The Productivity Paradox and the New Economy: The Spanish Case

This paper studies the impact of the information and communication technologies (ICT) on economic growth in Spain using a dynamic general equilibrium approach. Contrary to previous works, we use a production function with six different capital inputs, three of them corresponding to ICT assets. Calibration of the model suggests that the contribution of ICT to Spanish productivity growth is very relevant, whereas the contribution of non-ICT capital has been even negative. Additionally, over the sample period 1995-2002, we find a negative TFP and productivity growth. These results together aim at the hypothesis that the Spanish economy could be placed within the productivity paradox.New economy, information and communication technologies, technological change, productivity paradox.

ICT-specific technological change and productivity growth in the US 1980-2004

This paper studies the impact of the information and communication technologies (ICT) on U.S. economic growth using a dynamic general equilibrium approach. We use a production function with six different capital inputs, three of them corresponding to ICT assets and other three to non-ICT assets. We find that the technological change embedded in hardware equipment is the main leading non-neutral force of the U.S. productivity growth and accounts for about one quarter of it during the period 1980-2004. As a whole, ICT-specific technological change accounts for about 35% of total labor productivity growth.New economy, information and communication technologies, specific-technological change, neutral-technological change.

El texto de "Politics and the English language" y el contexto de "Newspeak". A propósito de una crítica de las ideas lingüísticas de George Orwell

Este estudio explora las recomendaciones estilísticas hechas por George Orwell en su obra famosa "Politics and the English Language" y los principios que regulan la lengua artificial creada por los "etimólogos" de "Nineteen Eighty-Four". Se centra en las diferencias radicales entre las funciones y los contextos, que aparecen, respectivamente, en cada obra. El examen cuidadoso de estas diferencias arroja luz sobre la naturaleza exacta de la relación entre los preceptos lingüísticos de Orwell y los principios de la "Newspeak", invalidando así los supuestos críticos que consideran a este último como una sátira de las recomendaciones de "Politics and the English Language".This study explores the stylistic recommendations made by George Orwell in his famous essay “Politics and the English Language” and the principles governing the artificial language created by the “etymologists” of “Nineteen Eighty-Four”, focussing on the radical differences between their respective functions and the contexts in which each appears. The careful examination of these differences sheds light on the exact nature of the connection between Orwell's linguistic precepts and the principles of “Newspeak”, thus invalidating those critical assumptions which regard the latter as a satire on the recommendations of “Politics and the English Language”.notPeerReviewe

Automatic learning framework for pharmaceutical record matching

Pharmaceutical manufacturers need to analyse a vast number of products in their daily activities. Many times, the same product can be registered several times by different systems using different attributes, and these companies require accurate and quality information regarding their products since these products are drugs. The central hypothesis of this research work is that machine learning can be applied to this domain to efficiently merge different data sources and match the records related to the same product. No human is able to do this in a reasonable way because the number of records to be matched is extremely high. This article presents a framework for pharmaceutical record matching based on machine learning techniques in a big data environment. The proposed framework aims to explode the well-known rules for the matching of records from different databases for training machine learning models. Then the trained models are evaluated by predicting matches with records that do not follow these known rules. Finally, the production environment is simulated by generating a huge amount of combinations of records and predicting the matches. The obtained results show that, despite the good results obtained with the training datasets, in the production environment, the average accuracy of the best model is around 85%. That shows that matches which do not follow the known rules can be predicted and, considering that there is not a human way to process this amount of data, the results are promising.This work was supported by the Research Program of the Ministry of Economy and competitiveness, Government of Spain, through the DeepEMR Project, under Grant TIN2017-87548-C2-1-