Hello! I am a PhD student in Public Policy at the University of Tokyo from 2023. My research interests are in empirical industrial organization, applied econometrics and labor economics. I received a BA in Economics from Waseda University and an MA in Economics from the University of Tokyo.
- Contact: utamaru@g.ecc.u-tokyo.ac.jp
Selected Work-in-Progress
Nonparametric Identification of Production Functions without a Markov Assumption
Abstract
This paper proposes a new method for identifying and estimating production functions without the first-order Markov assumption for productivity, which is typically required for identification. By removing this assumption, it becomes possible to identify production functions even when the first-order Markov assumption is not satisfied. After proving the result of nonparametric identification, I compare the performance of our method with an existing method using parametric models through Monte Carlo simulations. While existing methods fail to estimate parameters when the data do not satisfy the first-order Markov assumption, my proposed method can accurately estimate parameters. A Simple Solution to the Identification Problem in Olley-Pakes (1996)
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
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. 