Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We study Ackerberg, Caves, and Frazer's (Econometrica, 2015, 83, 2411–2451; hereafter ACF) production function estimation method using Monte Carlo simulations. First, we replicate their results by following their procedure to confirm the existence of a spurious minimum in the estimation, as noted by ACF. In the population, or when sample sizes are sufficiently large, this “global” identification problem may not be a concern because the spurious minimum occurs only at extreme values of capital and labor coefficients. However, in finite samples, their estimator can produce estimates that may not be clearly distinguishable from the spurious ones. In our second experiment, we modify the ACF procedure and show that robust estimates can be obtained using additional lagged instruments or sequential search. We also provide some arguments for why such modifications help in the ACF setting.