Efficiency or Competition? A Structural Econometric Analysis of Canada's AWS Auction and the Set‐Aside Provision

In 2008, Industry Canada auctioned 105 MHz of spectrum to a group of bidders that included incumbents and potential new entrants into the Canadian mobile phone market, raising $4.25 billion. In an effort to promote new entry, 40 MHz of spectrum was set‐aside for new entrants. In order to estimate the implicit cost of the set‐aside provision, we estimate the parameters of the bidders' profit function via a maximum match estimator based on the notion of pairwise stability in matches. We find that all telecommunications firms valued both geographic complementarities across auction licenses as well as absolute spectrum. Under a reasonable alternative scenario, our results indicate that the set‐aside led to a total profit loss of approximately 10%.     

Institutionalization or Decoupling? An Exploratory Analysis of the UN Global Compact LEAD Initiative

Institutionalization occurs when organizations adopt policies and practices in an attempt to increase their legitimacy and competitive position. This process assumes that an organization's formal policies (macro‐level) are translated into organizational practices (micro‐level). This translation, however, can fail to occur, resulting in organizational decoupling and de‐institutionalization. Drawing on an institutionalization typology based on different levels of legitimacy (macro) and taken‐for‐grantedness (micro), the article draws on data from an assessment of the United Nations Global Compact LEAD initiative, assessing the extent to which this initiative is being institutionalized in the member firms and examining the implications for the LEAD initiative and our thinking about institutionalization.     

Network Embeddedness in Exploration and Exploitation of Joint R&D Projects: A Structural Approach

This paper analyses how network embeddedness affects the exploration and exploitation of R&D project performance. By developing joint projects, partners and projects are linked to one another and form a network that generates social capital. We examine how the location, which determines the access to information and knowledge within a network of relationships, affects the performance of projects. We consider this question in the setup of exploration and exploitation projects, using a database built from an EU framework. We find that each of the structural embeddedness dimensions (degree, betweenness and eigenvector centrality) have a different impact on the exploration and exploitation project performance. Our empirical analysis extends to project management literature and social capital theory, by including the effect that the acquisition of external knowledge has on the performance of the project.     

Nonparametric Estimation of the Number of Drug Users in Hong Kong Using Repeated Multiple Lists

We update a previous approach to the estimation of the size of an open population when there are multiple lists at each time point. Our motivation is 35 years of longitudinal data on the detection of drug users by the Central Registry of Drug Abuse in Hong Kong. We develop a two‐stage smoothing spline approach. This gives a flexible and easily implemented alternative to the previous method which was based on kernel smoothing. The new method retains the property of reducing the variability of the individual estimates at each time point. We evaluate the new method by means of a simulation study that includes an examination of the effects of variable selection. The new method is then applied to data collected by the Central Registry of Drug Abuse. The parameter estimates obtained are compared with the well known Jolly–Seber estimates based on single capture methods.     

Comparison of hazard rates for dependent random variables

In this paper, we define and study a new notion for the comparison of the hazard rates of two random variables taking into account their mutual dependence. Properties, applications and the comparison for a data set are given.     

Regularization with Maximum Entropy and Quantum Electrodynamics: The Merg(E) Estimators

It is well-known that under fairly conditions linear regression becomes a powerful statistical tool. In practice, however, some of these conditions are usually not satisfied and regression models become ill-posed, implying that the application of traditional estimation methods may lead to non-unique or highly unstable solutions. Addressing this issue, in this paper a new class of maximum entropy estimators suitable for dealing with ill-posed models, namely for the estimation of regression models with small samples sizes affected by collinearity and outliers, is introduced. The performance of the new estimators is illustrated through several simulation studies.     

Validation of Nonparametric Two-sample Bootstrap in ROC Analysis on Large Datasets

The nonparametric two-sample bootstrap is applied to computing uncertainties of measures in receiver operating characteristic (ROC) analysis on large datasets in areas such as biometrics, speaker recognition, etc. when the analytical method cannot be used. Its validation was studied by computing the standard errors of the area under ROC curve using the well-established analytical Mann–Whitney statistic method and also using the bootstrap. The analytical result is unique. The bootstrap results are expressed as a probability distribution due to its stochastic nature. The comparisons were carried out using relative errors and hypothesis testing. These match very well. This validation provides a sound foundation for such computations.