Outsourcing to emerging markets: Theoretical perspectives and policy implications

Creating value through outsourcing has emerged as a popular competitive strategy for firms in various industries. In order to survive in the domestic and international marketplaces, firms, especially in developed countries, are seeking opportunities offshore, which is one focus of globalization. Offshore outsourcing has emerged as a popular competitive strategy and emerging markets have become increasingly attractive locations. As firms in developed countries (e.g., the US) continue to face enormous challenges to sustain competitive advantage, outsourcing to emerging markets is becoming an increasingly important source of business renewal and corporate transformation.In spite of the growing strategic significance of sourcing, we have limited knowledge of offshoring and outsourcing to emerging markets. The objectives of this review are fourfold: (1) to provide a better understanding of the concepts of offshoring and outsourcing business models, (2) to discuss relevant theoretical perspectives related to outsourcing, (3) to present a taxonomy of outsourcing strategies drawing on the extant literature, and (4) to discuss public policy implications. Conclusions and direction for future research are provided.     

An empirical investigation of the box-cox model and a nonlinear least squares alternative

In this paper we present a generalized functional form estimator, recently developed by jeffrey Wooldridge; and then we compare it empirically to the popular Box-Cox (BC) estimator using three data sets. We begin by briefly reviewing the drawbacks of the BC estimator. We Then introduce the nonlinear lest squares (NLS) alternative of Wooldridge which retains the desirable qualities of the BC estimator without the associated theoretical problems. We continue by applying both the BC and the NLS models to data from three classic hedonic regression studies and then compare the estimation resuts-point estimates, inferences and fitted values. The estimations include a wage rate equation, and two computer hedonic regression equations, one using data from a classic study by Gregory Chow and the other using an IBM data set that formed the basis of the new official BLS computer price index.     

An improved algorithm for the triples test

An algorithm, in the form of a Fortran subroutine TRIPLE, is given to compute statistics for the triples test for symmetry, The computational complexity of the algorithm is O(n²) which is an improvement over the straight for ward method, which is O(n³).     

Do “Clicker” Educational Sessions Enhance the Effectiveness of a Social Norms Marketing Campaign?

Abstract

Objective: Social norms campaigns are a cost-effective way to reduce high-risk drinking on college campuses. This study compares effectiveness of a “standard” social norms media (SNM) campaign for those with and without exposure to additional educational sessions using audience response technology (“clickers”). Methods: American College Health Association's National College Health Assessment questions are used to evaluate actual and perceived use. Additional survey questions assess individual exposure to the interventions. Results: The authors find “clicker” technology to be more effective than social norms poster media alone in reducing misperceptions of normative alcohol use for those students who attended clicker sessions. Conclusion: Poster SNM campaigns may be most effective when supported by group “clicker” heath-related sessions.     

Gibbs sampling for marginal posterior expectations

In earlier work (Gelfand and Smith, 1990 and Gelfand et al, 1990) a sampling based approach using the Gibbs sampler was offered as a means for developing marginal posterior densities for a wide range of Bayesian problems several of which were previously inaccessible. Our purpose here is two-fold. First we flesh out the implementation of this approach for calculation of arbitrary expectations of interest. Secondly we offer comparison with perhaps the most prominent approach for calculating posterior expectations, analytic approximation involving application of the LaPlace method. Several illustrative examples are discussed as well. Clear advantages for the sampling based approach emerge.     

Evaluation of two school‐based HIV prevention interventions in the border city of Tijuana,Mexico

This research project examined the individual and combined effectiveness of an HIV prevention workshop and a free condom distribution program in four high schools in Tijuana, Mexico. Adolescents (N = 320) completed baseline measures on sexual practices and theoretical correlates and participated in a two‐part study. In Study I, students were randomly assigned to an HIV prevention workshop or a control condition, with a 3‐month follow‐up assessment. Results indicate three significant workshop benefits regarding HIV transmission by altering sexual initiation, access to condoms, and traditional beliefs regarding condoms. In Study 2, we set up a condom distribution program at two of the participating schools, and students completed a 6‐month follow‐up assessment. Results indicate that exposure to the workshop followed by access to the condom distribution program yielded two beneficial results for reducing HIV transmission: moderating sexual initiation and increasing condom acquisition. Access to the condom distribution program alone had no effects on behavioral and psy‐chosocial correlates of HFV transmission. We discuss implications of these results.     

