arXiv 6 Apr 2026 · Econometrics
arXiv:2604.04458 · PDF · DOI · OpenAlex · Extracted main text
Production function estimates underpin the measurement of firm-level markups, allocative efficiency, and the productivity effects of policy interventions. Since Olley and Pakes (1996), every major proxy variable estimator has identified the production function through a first-order Markov assumption on unobserved productivity; I show that misspecification of this assumption generates persistent upward bias in the materials elasticity that propagates into overestimated markups and inflated treatment effects. I replace the Markov restriction with conditional independence across three intermediate input demands, a static condition grounded in input market segmentation, and establish nonparametric identification from a single cross-section. I develop a GMM estimator and establish consistency and asymptotic normality. Monte Carlo simulations confirm that the proposed estimator is unbiased across Markov and non-Markov environments, while the standard estimator exhibits persistent bias of up to 63 percent of the true materials elasticity. In 502 Japanese manufacturing industries, the proposed method yields systematically lower markups than the standard method across the entire distribution (median 0.93 vs. 1.03), reducing the share of industries with markups above unity from 54 to 37 percent. In a difference-in-differences analysis of the 2011 Tohoku earthquake, the standard method overstates the productivity loss by 0.40 percentage points, roughly $3.6 billion (400 billion yen) per year.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Olley, G. Steven and Pakes, Ariel (1996) The Dynamics of Productivity in the Telecommunications Equipment Industry | 1.000 | 10 | 5 | 100% |
| 2 | Gandhi, Amit and Navarro, Salvador and Rivers, David A (2020) On the Identification of Gross Output Production Functions | 1.000 | 7 | 3 | 100% |
| 3 | Chen, Zhiyuan and Liao, Moyu and Schurter, Karl (2024) Identifying Treatment Effects on Productivity: Theory with An Application to Production Digitalization | 0.956 | 8 | 5 | 88% |
| 4 | Ackerberg, Daniel A. and Caves, Kevin and Frazer, Garth (2015) Identification Properties of Recent Production Function Estimators | 0.935 | 11 | 6 | 82% |
| 5 | Hu, Yingyao and Schennach, Susanne M (2008) Instrumental Variable Treatment of Nonclassical Measurement Error Models | 0.874 | 6 | 2 | 100% |
| 6 | De Loecker, Jan and Warzynski, Frederic (2012) Markups and Firm-Level Export Status | 0.843 | 3 | 3 | 100% |
| 7 | De Loecker, Jan and Eeckhout, Jan and Unger, Gabriel (2020) The Rise of Market Power and the Macroeconomic Implications | 0.843 | 3 | 3 | 100% |
| 8 | Hu, Yingyao and Huang, Guofang and Sasaki, Yuya (2020) Estimating production functions with robustness against errors in the proxy variables | 0.737 | 3 | 2 | 100% |
| 9 | Imbens, Guido W. and Newey, Whitney K (2009) Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity | 0.644 | 4 | 1 | 100% |
| 10 | Matzkin, Rosa L (2003) Nonparametric Estimation of Nonadditive Random Functions | 0.644 | 4 | 1 | 100% |
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