Corporate Governance and Long Term Performance of the Business Groups: The Case of Chaebols in Korea

The existence of the business groups has been associated with market failure in emerging economies, and thus their performance has been argued and found to have declined with development of market institutions surrounding them. This paper takes up this issue of long-term performance of the business groups but argues that it has also to do with the internal problems, such as changes in the ownership and governance structure. It finds, with the Korea data and new method and theoretical grounds, that the relative performance of the business groups, the Chaebols, had consistently declined over the 1980s and 1990s although they were more efficient than the non-Chaebol firms during the early 1980s. The results are robust to different estimation methods, and also to controls for the possible survivorship bias, industry composition, and scale effects. The paper explains the performance change by examining the decrease of the shares held by the controlling families and the associated aggravation of the agency problem leading to unjustifiable expansion drives.Business groups, Long Term performance, Corporate Governance, Chaebols

Inference on conditional quantile processes in partially linear models with applications to the impact of unemployment benefits

We propose methods to estimate and conduct inference on conditional quantile processes for models with nonparametric and linear components. The estimation procedure uses local linear or quadratic regressions, with the bandwidth allowed to vary across quantiles to adapt to data sparsity. We establish a Bahadur representation that holds uniformly in the covariate value and the quantile index. Then,we show that the proposed estimator converges weakly to a Gaussian process and develop methods for constructing uniform confidence bands and hypothesis testing. Our results also cover locally partially linear models with boundary points, thereby allowing for Sharp Regression Discontinuity Designs (SRD). This allows us to study the effects of unemployment insurance (UI) benefits extensions using the dataset of Nekoei and Weber (2017) who found a statistically significant effect, though of minor economic importance using an SRD focusing on the average effect. Our model allows heterogeneity with respect to both the covariate and the quantile. We find economically strong significant effects in the tail of the distribution,say the 10% quantile of the outcome variable (e.g., the wage change distribution). Under a rank invariance assumption, this implies that individuals who benefited the most are those who would have experienced substantial wage cuts if there were no benefit extension. Since our setup allows for discrete covariates, we also find positive and statistically significant effects for white-collar and female workers and those with a college education, but not for blue-collar male workers without higher education. Hence, while UI benefits reduce the within-group inequality for some subgroups by covariates, they can be viewed as regressive and enhancing between-group inequality, although they also help to bridge the gender gap.First author draf

Essays on Semiparametric Bayesian Regression

90 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.Throughout the thesis, we emphasize that quantile regression provides a nonparametric method to construct the probabilistic model, the likelihood, so it provide a simple but powerful strategy for semiparametric Bayesian methods.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Replication Data for: Estimating the Effects of the English Rule on Litigation Outcomes

Replication Data for: Estimating the Effects of the English Rule on Litigation Outcome

Cluster Robust Covariance Matrix Estimation in Panel Quantile Regression with Individual Fixed Effects

This study develops cluster robust inference methods for panel quantile regression (QR) models with individual fixed effects, allowing for temporal correlation within each individual. The conventional QR standard errors can seriously underestimate the uncertainty of estimators and, therefore, overestimate the significance of effects, when outcomes are serially correlated. Thus, we propose a clustered covariance matrix (CCM) estimator to solve this problem. The CCM estimator is an extension of the heteroskedasticity and autocorrelation consistent covariance matrix estimator for QR models with fixed effects. The autocovariance element in the CCM estimator can be substantially biased, due to the incidental parameter problem. Thus, we develop a bias-correction method for the CCM estimator. We derive an optimal bandwidth formula that minimizes the asymptotic mean squared errors, and propose a data-driven bandwidth selection rule. We also propose two cluster robust tests, and establish their asymptotic properties. We then illustrate the practical usefulness of the proposed methods using an empirical application.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Uniform Inference on Quantile Effects under Sharp Regression Discontinuity Designs

<p>This study develops methods for conducting uniform inference on quantile treatment effects for sharp regression discontinuity designs. We develop a score test for the treatment significance hypothesis and Wald-type tests for the hypotheses related to treatment significance, homogeneity, and unambiguity. The bias from the nonparametric estimation is studied in detail. In particular, we show that under some conditions, the asymptotic distribution of the score test is unaffected by the bias, without under-smoothing. For situations where the conditions can be restrictive, we incorporate a bias correction into the Wald tests and account for the estimation uncertainty. We also provide a procedure for constructing uniform confidence bands for quantile treatment effects. As an empirical application, we use the proposed methods to study the effect of cash-on-hand on unemployment duration. The results reveal pronounced treatment heterogeneity and also emphasize the importance of considering the long-term unemployed.</p

WHAT DO KERNEL DENSITY ESTIMATORS OPTIMIZE?

Abstract. Some linkages between kernel and penalty methods of density estimation are explored. It is recalled that classical Gaussian kernel density estimation can be viewed as the solution of the heat equation with initial condition given by data. We then observe that there is a direct relationship between the kernel method and a particular penalty method of density estimation. For this penalty method, solutions can be characterized as a weighted average of Gaussian kernel density estimates, the average taken with respect to the bandwidth parameter. A Laplace transform argument shows that this weighted average of Gaussian kernel estimates is equivalent to a fixed bandwidth kernel estimate using a Laplace kernel. Extensions to higher order kernels are considered and some connections to penalized likelihood density estimators are made in the concluding sections. 1