Rentaro Utamaru

I received my Ph.D. in Public Policy from the University of Tokyo in March 2026. I am a JSPS Postdoctoral Research Fellow at Waseda University. My research interests are in empirical industrial organization and applied econometrics, with a focus on identification and estimation of production functions.

Contact: rentaro.utamaru@gmail.com

Working Papers

Nonparametric Identification and Estimation of Production Functions Invariant to Productivity Dynamics

Selected Paper, Rising Stars Session, International Industrial Organization Conference (IIOC), 2026

Abstract

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 Tōhoku earthquake, the standard method overstates the productivity loss by 0.40 percentage points, roughly $3.6 billion (¥400 billion) per year.

Restoring Identification in the Olley-Pakes Estimator: A Timing Assumption Approach

Abstract

The production function estimation method proposed by Olley and Pakes (1996) to correct for selection bias suffers from a identification problem concerning the capital elasticity parameter. This paper proposes a simple solution to this problem. Our approach introduces an unanticipated, idiosyncratic firm-level demand shock into the firm's dynamic decision-making process. Under a key timing assumption, this demand shock serves as a valid exclusion restriction: it affects the selection mechanism but is independent of the firm's productivity. This exogenous variation restores the identification of the capital elasticity parameter. We validate the performance of our proposed estimator through Monte Carlo simulations and an empirical application using Japanese manufacturing data. We demonstrate that while the standard Olley-Pakes estimator is inconsistent and systematically biased, our proposed estimator is consistent and performs well.

From Reduced to Structural: A New Identification Strategy for Production Functions

Best Paper Award, The 20th Applied Econometrics Conference, 2025

Abstract

Standard proxy variable estimators for gross output production functions face a identification problem. These estimators often suffer from weak instrument issues, making it difficult to consistently estimate the elasticity of flexible inputs. This paper introduces a "from reduced to structural" identification strategy. We demonstrate that by first estimating a well-posed reduced-form relationship to recover a proxy for the structural unobservable, the full parameters of the original structural model can then be identified. We apply this framework to the estimation of gross output production functions. Monte Carlo simulations show our estimator is robust and substantially less biased than existing methods. An empirical application using Chilean plant-level data yields more plausible production elasticities and, consequently, more stable estimates of firm-level markups.

Work in Progress

Production Reallocation and Merger Efficiencies in a Multiplant Industry with Satoshi Imahie and Yuta Toyama