Collective Bargaining under Complete Information

In this paper, we build and structurally estimate a complete information bargaining model of collective negotiation for Spain. For large firms, the assumption of complete information seems a sensible one, and it matches the collective bargaining environment better than the one provided by private information models. The specification of the model with players having different discount factors allows us to measure their relative bargaining power, a recurrent question in the theory of bargaining. We find that both entrepreneurs and workers have high discount factors, and no evidence that entrepreneurs have bigger bargaining power as usually assumed.Delays, sequential bargaining, structural estimation

Multidimensional poverty in the EU: rethinking AROPE through a multi-criteria analysis

At risk of poverty or social exclusion rate (AROPE) constitutes the pivotal indicator of living conditions and poverty in the European Union. Nevertheless, as a multidimensional poverty measure, it has some drawbacks that significantly reduce its utility. In this paper, we propose an alternative multi-criteria approach that provides some innovations for the computation of multidimensional poverty in the European countries. We first propose a normalization formula for each dimension by using a double point of reference. We then put forward alternative aggregation functions that permit diverse degrees of substitutability across dimensions. This new formulation allows us to go beyond focusing merely on the rate of people classified as AROPE, making it possible to evaluate aspects such as the intensity of multidimensional poverty and how changes over time are distributed across population in terms of shared prosperity, as showed in an illustration for the EU28 countries.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Neurophysiology

Contains reports on two research projects.National Institutes of Health (Grant 5 RO1 NB-04985-04)U. S. Air Force (Aerospace Medical Division) under Contract AF33(615)-3885Bell Telephone Laboratories Incorporated (Grant)DSR Project 55-257Bioscience Division of National Aeronautics and Space Administration through Contract NSR 22-009-13

Automatic sensitivity-adjustment for a curvature sensor

There are different techniques to sense the wavefront phase-distortions due to atmospheric turbulence. Curvature sensors are practical in their sensitivity being adjustable to the prevailing atmospheric conditions. Even at the best sites, the turbulence intensity has been found to vary at times over only a few minutes and regularly over longer periods. Two methods to automatically adjust the sensitivity of a curvature sensor are proposed: First, the defocus distance can be adjusted prior to the adaptive-optics (AO) loop through the acquisition of a long exposure image and can then be kept constant. Secondly, the defocus distance can be changed during the AO loop, based on the voltage values sent to the deformable mirror. We demonstrate that the performance increase - assessed in terms of the image Strehl-ratio - can be significant.Comment: Accepted for publication in the adaptive-optics feature of Applied Optic

Dipolar glass polymers containing polarizable groups as dielectric materials for energy storage applications. A minireview

Materials that have high dielectric constants, high energy densities and minimum dielectric losses are highly desirable for use in capacitor devices. In this sense, polymers and polymer blends have several advantages over inorganic and composite materials, such as their flexibilities, high breakdown strengths, and low dielectric losses. Moreover, the dielectric performance of a polymer depends strongly on its electronic, atomic, dipolar, ionic, and interfacial polarizations. For these reasons, chemical modification and the introduction of specific functional groups (e.g., F, CN and R−S(=O)2−R´) would improve the dielectric properties, e.g., by varying the dipolar polarization. These functional groups have been demonstrated to have large dipole moments. In this way, a high orientational polarization in the polymer can be achieved. However, the decrease in the polarization due to dielectric dissipation and the frequency dependency of the polarization are challenging tasks to date. Polymers with high glass transition temperatures (Tg) that contain permanent dipoles can help to reduce dielectric losses due to conduction phenomena related to ionic mechanisms. Additionally, sub-Tg transitions (e.g., γ and β relaxations) attributed to the free rotational motions of the dipolar entities would increase the polarization of the material, resulting in polymers with high dielectric constants and, hopefully, dielectric losses that are as low as possible. Thus, polymer materials with high glass transition temperatures and considerable contributions from the dipolar polarization mechanisms of sub-Tg transitions are known as “dipolar glass polymers”. Considering this, the main aspects of this combined strategy and the future prospects of these types of material were discussed

To aggregate or not to aggregate? Euro area inflation forecasting

In this paper we investigate whether the forecast of the HICP components (indirect approach) improves upon the forecast of overall HICP (direct approach) and whether the aggregation of country forecasts improves upon the forecast of the euro-area as a whole, considering the four largest euro area countries. The direct approach provides clearly better results than the indirect approach for 12 and 18 steps ahead for the overall HICP, while for shorter horizons the results are mixed. For the euro area HICP excluding unprocessed food and energy(HICPX), the indirect forecast outperforms the direct whereas the differences are only marginal for the countries. The aggregation of country forecasts does not seem to improve upon the forecast of the euro area HICP and HICPX. This result has however to be taken with caution as differences appear to be rather small and due to the limited country coverage. JEL Classification: C11, C32, C53, E31, E37Bayesian VARs, Forecasting short-term inflation, HICP sub-components/aggregation, Model Selection