Identifying the Elasticity of Substitution with Biased Technical Change
通过蒙特卡洛分析，挑战了资本劳动替代弹性与技术偏向无法同时识别的传统观点，发现联合建模生产函数和一阶条件优于单方程方法，尤其结合归一化时效果更佳。
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.