Much of the recent empirical work estimating production functions has used methodologies proposed in two distinct lines of literature: 1) the literature started by Olley and Pakes (1996) using "proxy variable" techniques, and 2) what is commonly referred to as the "dynamic panel" literature. We illustrate how timing and firm information set assumptions are key to both methodologies, and how these assumptions can be strengthened or weakened almost continously, a point that seems underappreciated in the literature. We also discuss other assumptions that have been utilized in these literatures to increase the efficiency (or precision) of estimates. Empirically, we then examine how, in a number of plant level production datasets, strengthening or weakening the timing/information set assumptions affects the precision of estimates. We compare these impacts on precision to those achieved by imposing other potential assumptions. We hope this gives researchers a better idea of the efficiency tradeoffs between different possible assumptions, at least in the production function context.