How do the Location Determinants of Vertical FDI and Horizontal FDI Differ?

Distinguishing between vertical and horizontal foreign direct investment (FDI), this paper examines how the location determinants of the two types of FDI differ. Based on a conditional logit model and data on Japanese foreign affiliates, the main findings are that the most important determinant for horizontal FDI is a large market, whereas labor costs play a significant role in the case of vertical FDI. Concerning the effect of tariffs, geographical distance, and labor quality on the location decision, this study obtains results that differ from those of previous studies on the determinants of location choice of Japanese multinationals. First, tariffs and distance have opposite effects on the location decisions in the case of horizontal and vertical FDI. Second, labor quality has a positive effect only on the location decision of horizontal FDI.horizontal FDI, vertical FDI, location decisoin, Japan's FDI

pH-Responsive Polymers for Biosurfaces: An Investigation of Solution Properties and Surface Modifying Performance

Gantrez® copolymers are used widely in the personal care industry, but their mechanisms of interaction with dental surfaces are poorly understood. In this study, we determined a number of the solution and polymer properties that control deposition, adsorption and adhesion in Gantrez® polymer aqueous solutions to model dental surfaces. QCM studies specifically showed that polymer adsorption to model hydroxyapatite surfaces occurred only in solutions with polymer concentration above c*. Adsorption and adhesion was greater in solutions of higher pH, where greater charge density along the polymer backbone was expected. For films prepared via spin-coating onto mica surfaces, addition of surfactants to the solution resulted in film surfaces with more regular morphology and surface coverage. Flow-through studies in QCM, however, showed that there was no polymer deposition or adhesion to the silicon surface from solution in the presence or absence of surfactants. The results from these studies provide an expanded database to allow researchers to design formulations to meet specific requirements of deposition and adhesion for personal care applications

Parametric or nonparametric? A parametricness index for model selection

In model selection literature, two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike's information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric scenario). But there is little work that addresses if it is possible and how to detect the situation that a specific model selection problem is in. In this work, we differentiate the two scenarios theoretically under some conditions. We develop a measure, parametricness index (PI), to assess whether a model selected by a potentially consistent procedure can be practically treated as the true model, which also hints on AIC or BIC is better suited for the data for the goal of estimating the regression function. A consequence is that by switching between AIC and BIC based on the PI, the resulting regression estimator is simultaneously asymptotically efficient for both parametric and nonparametric scenarios. In addition, we systematically investigate the behaviors of PI in simulation and real data and show its usefulness.Comment: Published in at http://dx.doi.org/10.1214/11-AOS899 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Kernel Estimation and Model Combination in a Bandit Problem with Covariates

Multi-armed bandit problem is an important optimization game that requires an exploration-exploitation tradeoff to achieve optimal total reward. Motivated from industrial applications such as online advertising and clinical research, we consider a setting where the rewards of bandit machines are associated with covariates, and the accurate estimation of the corresponding mean reward functions plays an important role in the performance of allocation rules. Under a flexible problem setup, we establish asymptotic strong consistency and perform a finite-time regret analysis for a sequential randomized allocation strategy based on kernel estimation. In addition, since many nonparametric and parametric methods in supervised learning may be applied to estimating the mean reward functions but guidance on how to choose among them is generally unavailable, we propose a model combining allocation strategy for adaptive performance. Simulations and a real data evaluation are conducted to illustrate the performance of the proposed allocation strategy

Reconsidering the Backward Vertical Linkages of Foreign Affiliates: Evidence from Japanese Multinationals

This paper examines the determinants of the backward vertical linkages of Japanese foreign affiliates in manufacturing for the period 1994-2000, focusing on the local backward linkages, or local procurements in the host country. Our major findings are twofold. First, the unobserved affiliate-specific characteristics explain the large part of the variation of the backward linkages among foreign affiliates. Second, the experience of the affiliate has positive and sometimes non-linear impacts on local procurements for the affiliates, especially in Southeast Asia and China.Vertical Backward Linkages, Foreign Direct Investment, Local Procurements, Southeast Asia, China