This paper presents a performance analysis of market-based batch schedulers for clusters of workstations. In contrast to previous work, we use user-centric performance metrics as the basis for system evaluation. Each user is modeled as having a utility function for each job which measures value delivered to the user as function of execution time. Summing over all utility functions in the workload, we use aggregate utility as a measure of overall value delivered to users. With aggregate utility as the performance metric, simulations are used to quantify the performance of both market-based and traditional batch scheduling algorithms under a variety of synthetic work-loads. Results show that an auction-based batch scheduling algorithm improves performance by a factor of up to 2-5x for sequential workloads and up to 14x for highly parallel workloads compared to traditional scheduling algorithms.