“Privatization and Productivity in China.” Accepted at RAND Journal of Economics
Joint work with Yuyu Chen, Mitsuru Igami and Mo Xiao. Current version, 2021, April 8
We study how changes in ownership affect the productivity of firms. Privatization of state-owned enterprises (SOEs) was a major economic reform during China's rapid growth, but its true impact remains controversial. Although private firms seem more productive than SOEs, the government selectively privatized (or liquidated) non-performing SOEs, which complicates the measurement of productivity. We address this selection problem by incorporating endogenous ownership change into a nonparametric estimation method and exploiting a lag structure in data. Results suggest privatization conferred both short-run and long-run productivity gains. The private-SOE productivity gap is larger among older firms and in less economically liberal regions.
Current version, 2021, June 2
(Formerly circulated as "Identification and Inference of Post-Treatment Subgroup Effects.")
This study proposes a method to identify treatment effects without exclusion restrictions in randomized experiments with noncompliance. Exploiting a baseline survey commonly available in randomized experiments, I decompose the intention-to-treat effects conditional on the endogenous treatment status. I then identify these parameters to understand the effects of the assignment and treatment. The key assumption is that a baseline variable maintains rank orders similar to the control outcome. I also reveal that the change-in-changes strategy may work without repeated outcomes. Finally, I propose a new estimator that flexibly incorporates covariates and demonstrate its properties using two experimental studies.
Joint work with Takuya Ishihara. Current version, 2020, September 16. New draft will be ready soon.
(Formerly circulated as "Manipulation Robust Regression Discontinuity Designs")
Regression discontinuity designs (RDDs) may not deliver reliable results if units manipulate their running variables. It is commonly believed that imprecise manipulations are harmless and, diagnostic tests detect precise manipulations. However, we demonstrate that RDDs may fail to point-identify in the presence of imprecise manipulation, and that not all harmful manipulations are detectable.
To formalize these claims, we propose a class of RDDs with harmless or detectable manipulations over locally randomized running variables as manipulation-robust RDDs. The conditions for the manipulation-robust RDDs may be intuitively verified using the institutional background. We demonstrate its verification process in case studies of applications that use the McCrary (2008) density test. The restrictions of manipulation-robust RDDs generate partial identification results that are robust to possible manipulation. We apply the partial identification result to a controversy regarding the incumbency margin study of the U.S. House of Representatives elections. The results show the robustness of the original conclusion of Lee (2008).
Joint work with Kohei Kawaguchi. Current version, 2020, April 21
We propose an estimation procedure for discrete choice models of differentiated products with possibly high-dimensional product attributes. In our model, high-dimensional attributes can be determinants of both mean and variance of the indirect utility of a product. The key restriction in our model is that the high-dimensional attributes affect the variance of indirect utilities only through finitely many indices. In a framework of the random-coefficients logit model, we show a bound on the error rate of a l1-regularized minimum distance estimator and prove the asymptotic linearity of the de-biased estimator.
Works in Progress
"Complementarity in Couples’ Retirement : The Effect of Mandatory Retirement Age Extension"（with Mika Akesaka）
"Targeted Meta-Analysis" (with Toru Kitagawa)