Key Sectors,Industrial Clustering and Multivariate Outliers

In this paper a reflection is made on the problems that can arise in key sector analysis and industrial clustering, due to the usual presence of outliers when using multidimensional data related to the sectors in an input–output table. Multidimensional outliers are considered as being not only linked to the low number of clusters usually observed in this kind of study, but probably causing invalid results in most of the works involving multivariate statistical techniques, such as cluster and factor analysis. Actually, by comparing the key sectors of the Spanish economy obtained in Díaz et al. (2006) Díaz, B., Moniche, L. and Morillas, A. 2006. A fuzzy clustering approach to the key sectors of the Spanish economy. Economic Systems Research, 18: 299–318. [Taylor & Francis Online] [Google Scholar] to the ones we get taking into account the problem the outliers pose, one can realize they greatly distort the results. On the other hand, it is shown that identification of outliers can be considered as a good and new procedure to help select the most important sectors in an economy.     

An empirical approach to the organisational determinants of spin-off creation in European universities

Given the importance in recent years of the phenomenon of academic entrepreneurship in European universities, knowledge about determinants and performance of this technology transfer mechanism may have important managerial and policy implications. Applying the resource-based view at universities, we have tested the influence of five bundles of resources and capabilities on spin-off activity rates as well as on the performance of new spin-offs in a sample of seven European universities. These five bundles are: technology transfer policies and strategies; human capital; stock of technology; resources and capabilities of technology transfer offices; and support measures for academic entrepreneurship. Our results show that excellence of human capital and the presence of university-based financial support measures are strongly associated with both spin-off activity and performance. Another interesting finding is related to the availability of university-based non-economic support measures (training, counselling, etc.), which have no influence on spin-off activity but are significantly associated with performance of new spin-offs. We also discuss some interesting relationships between explanatory variables.     

Credit derivatives, capital requirements and opaque OTC markets

In this paper we study the optimal design of credit derivative contracts when banks have private information about their ability in the loan market and are subject to capital requirements. First, we prove that when banks are subject to a maximum loss capital requirement the optimal signaling contract is a binary credit default basket. Second, we show that if credit derivative markets are opaque then banks cannot commit to terminal-date risk exposure, and therefore the optimal signaling contract is more costly. The above results allow us to discuss the potential implications of different capital adequacy rules for the credit derivative markets.     

Barriers to women entrepreneurship. Different methods,different results?

Building on research by Akehurst et al. (Serv Ind J 32:2489–2505, 2012), this study analysed internal and external factors in women entrepreneurship and linked these factors to the barriers that women face when starting businesses. To do so, two contrasting statistical techniques were used: PLS and QCA. After analysing results from each of these techniques, we observed that family duties and difficulties in obtaining financing (both internal and external) were the main factors related to barriers faced by women entrepreneurs.     

How Investment Opportunities Affect Optimal Capital Structure

This article addresses the question of how competition for investments among companies in a certain industry affects their capital structure. The authors develop a new modelling framework that simulates financial variables of a set of firms in a given sector, and uses the framework to analyze how such firms compete for new investments. The leverage of companies affects their flexibility to react to and take advantage of investment opportunities, and the authors show how such flexibility can be optimized to maximize the firm’s growth. As an illustration, they apply the model to a set of European airlines and global pharmaceutical companies. The novelty introduced by this paper is the explicit modelling of the interaction between several companies. The literature on optimal capital structure focuses on individual companies optimizing their capital structure in a world in which the actions of their competitors are exogenous. The authors’ results show how to incorporate the competitive position of the firm as well as the availability of investment opportunities into the capital structure decison.     

Data-mining,GIS and multicriteria analysis in a comprehensive method for bicycle network planning and design

In many Brazilian cities, the most common procedure for planning cycling networks is using aggregated population data in census tracts, which may not take into account the true origin and destination of trips. It may also not identify potential users of a particular mode of transport. This is particularly important considering that implementing cycling infrastructures should be based on the assumption that they are able to meet the users' needs. Therefore, the aim of this study is to develop and adopt an objective method to design and compare cycling networks based on data-mining of disaggregated origin-destination data, GIS resources, and multicriteria analysis techniques. The method follows three steps: 1) identifying potential users based on real user profiles, 2) designing proposed cycling networks and 3) a comparison between the networks proposed in this study and those developed by the municipality selected as a case study, considering real and potential users, as well as cost and benefit criteria. As a positive outcome, using disaggregated data allows for a reasonable estimate of the number of people served by the networks, a detailed analysis of their proximity to the infrastructure, as well as identifying potential users. Comparing cycling networks considering cost and benefit criteria shows that the chosen criteria were effective. It was also determined that the cycling network of the studied city poorly serves bicycle transport users, if compared to the proposed networks. These findings indicate that appropriate methods for planning cycling networks are still needed.     

Extending multi-tenant architectures: a database model for a multi-target support in SaaS applications

Multi-tenant architectures (MTAs) are considered a cornerstone in the success of Software as a Service as a new application distribution formula. Multi-tenancy allows multiple customers (i.e. tenants) to be consolidated into the same operational system. This way, tenants run and share the same application instance as well as costs, which are significantly reduced. Functional needs vary from one tenant to another; either companies from different sectors run different types of applications or, although deploying the same functionality, they do differ in the extent of their complexity. In any case, MTA leaves one major concern regarding the companies’ data, their privacy and security, which requires special attention to the data layer. In this article, we propose an extended data model that enhances traditional MTAs in respect of this concern. This extension – called multi-target – allows MT applications to host, manage and serve multiple functionalities within the same multi-tenant (MT) environment. The practical deployment of this approach will allow SaaS vendors to target multiple markets or address different levels of functional complexity and yet commercialise just one single MT application. The applicability of the approach is demonstrated via a case study of a real multi-tenancy multi-target (MT²) implementation, called Globalgest.     

Gender segregation and earnings differences in the Spanish labour market

This article analyses the effects of different types of gender segregation on the gender wage differential for the Spanish labour market. Matched employer–employee data from a sample of 226,535 workers are used. These workers are employed in 61 occupations within 26,492 establishments in 51 different industries. Workers belonging to the same industry, establishment or job share common factors which cannot be observed and these factors affect wages. If these unobservable variables are correlated with the explanatory variables, their estimated effects will be biased. For this reason, we estimate the effects of each type of gender segregation on the wage gap using a robust specification to these possible correlations. We obtain that industrial segregation by gender explains a lower part of the wage gap between men and women than previous researches found using standard regressions, while the contributions of establishment segregation and occupational segregation within each establishment are greater.     

The impact of multiple structural changes on mortality predictions

Most mortality models proposed in recent literature rely on the standard ARIMA framework (in particular: a random walk with drift) to project mortality rates. As a result the projections are highly sensitive to the calibration period. We therefore analyse the impact of allowing for multiple structural changes on a large collection of mortality models. We find that this may lead to more robust projections for the period effect but that there is only a limited effect on the ranking of the models based on backtesting criteria, since there is often not yet sufficient statistical evidence for structural changes. However, there are cases for which we do find improvements in estimates and we therefore conclude that one should not exclude on beforehand that structural changes may have occurred.