into other subcellular compartments for dinoflagellate growth and maintenance, (ii) carbon-containing photosynthates are translocated to all four coral tissue layers, where they accumulate after only 15 min in coral lipid droplets from the oral gastroderm and within 6 h in glycogen granules from the oral epiderm, and (iii) the translocation of nitrogen-containing photosynthates is delayed by 3 h. IMPORTANCE: Our results provide detailed in situ subcellular visualization of the fate of photosynthesis-derived carbon and nitrogen in the coral-dinoflagellate endosymbiosis. We directly demonstrate that lipid droplets and glycogen granules in the coral tissue are sinks for translocated carbon photosynthates by dinoflagellates and confirm their key role in the trophic interactions within the coral-dinoflagellate association.

Fusion of Multi-Atlas Segmentations with Spatial Distribution Modeling

In recent years, multi-atlas fusion methods have gainedsignificant attention in medical image segmentation. Inthis paper, we propose a general Markov Random Field(MRF) based framework that can perform edge-preservingsmoothing of the labels at the time of fusing the labelsitself. More specifically, we formulate the label fusionproblem with MRF-based neighborhood priors, as an energyminimization problem containing a unary data term and apairwise smoothness term. We present how the existingfusion methods like majority voting, global weightedvoting and local weighted voting methods can be reframedto profit from the proposed framework, for generatingmore accurate segmentations as well as more contiguoussegmentations by getting rid of holes and islands. Theproposed framework is evaluated for segmenting lymphnodes in 3D head and neck CT images. A comparison ofvarious fusion algorithms is also presented.

A bayesian spatial individual effects probit model of the 2010 UK general election

The Conservative Party emerged from the 2010 United Kingdom General Election as the largest single party, but their support was not geographically uniform. In this paper, we estimate a hierarchical Bayesian spatial probit model that tests for the presence of regional voting effects. This model allows for the estimation of individual region-specic effects on the probability of Conservative Party success, incorporating information on the spatial relationships between the regions of the mainland United Kingdom. After controlling for a range of important covariates, we find that these spatial relationships are significant and that our individual region-specic effects estimates provide additional evidence of North-South variations in Conservative Party support.

Competitive ability not kinship affects growth of Arabidopsis thaliana accessions.

In many organisms, individuals behave more altruistically towards relatives than towards unrelated individuals. Here, we conducted a study to determine if the performance of Arabidopsis thaliana is influenced by whether individuals are in competition with kin or non-kin. We selected seven pairs of genetically distinct accessions that originated from local populations throughout Europe. We measured the biomass of one focal plant surrounded by six kin or non-kin neighbours in in vitro growth experiments and counted the number of siliques produced per pot by one focal plant surrounded by four kin or non-kin neighbours. The biomass and number of siliques of a focal plant were not affected by the relatedness of the neighbour. Depending on the accession, a plant performed better or worse in a pure stand than when surrounded by non-kin plants. In addition, whole-genome microarray analyses revealed that there were no genes differentially expressed between kin and non-kin conditions. In conclusion, our study does not provide any evidence for a differential response to kin vs non-kin in A. thaliana. Rather, the outcome of the interaction between kin and non-kin seems to depend on the strength of the competitive abilities of the accessions.

Personal Indebtedness, Spatial Effects and Crime

There is a long and detailed history of attempts to understand what causes crime. One of the most prominent strands of this literature has sought to better understand the relationship between economic conditions and crime. Following Becker (1968), the economic argument is that in an attempt to maintain consumption in the face of unemployment, people may resort to sources of illicit income. In a similar manner, we might expect ex–ante, that increases in the level of personal indebtedness would be likely to provide similar incentives to engage in criminality. In this paper we seek to understand the spatial pattern of property and theft crimes using a range of socioeconomic variables, including data on the level of personal indebtedness.

