How does distance affect market entry mode choice? Evidence from French companies

The objective of this article is to examine the relevant dimensions of distance for foreign market entry mode choice. Based on a sample of 203 interfirm linkages formed by French multinationals with partners across the world, we analyze the impact of four dimensions of distance on the choice between cooperative alliances and mergers-acquisitions. The findings indicate that administrative and economic distance have a significant influence on market entry mode choice, whereas the impact of cultural and geographic distance is not significant. They further highlight the important role of the host country's government effectiveness for market entry mode decisions.     

冷战后的中美政治安全关系

冷战结束十年以来 ,美国的对华政策依然摇摆不定。之所以如此 ,是因为十年来美国视中国为敌还是为友 ,或者非敌非友 ,至今还未定性。而美国难于在其国际政治安全战略中给中国定性的原因 ,则与以下的三个层次密切相关 ,即美国的国内政治层次、中美关系的发展层次和全球层次。     

Bayesian modelling of catch in a north-west Atlantic fishery

Summary. We model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used and the mesh size of the nets) are obvious candidates, but one can also consider the season or the actual location of the catch. Our database leads to 28 possible regressors (arising from six continuous variables and four categorical variables, whose 22 levels are treated separately), resulting in a set of 177 million possible linear regression models for the log-catch. Zero observations are modelled separately through a probit model. Inference is based on Bayesian model averaging, using a Markov chain Monte Carlo approach. Particular attention is paid to the prediction of catches for single and aggregated ships.     

Estimation of generalized linear latent variable models

Summary.  Generalized linear latent variable models (GLLVMs), as defined by Bartholomew and Knott, enable modelling of relationships between manifest and latent variables. They extend structural equation modelling techniques, which are powerful tools in the social sciences. However, because of the complexity of the log-likelihood function of a GLLVM, an approximation such as numerical integration must be used for inference. This can limit drastically the number of variables in the model and can lead to biased estimators. We propose a new estimator for the parameters of a GLLVM, based on a Laplace approximation to the likelihood function and which can be computed even for models with a large number of variables. The new estimator can be viewed as an M -estimator, leading to readily available asymptotic properties and correct inference. A simulation study shows its excellent finite sample properties, in particular when compared with a well-established approach such as LISREL. A real data example on the measurement of wealth for the computation of multidimensional inequality is analysed to highlight the importance of the methodology.     

A Semi-parametric Regression Model with Errors in Variables

Abstract.  In this paper, we consider a partial linear regression model with measurement errors in possibly all the variables. We use a method of moments and deconvolution to construct a new class of parametric estimators together with a non-parametric kernel estimator. Strong convergence, optimal rate of weak convergence and asymptotic normality of the estimators are investigated.     

苏北“三农”问题的现状分析与研究

从江苏省“两个率先”目标的高度,就江苏苏北地区的“三农”问题进行分析,指出能否解决苏北“三农”问题已是影响江苏实现“两个率先”目标的关键,进而提出了解决苏北“三农”问题应从转变观念、提高农民国民待遇、加强农村基础设施建设和加快农村经济结构调整,以及加快苏北城市化建设,促进农村劳动力转移和控制人口增长,开发苏北农村的人力资源着手。     

湖南“二王”与近代湖南书院改制

王闿运与王先谦在近代史上并称湖南“二王,”他们都与湖南书院改制有非常密切的关系。王闿运不认同西式学堂教育,主张在传统书院教育体制之内改善书院教育,船山书院在其主掌之下至1915年方改为学堂;王先谦接受了西式学堂体制,但其坚守学堂不能离“道”而行,在湖南书院改制中呈现出双重趋向。“二王”与湖南书院改制的关系,透射出传统型知识分子在晚清变局中的复杂心态。     

Joint impact of multiple observations on a subset of variables in multiple linear regression

In multiple linear regression analysis, each observation affects the fitted regression equation differently and has varying influences on the regression coefficients of the different variables. Chatterjee & Hadi (1988) have proposed some measures such as DSSE_ij (Impact on Residual Sum of Squares of simultaneously omitting the ith observation and the jth variable), F_j (Partial F-test for the jth variable) and F_j(i) (Partial F-test for the jth variable omitting the ith observation) to show the joint impact and the interrelationship that exists among a variable and an observation. In this paper we have proposed more extended form of those measures DSSE_IJ, F_J and F_J(I) to deal with the interrelationships that exist among the multiple observations and a subset of variables by monitoring the effects of the simultaneous omission of multiple variables and multiple observations.     

Gender‐Sensitive Research in a Chinese Community

The aim of this article is to foster an awareness of the need for gender‐sensitive research in the context of the methodological and ethical challenges posed by such research. We trace the development of gender sensitivity and masculinity in social work practice and research and connect this to an overview of the issues posed by research on sensitive topics. Reflecting on a research project involving Chinese male sexual abuse survivors, we draw conclusions illustrating and proposing a range of methodological practices and ethical safeguards. We underscore the importance of gender‐sensitivity in performing research on sensitive topics with men in a Chinese community.     

Estimation for Partially Linear Single-index Instrumental Variables Models

In this article, we generalize the partially linear single-index models to the scenario with some endogenous covariates variables. It is well known that the estimators based on the existing methods are often inconsistent because of the endogeneity of covariates. To deal with the endogenous variables, we introduce some auxiliary instrumental variables. A three-stage estimation procedure is proposed for partially linear single-index instrumental variables models. The first stage is to obtain a linear projection of endogenous variables on a set of instrumental variables, the second stage is to estimate the link function by using local linear smoother for given constant parameters, and the last stage is to obtain the estimators of constant parameters based on the estimating equation. Asymptotic normality is established for the proposed estimators. Some simulation studies are undertaken to assess the finite sample performance of the proposed estimation procedure.