I am a Ph.D. candidate in Public Policy at the University of Tokyo. Starting April 2026, I will join Waseda University as a JSPS Postdoctoral Research Fellow. 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
Education
- Ph.D. in Public Policy, The University of Tokyo, 2026 (expected)
- M.A. in Economics, The University of Tokyo, 2023
- B.A. in Economics, Waseda University, 2021
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
We establish the nonparametric identification of both the production function and the distribution of unobserved productivity without imposing dynamic restrictions on the productivity process. Unlike standard proxy variable estimators that rely on a first-order Markov assumption, our strategy exploits the static covariance structure of three flexible inputs. By treating input demands as proxies subject to optimization errors, we map the production system to a measurement error model with auxiliary variables. This framework identifies the model primitives using only the joint distribution of inputs within a single period. We propose a Generalized Method of Moments estimator and verify its consistency through Monte Carlo simulations, particularly in non-stationary environments where conventional dynamic methods exhibit bias. An empirical application to Japanese manufacturing firms yields elasticity estimates that differ from standard benchmarks, providing evidence of time-varying production technologies and alternative implications for allocative efficiency.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