Personal Indebtedness, Spatial Effects and Crime

There is a long and detailed history of attempts to understand what causes crime. One of the most prominent strands of this literature has sought to better understand the relationship between economic conditions and crime. Following Becker (1968), the economic argument is that in an attempt to maintain consumption in the face of unemployment, people may resort to sources of illicit income. In a similar manner, we might expect ex–ante, that increases in the level of personal indebtedness would be likely to provide similar incentives to engage in criminality. In this paper we seek to understand the spatial pattern of property and theft crimes using a range of socioeconomic variables, including data on the level of personal indebtedness.

Estudi de les alteracions en la capacitat neuroprotectora dels astròcits reactius en l'envelliment

En el periodo 2005-2008 hemos publicado tres artículos sobre las alteraciones de los astrocitos reactivos en el cerebro durante el envejecimiento. En el primer estudio, evaluamos la capacidad neuroprotectora de los astrocitos en un modelo experimental in vitro de envejecimiento. Los cambios en el estrés oxidativo, la captación del glutamato y la expresión proteica fueron evaluados en los astrocitos corticales de rata cultivados durante 10 y 90 días in vitro (DIV). Los astrocitos envejecidos tenían una capacidad reducida de mantener la supervivencia neuronal. Estos resultados indican que los astrocitos pueden perder parcialmente su capacidad neuroprotectora durante el envejecimiento. En el segundo estudio el factor neurotrófico derivado de la línea glial (GDNF) fue probado para observar sus efectos neurotróficos contra la atrofia neuronal que causa déficits cognitivos en la vejez. Las ratas envejecidas Fisher 344 con deficiencias en el laberinto de Morris recibieron inyecciones intrahippocampales de un vector lentiviral que codifica GDNF humano en los astrocitos o del mismo vector que codifica la proteína fluorescente verde humana como control. El GDNF secretado por los astrocitos mejoró la función de la neurona como se muestra por aumentos locales en la síntesis de los neurotransmisores acetilcolina, dopamina y serotonina. El aprendizaje espacial y la prueba de memoria demostraron un aumento significativo en las capacidades cognitivas debido a la exposición de GDNF, mientras que las ratas control mantuvieron sus resultados al nivel del azar. Estos resultados confirman el amplio espectro de la acción neurotrófica del GDNF y abre nuevas posibilidades de terapia génica para reducir la neurodegeneración asociada al envejecimiento. En el último estudio, examinamos cambios en la fosforilación de tau, el estrés oxidativo y la captación de glutamato en los cultivos primarios de astrocitos corticales de ratones neonatos de senescencia acelerada (SAMP8) y ratones resistentes a la senescencia (SAMR1). Nuestros resultados indican que las alteraciones en cultivos del astrocitos de los ratones SAMP8 son similares a las detectadas en cerebros enteros de los ratones SAMP8 de 1-5 meses de edad. Por otra parte, nuestros resultados sugieren que esta preparación in vitro es adecuada para estudiar en este modelo murino el envejecimiento temprano y sus procesos moleculares y celulares.

How private is private information? The ability to spot deception in an economic game

We provide experimental evidence on the ability to detect deceit in a buyer-seller game with asymmetric information. Sellers have private information about the buyer's valuation of a good and sometimes have incentives to mislead buyers. We examine if buyers can spot deception in face-to-face encounters. We vary (1) whether or not the buyer can interrogate the seller, and (2) the contextual richness of the situation. We find that the buyers' prediction accuracy is above chance levels, and that interrogation and contextual richness are important factors determining the accuracy. These results show that there are circumstances in which part of the information asymmetry is eliminated by people's ability to spot deception.

Spatial Takeoff in the First Industrial Revolution

Using the framework of Desmet and Rossi-Hansberg (forthcoming), we present a model of spatial takeoff that is calibrated using spatially-disaggregated occupational data for England in c.1710. The model predicts changes in the spatial distribution of agricultural and manufacturing employment which match data for c.1817 and 1861. The model also matches a number of aggregate changes that characterise the first industrial revolution. Using counterfactual geographical distributions, we show that the initial concentration of productivity can matter for whether and when an industrial takeoff occurs. Subsidies to innovation in either sector can bring forward the date of takeoff while subsidies to the use of land by manufacturing firms can significantly delay a takeoff because it decreases spatial concentration of activity.