Estimation and Inference for Linear Panel Data Models Under Misspecification When Both n and T are Large

This article considers fixed effects (FE) estimation for linear panel data models under possible model misspecification when both the number of individuals, n, and the number of time periods, T, are large. We first clarify the probability limit of the FE estimator and argue that this probability limit can be regarded as a pseudo-true parameter. We then establish the asymptotic distributional properties of the FE estimator around the pseudo-true parameter when n and T jointly go to infinity. Notably, we show that the FE estimator suffers from the incidental parameters bias of which the top order is O(T^{? 1}), and even after the incidental parameters bias is completely removed, the rate of convergence of the FE estimator depends on the degree of model misspecification and is either (nT)^{? 1/2} or n^{? 1/2}. Second, we establish asymptotically valid inference on the (pseudo-true) parameter. Specifically, we derive the asymptotic properties of the clustered covariance matrix (CCM) estimator and the cross-section bootstrap, and show that they are robust to model misspecification. This establishes a rigorous theoretical ground for the use of the CCM estimator and the cross-section bootstrap when model misspecification and the incidental parameters bias (in the coefficient estimate) are present. We conduct Monte Carlo simulations to evaluate the finite sample performance of the estimators and inference methods, together with a simple application to the unemployment dynamics in the U.S.     

Labour Underutilisation and Gender: Unemployment Versus Hidden-Unemployment

As labour markets have become more complex there has been increasing interest among researchers in understanding the ways that social and labour market processes and contexts impact on various labour market states. One important area has been in understanding the differences between unemployment and hidden unemployment. This paper considers the ways in which these two labour market states differ for a sample of male and female respondents to the Household Income and Labour Dynamics Australia (HILDA) survey. It presents data related to the reasons why respondents in these two labour force states consider they are jobless and analyses the characteristics of male and female respondents in the two labour market states to consider differences in outcomes. The findings suggest that there are differences in the two states of labour market outcomes and that these are further complicated when one considers processes for males and females.     

A Critical Look at Some Analyses of Major League Baseball Salaries

At a data analysis exposition sponsored by the Section on Statistical Graphics of the ASA in 1988, 15 groups of statisticians analyzed the same data about salaries of major league baseball players. By examining what they did, what worked, and what failed, we can begin to learn about the relative strengths and weaknesses of different approaches to analyzing data. The data are rich in difficulties. They require reexpression, contain errors and outliers, and exhibit nonlinear relationships. They thus pose a realistic challenge to the variety of data analysis techniques used. The analysis groups chose a wide range of model-fitting methods, including regression, principal components, factor analysis, time series, and CART. We thus have an effective framework for comparing these approaches so that we can learn more about them. Our examination shows that approaches commonly identified with Exploratory Data Analysis are substantially more effective at revealing the underlying patterns in the data and at building parsimonious, understandable models that fit the data well. We also find that common data displays, when applied carefully, are often sufficient for even complex analyses such as this.     

Comparing several experimental groups with a control in the multivariate case

This paper presents a multivariate extension of Dunnett's test for comparing simultaneously k treatment group means with a single control group mean. A test based on Hotelling T²statistics is presented and approximate critical values are evaluated for the case of equal numbers of observations in each group, for the .05 and .01 levels of significance, for 1 to 5 variates, for 1 to 10 treatment groups, and for varying degrees of freedom. The accuracy of the procedure for generating approximate critical values is assessed via simulation studies conducted for selected cases and an example is presented using real data.