Public expenditure and deficit in Spain (1958-2014)

La formulación de dos ecuaciones diferentes confirma la influencia del déficit en el crecimiento del gasto público en el período 1958-2014. El trabajo aporta dos novedades al análisis de los determinantes del gasto público en España: por una parte, se extiende considerablemente el período objeto de estudio; por otra, se usa el análisis de raíces unitarias y cointegración con puntos de ruptura, que no ha sido utilizado anteriormente en este caso.El propósito de este trabajo es analizar un modelo reducido de determinantes del crecimiento del gasto público desde el lado de la demanda. El modelo está basado en la hipótesis de Buchanan y Wagner pero se han añadido diversas variables que se consideran determinantes de demanda del gasto público. The objective of this study is to analyze a reduced model of the determinants of public spending growth from a demand side perspective. The model is based on the Buchanan and Wagner hypothesis but incorporates several other variables considered as determinants of public spending growth as well. The formulation of two different equations confirmed the influence of deficit on public spending growth during the period 1958-2014. This work provides two contributions to the analysis of public spending determinants in Spain. Firstly, the study period is considerably longer than that of others, and, secondly, unit root and cointegration analysis are used with breakpoints, which, to our knowledge, have not been previously utilized.O propósito deste trabalho é analisar um modelo reduzido de determinantes do crescimento do gasto público desde o lado da demanda. O modelo está baseado na hipótese Buchanan e Wagner mas se há adicionado diversas variáveis que se consideram determinantes de demanda do gasto público. A formulação de duas equações diferentes confirma a influência do déficit no crescimento do gasto público no período 1958-2014. O trabalho aporta duas novidades à análise dos determinantes do gasto público na Espanha. Por uma parte se estende consideravelmente o período objeto de estudo. Por outra se usa a análise de raízes unitárias e co-integração com pontos de ruptura que não há sido utilizado anteriormente neste caso

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Diversidad etnolingüística y diferencias de renta entre países. Canales de transmisión

En este trabajo analizamos los vínculos directos e indirectos entre la diversidad etnolingüística y las diferencias de renta entre países. Se demuestra que existe una relación negativa y estadísticamente significativa entre ambas variables si se consideran de forma aislada. Sin embargo, encontramos que la mayor parte de este vínculo negativo se explica a través de los efectos que la diversidad etnolingüística tiene sobre otras variables que a su vez se relacionan con la renta (pobreza, corrupción y fertilidad). Estudiamos estos mecanismos de transmisión indirecta de los efectos y llegamos a la conclusión que la fertilidad es el mecanismo de transmisión más relevante del modelo.In this paper we analyze the direct and indirect links between ethnolinguistic diversity and income differences between countries. It is shown that there is a negative and statistically significant relationship between both variables if they are considered in isolation way. However, we find that most of this negative link is explained by the effects that ethnolinguistic diversity has on other variables at the same time are related to income (poverty, corruption and fertility). We study these mechanisms of indirect transmission of the effects and reach the conclusion that fertility is the most relevant transmission mechanism of the model.Universidad de Sevilla. Máster Universitario en Consultoría Económica y Análisis Aplicad

A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks

Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many environmental and social applications. The increase in availability of RS data has led to the development of new techniques for digital pattern classification. Very recently, deep learning (DL) models have emerged as a powerful solution to approach many machine learning (ML) problems. In particular, convolutional neural networks (CNNs) are currently the state of the art for many image classification tasks. While there exist several promising proposals on the application of CNNs to LULC classification, the validation framework proposed for the comparison of different methods could be improved with the use of a standard validation procedure for ML based on cross-validation and its subsequent statistical analysis. In this paper, we propose a general CNN, with a fixed architecture and parametrization, to achieve high accuracy on LULC classification over RS data from different sources such as radar and hyperspectral. We also present a methodology to perform a rigorous experimental comparison between our proposed DL method and other ML algorithms such as support vector machines, random forests, and k-nearest-neighbors. The analysis carried out demonstrates that the CNN outperforms the rest of techniques, achieving a high level of performance for all the datasets studied, regardless of their different characteristics.Ministerio de Economía y Competitividad TIN2014-55894-C2-1-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-