Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Recent work estimating production functions has often used methodologies proposed in two literatures: (1) “proxy variable” estimation techniques (Olley, S. and Pakes, A., 1996, Econometrica, 64, pp. 1263–1295), and (2) “dynamic panel” estimation techniques. I illustrate how timing and information set assumptions are key to both, and how these assumptions can be strengthened (or weakened) almost continuously. I examinehow, in some common production datasets, strengthening or weakening these assumptions affects the precision of estimates—comparing these impacts to those achieved by imposing alternative assumptions sometimes utilized in these literatures. This illustrates efficiency tradeoffs between different possible assumptions, at least in the production function context.