Market Power in the Spanish Electricity Auction

Published as an article in: Journal of Regulatory Economics, 2010, vol. 37, issue 1, pages 42-69.

Roaming in the Mobile Internet: when coverage sharing agreements call for regulation

Revised: 2006-06

Union Formation and Bargaining Rules in the Labor Market

This paper analyzes union formation in a model of bargaining between a firm and several unions. We address two questions: first, the optimal configuration of unions (their number and size) and, second, the impact of the bargaining pattern (simultaneous or sequential). For workers, grouping into several unions works as a price discrimination device which, at the same time, decreases their market power. The analysis shows that optimal union configuration depends on the rules that regulate the bargaining process (monopoly union, Nash bargaining or right to manage).

Divergences in the Long Run Trends in the Price of Energy and of Energy Services

32 p.

Optimal Abandonment of Coal-Fired Stations in the EU

36 p.

Ghost seats in the Basque Parliament

In elections voters have generally four options: to abstain, to cast a blank vote, to cast a null vote, or to vote for a candidate or party. This last option is a positive expression of support, while the other three options reflect lack of interest, or dissatisfaction with the parties or the political system. However only votes for parties or candidates are taken into account in the apportionment method. In particular the number of seats allocated to parties remains constant even if the number of non votes (i.e. blank votes, null votes or abstention) is very large. This paper proposes to treat the non votes as a party in the apportionment method and to leave empty the corresponding seats. These empty seats are referred to as "ghost seats". How this would affect the decision-making is quantified in terms of power indices. We apply this proposal to a case study:the regional Parliament of the Basque Autonomous Community (Spain) from 1980 till 2012.

Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.

Proposal and validation of methodologies for the categorisation of continuous variables in the development of prediction models

158 p.