"Win/Loss Data and Consumer Switching Costs: Measuring Diversion Ratios and the Impact of Mergers" the Journal of Industrial Economics, 72 (1) (March 2024), 327-355, with Jeff Qiu and Gloria Sheu. (WP Version) 


The diversion ratio is a key input to indicators of merger harm like up- ward pricing pressure. Measuring the diversion ratio, however, is challenging in the presence of consumer switching costs. We propose an identification strategy for diversion that relies on win/loss data from the two merging firms, a type of data that antitrust authorities can frequently obtain. First, we show that win/loss data from the merging firms and market shares for all firms in two periods are sufficient to identify the diversion ratios between the merging partners. Second, we show that win/loss data from the merging firms are sufficient for partial identification, and we construct a lower bound that provides a good approximation to the diversion ratio when switching costs are high. We demonstrate the performance of our method with numerical simulations and with an application to the Anthem/Cigna merger.

“Privatization and Productivity in China.”  the RAND Journal of Economics, 52:4 (Winter 2021), 884–916, with Yuyu Chen, Mitsuru Igami, and Mo Xiao. (WP version)


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.

Working Papers

"Local-polynomial Estimation for Multivariate Regression Discontinuity Designs" submitted

Joint work with Takuya Ishihara, Daisuke Kurisu, and Yasumasa Matsuda. Current version, 2024, Feb. 15.


We introduce a multivariate local-linear estimator for multivariate regression discontinuity designs in which treatment is assigned by crossing a boundary in the space of running variables. The dominant approach uses the Euclidean distance from a boundary point as the scalar running variable; hence, multivariate designs are handled as uni-variate designs. However, the distance running variable is incompatible with the assumption for asymptotic validity. We handle multivariate designs as multivariate. In this study, we develop a novel asymptotic normality for multivariate local-polynomial estimators. Our estimator is asymptotically valid and can capture heterogeneous treatment effects over the boundary. We demonstrate the effectiveness of our estimator through numerical simulations. Our empirical illustration of a Colombian scholarship study reveals a richer heterogeneity (including its absence) of the treatment effect that is hidden in the original estimates.

"Market Size and Market Power"  submitted

Joint work with Donghyuk Kim. Current version, 2024, Feb. 7th.


We propose a method of estimating the market size of a differentiated product market using data on firm entries and quantities sold. Market sizes are often unobserved, and observing quantities is insufficient for computing market shares, which are key inputs to market power estimation. We exploit an entry model with competition in differentiated product markets to estimate the joint distribution of equilibrium entries and market shares. We recover the market sizes through an accounting relationship between quantities and market shares. Our approach may reduce the potential bias in market power estimates and may facilitate other studies of market sizes.

"Joint diagnostic test of regression discontinuity designs: multiple testing problem"  submitted

Joint work with Koki Fusejima and Takuya Ishihara. Current version, 2023, Oct. 5th. [R package]


Current diagnostic tests for regression discontinuity (RD) design face a multiple testing problem. We find a massive over-rejection of the identifying restriction among empirical RD studies published in top-five economics journals. Each test achieves a nominal size of 5%; however, the median number of tests per study is 12. Consequently, more than one-third of studies reject at least one of these tests and their diagnostic procedures are invalid for justifying the identifying assumption. We offer a joint testing procedure to resolve the multiple testing problem. Our procedure is based on a new joint asymptotic normality of local linear estimates and local polynomial density estimates. In simulation studies, our joint testing procedures outperform the Bonferroni correction. We implement the procedure as an R package, rdtest, with two empirical examples in its vignettes.

"Manipulation-Robust Regression Discontinuity Designs"  submitted

Joint work with Takuya Ishihara. Current version, 2023, September 28th. [full slides] (as of 2022, Nov. 18) 

(Formerly circulated as "Harmless and Detectable Manipulations of the Running Variable in Regression Discontinuity Designs: Tests and Bounds")


We present a new identification condition for regression discontinuity designs. We replace the local randomization of Lee (2008) with two restrictions on its threat, namely, the manipulation of the running variable. Furthermore, we provide the first auxiliary assumption of McCrary (2008)’s diagnostic test to detect manipulation. Based on our auxiliary assumption, we derive a novel expression of moments that immediately implies the worst-case bounds of Gerard, Rokkanen, and Rothe (2020) and an enhanced interpretation of their target parameters. We highlight two issues: an overlooked source of identification failure, and a missing auxiliary assumption to detect manipulation. In the case studies, we illustrate our solution to these issues using institutional details and economic theories.

“Noncompliance in randomized control trials without exclusion restrictions.”   

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.


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

"Finite-Sample Optimal Estimation for Binary-Outcome Regression Discontinuity Designs"(with Takuya Ishihara and Kohei Yata

"Spatial Regression Discontinuity Designs"(with Takuya Ishihara, Daisuke Kurisu, and Yasumasa Matsuda)

"Complementarity in Couples’ Retirement : The Effect of Mandatory Retirement Age Extension"(with Mika Akesaka)

"Linear Regressions, Shorts to Long" (with Toru Kitagawa) [Preliminary draft]