Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
The capital-labor substitution elasticity and technical biases in production are critical parameters. The received wisdom claims their joint identification is infeasible. We challenge that interpretation. Putting the new approach of "normalized" production functions at the heart of a Monte Carlo analysis we identify the conditions under which identification is feasible and robust. The key result is that jointly modeling the production function and first-order conditions is superior to single-equation approaches especially when merged with "normalization." Our results will have fundamental implications for production-function estimation under non-neutral technical change, for understanding the empirical relevance of normalization and variability underlying past empirical studies. (JEL E22, O33, O41)