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  5. Frontiers in Production Function Estimation
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Frontiers in Production Function Estimation

Paper Session

Sunday, Jan. 4, 2026 10:15 AM - 12:15 PM (EST)

Philadelphia Convention Center, 203-A
Hosted By: American Economic Association
  • Chair: Ulrich Doraszelski, University of Pennsylvania

Leveraging Subjective Expectations for Production Functions

Agnes Norris Keiller
,
London School of Economics
Áureo de Paula
,
University College London
John Van Reenen
,
London School of Economics

Abstract

Standard methods for estimating production functions in the Olley and Pakes (1996) tradition require assumptions on input choices. We introduce a new method that exploits (increasingly available) data on a firm’s expectations of its future output and inputs that allows us to obtain consistent production function parameter estimates while relaxing these input demand assumptions. In contrast to dynamic panel methods, our proposed estimator can be implemented on very short panels (including a single cross-section), and Monte Carlo simulations show it outperforms alternative estimators when firms’ material input choices are subject to optimization error. Implementing a range of production function estimators on UK data, we find our proposed estimator yields results that are either similar to or more credible than commonly used alternatives. These differences are larger in industries where material inputs appear harder to optimize. We show that TFP implied by our proposed estimator is more strongly associated with future jobs growth than existing methods, suggesting that failing to adequately account for input endogeneity may underestimate the degree of dynamic reallocation in the economy.

Production Functions under Imperfect Competition: A Quality Equivalent Cournot Model

Dan Ackerberg
,
University of Texas at Austin
Jan De Loecker
,
KU Leuven

Abstract

The presence of imperfect competition introduces distinct challenges when identifying, and estimating, production functions. We start by highlighting that some existing approaches to production function estimation cannot completely abstract away from the presence of imperfect competition in the product market. We then extend these existing approaches to accommodate some additional oligopoly models commonly used in empirical work by using a sufficient statistic approach, and show that the presence of such strategic interactions has important benefits in that they introduce additional exogenous variation that can help identify production functions. We study how to optimally leverage this exogenous variation, both with and without direct data on a firm’s competitors, and use Monte-Carlo experiments to 1) verify that the existence of strategic interactions can identify production functions that would not otherwise be identified, and 2) assess the extent to which what applied researchers observe about competition affects the precision of estimates based on this variation.

A Generalized Control Function Approach to Production Function Estimation

Ulrich Doraszelski
,
University of Pennsylvania
Lixiong Li
,
Johns Hopkins University

Abstract

We revisit the proxy variable approach to production function estimation. We show that the invertibility assumption at its heart is testable. We characterize what goes wrong if invertibility fails and what can still be done. We show that rethinking how the estimation procedure is implemented either eliminates or mitigates the bias that arises if invertibility fails. Furthermore, we show how a modification of the procedure ensures Neyman orthogonality and enhances efficiency and robustness.
JEL Classifications
  • D2 - Production and Organizations