标题： 一种用于多维生产率的生产函数结构识别的系统方法 显示英文标题

标题： A System Approach to Structural Identification of Production Functions with Multi-Dimensional Productivity

摘要： 有越来越多的实证证据表明，企业异质性在技术上是非中性的。 本文扩展了Gandhi等人(2020)的代理变量框架，用于结构性识别生产函数，在潜在企业生产率是多维的情况下，包括要素中性和（偏向）要素增强成分。 与替代方法不同，我们的模型可以在较弱的数据要求下进行识别，特别是不需要依赖通常不可用的投入价格的横截面变化来进行工具变量处理。 当市场完全竞争时，我们通过利用静态最优条件中包含的信息实现点识别，有效地采用方程组方法。 我们还展示了当企业具有市场力量时，如何在传统的代理变量框架中部分识别非中性生产技术。 显示英文摘要

摘要： There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This paper extends Gandhi et al.'s (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, our model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, we achieve point identification by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. We also show how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.