This study explores the effects of aggregating variables in large linear programs. The author defines a reasonable criterion for the resulting loss in accuracy, and derive bounds on this quantity. A posteriori bounds may be calculated after solving the aggregated problem, and a priori bounds before. It is also shown that standard iterative methods can be used to improve the accuracy of a given aggregated problem. A numerical example illustrates the results.