Optimal Workfare in Unemployment Insurance

Most workers are only partially insured against unemployment. One reason is that high unemployment compensation creates a free rider problem when monitoring of job search behavior is limited; people who do not seek employment (non-workers) may nevertheless collect unemployment compensation. We show that unproductive workfare for unemployed workers may improve unemployment insurance if workers and non-workers value leisure differently. If they differ only with respect to productivity workfare has to be based on a productivity related task requirement (task workfare); a simple time requirement (time workfare) is not enough. Task workfare is simply a better screening device, also implying that task workfare Pareto dominates time workfare. Finally, we show that the scope for using workfare is larger the smaller are the transfers from workers to non-workers.workfare; unemployment insurance

Training and evaluation of neural networks for multi-variate time series processing

We study training and generalization for multi-variate time series processing. It is suggested to used a quasi maximum likelihood approach rather than the standard sum of squared errors, thus taking dependencies among the errors of the individual time series into account. This may lead to improved generalization performance. Further, we extend the Optimal Brain Damage pruning technique to the multi-variate case. A key ingredient is an algebraic expression for the generalization ability of a multi-variate model. The variability of the suggested techniques are successfully demonstrated in a multi-variate scenario involving the prediction of the cylinder pressure in a marine engine. 1. Introduction Multi-variate nonlinear time series processing models are of interest in many neural network application areas. The application which motivates the present study is the supervision and fault diagnosis of marine engines. Time-series from sensors mounted on the engine are used as inputs to a si..