The effect of firm-level contracts on the structure of wages: evidence from matched employer-employee data

Revised: 2006-11

Managing and resisting ‘degeneration’ in employee-owned businesses: a comparative study of two large retailers in Spain and the UK

Employee-owned businesses have recently enjoyed a resurgence of interest as possible ‘alternatives’ to the somewhat tarnished image of conventional investor-owned capitalist firms. Within the context of global economic crisis, such alternatives seem newly attractive. This is somewhat ironic because, for more than a century, academic literature on employee-owned businesses has been dominated by the ‘degeneration thesis’. This suggested that these businesses tend towards failure – they either fail commercially, or they relinquish their democratic characters. Bucking this trend and offering a beacon - especially in the UK - has been the commercially successful, co-owned enterprise of the John Lewis Partnership (JLP) whose virtues have seemingly been rewarded with favourable and sustainable outcomes. This paper makes comparisons between JLP and its Spanish equivalent Eroski – the supermarket group which is part of the Mondragon cooperatives. The contribution of this paper is to examine in a comparative way how the managers in JLP and Eroski have constructed and accomplished their alternative scenarios. Using longitudinal data and detailed interviews with senior managers in both enterprises it explores the ways in which two large, employee-owned, enterprises reconcile apparently conflicting principles and objectives. The paper thus puts some new flesh on the ‘regeneration thesis’.

A Data Dropout Compensation Algorithm Based on the Iterative Learning Control Methodology for Discrete-Time Systems

This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity.