Does Immigration Raise Natives’ Income? National and Regional Evidence from Spain

How immigration affects the labor market of the host country is a topic of major concern for many immigrant-receiving nations. Spain is no exception following the rapid increase in immigrant flows experienced over the past decade. We assess the impact of immigration on Spanish natives’ income by estimating the net immigration surplus accruing at the national level and at high immigrant-receiving regions while taking into account the imperfect substitutability of immigrant and native labor. Specifically, using information on the occupational densities of immigrants and natives of different skill levels, we develop a mapping of immigrant-to-native self-reported skills that reveals the combination of natives across skills that would be equivalent to an immigrant of a given self-reported skill level, which we use to account for any differences between immigrant self-reported skill levels and their effective skills according to the Spanish labor market. We find that the immigrant surplus amounts to 0.04 percent of GDP at the national level and it is even higher for some of the main immigrant-receiving regions, such as Cataluña, Valencia, Madrid, and Murcia.

Regional Entrepreneurial Scorecard

[ES] Este trabajo analiza la relación entre el desarrollo regional y la creación de empresas desde una perspectiva micro del enfoque institucional, a partir de los stakeholders más relevantes que intervienen en el proceso. La contribución de los emprendedores al crecimiento económico regional viene siendo objeto de especial atención por los poderes públicos, para lo que se necesita un sistema de referencias que permita evaluar la adecuación de los programas públicos de fomento de la actividad emprendedora.

A High Degree of LINE-1 Hypomethylation Is a Unique Feature of Early-Onset Colorectal Cancer

12 p.

Regional IAM: analysis of risk-adjusted costs and benefits of climate policies

23 p.

Evolución del mercado de trabajo regional en tiempos de crisis: Madrid

Análisis de la situación laboral de la comunidad de Madrid, realizando una descripción de la evolución del panorama laboral en dicha región y comparando ésta con otros ámbitos geográficos, así como con otros contextos económicos. Utilizando datos de la EPA. Idioma: Español

Landscape Genetics for the Empirical Assessment of Resistance Surfaces: The European Pine Marten (Martes martes) as a Target-Species of a Regional Ecological Network

Coherent ecological networks (EN) composed of core areas linked by ecological corridors are being developed worldwide with the goal of promoting landscape connectivity and biodiversity conservation. However, empirical assessment of the performance of EN designs is critical to evaluate the utility of these networks to mitigate effects of habitat loss and fragmentation. Landscape genetics provides a particularly valuable framework to address the question of functional connectivity by providing a direct means to investigate the effects of landscape structure on gene flow. The goals of this study are (1) to evaluate the landscape features that drive gene flow of an EN target species (European pine marten), and (2) evaluate the optimality of a regional EN design in providing connectivity for this species within the Basque Country (North Spain). Using partial Mantel tests in a reciprocal causal modeling framework we competed 59 alternative models, including isolation by distance and the regional EN. Our analysis indicated that the regional EN was among the most supported resistance models for the pine marten, but was not the best supported model. Gene flow of pine marten in northern Spain is facilitated by natural vegetation, and is resisted by anthropogenic landcover types and roads. Our results suggest that the regional EN design being implemented in the Basque Country will effectively facilitate gene flow of forest dwelling species at regional scale.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.