Creating a reference to a complex emergency situation using time series methods: war in Guinea-Bissau 1998-1999

Impacts of complex emergencies or relief interventions have often been evaluated by absolute mortality compared to international standardized mortality rates. A better evaluation would be to compare with local baseline mortality of the affected populations. A projection of population-based survival data into time of emergency or intervention based on information from before the emergency may create a local baseline reference. We find a log-transformed Gaussian time series model where standard errors of the estimated rates are included in the variance to have the best forecasting capacity. However, if time-at-risk during the forecasted period is known then forecasting might be done using a Poisson time series model with overdispersion. Whatever, the standard error of the estimated rates must be included in the variance of the model either in an additive form in a Gaussian model or in a multiplicative form by overdispersion in a Poisson model. Data on which the forecasting is based must be modelled carefully concerning not only calendar-time trends but also periods with excessive frequency of events (epidemics) and seasonal variations to eliminate residual autocorrelation and to make a proper reference for comparison, reflecting changes over time during the emergency. Hence, when modelled properly it is possible to predict a reference to an emergency-affected population based on local conditions. We predicted childhood mortality during the war in Guinea-Bissau 1998-1999. We found an increased mortality in the first half-year of the war and a mortality corresponding to the expected one in the last half-year of the war.     

Comparative Journalism Research – An Overview

This overview focuses on the most common type of comparative journalism research, which is cross‐national comparative research. The overview presents a typology for different types of comparative journalism research, based on whether the research interest is in journalism as an activity or as a product; and, in the case of journalism as an activity, whether the interest is in the system level, the organizational level, or the individual level of journalism. The overview finds that the analysis of journalism on the individual level and of journalism as a product are the most common types of comparative research, whereas comparative analysis of journalism on the organizational level is much under‐studied.     

Robust Poisson likelihood estimation for frailty Cox models: A simulation study

Frailty models can be fit as mixed-effects Poisson models after transforming time-to-event data to the Poisson model framework. We assess, through simulations, the robustness of Poisson likelihood estimation for Cox proportional hazards models with log-normal frailties under misspecified frailty distribution. The log-gamma and Laplace distributions were used as true distributions for frailties on a natural log scale. Factors such as the magnitude of heterogeneity, censoring rate, number and sizes of groups were explored. In the simulations, the Poisson modeling approach that assumes log-normally distributed frailties provided accurate estimates of within- and between-group fixed effects even under a misspecified frailty distribution. Non-robust estimation of variance components was observed in the situations of substantial heterogeneity, large event rates, or high data dimensions.     

Learning discrete decomposable graphical models via constraint optimization

Statistical model learning problems are traditionally solved using either heuristic greedy optimization or stochastic simulation, such as Markov chain Monte Carlo or simulated annealing. Recently, there has been an increasing interest in the use of combinatorial search methods, including those based on computational logic. Some of these methods are particularly attractive since they can also be successful in proving the global optimality of solutions, in contrast to stochastic algorithms that only guarantee optimality at the limit. Here we improve and generalize a recently introduced constraint-based method for learning undirected graphical models. The new method combines perfect elimination orderings with various strategies for solution pruning and offers a dramatic improvement both in terms of time and memory complexity. We also show that the method is capable of efficiently handling a more general class of models, called stratified/labeled graphical models, which have an astronomically larger model space.     

The Value of Safety as Revealed in the Swedish Car Market: An Application of the Hedonic Pricing Approach

In this article the hedonic regression technique is used to estimate the value of traffic safety, using information from the Swedish market for automobiles. The results from the study show that the market price of an automobile is negatively correlated with its inherent risk level, i.e. Swedish car consumers pay a safety premium for safer cars. In comparison to previous Swedish stated-preference studies, this study reveals a lower willingness to pay for additional car safety, which might be a result of the interaction between government interventions and individual self-insurance and self-protection.JEL Classification: C51, D61, J28     

Subsidiary embeddedness as a determinant of divisional headquarters involvement in innovation transfer processes

This paper conceptualizes divisional headquarters as an important hub-firm within the MNE, orchestrating innovation transfer processes between subsidiaries. It is argued that the internal and external embeddedness of a subsidiary hosting an innovation development project subsequently influences divisional headquarters involvement in the transfer of that innovation. In this way, embeddedness, i.e., the relationships that firms have with each other in the innovation development phase, is brought to the fore as an important factor for MNE subsidiaries hosting innovation development projects for explaining the involvement of divisional headquarters in a subsequent transfer. This highlights divisional headquarters as an active orchestrator of innovation transfers within the MNE. Data from 169 innovation transfer projects as well as 146 internal and 121 external embedded relationships at subsidiary level support the argument of embeddedness as a driver of divisional headquarters involvement in subsidiary innovation transfer projects. From a business network perspective, the findings highlight the role of internal and external embeddedness during innovation development in the subsequent involvement of divisional headquarters in the transfer phase. Embeddedness is not only important for subsidiaries in the innovation development phase but also for divisional headquarters involvement in, and orchestration of, innovation transfer.     

Neo-colonial dynamics in global professional service firms: a periphery perspective

ABSTRACT

This article analyses neo-colonial dynamics at peripheral units of a Western global professional service firm. Drawing on postcolonial theory, an empirical study at global news agency Reuters’ subsidiaries in Mumbai and Bangalore shows how socio-ideological and technocratic control mechanisms allow subsidiary workers to elevate their status, while still cementing their role as dominated in the global hierarchy and silencing any resistance. Thus, on the one hand, it might appear that the global service firm provides periphery units the opportunity to become equals of core units, representing a ‘nurturing’ role achieved by instilling aspirational values and shaping imaginations. On the other hand, coercive and universalising routines such as performance assessments highlight the ambivalent nature of global firm initiatives to incorporate the periphery on an equal footing with core units, demonstrating the countervailing nature of contemporary power relations.     

Topological Performance Measures as Surrogates for Physical Flow Models for Risk and Vulnerability Analysis for Electric Power Systems

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist that can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. In this article, we suggest a classification of these models, which span from simple topologically‐oriented models to advanced physical‐flow‐based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this article is to compare performance estimates achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combinations of models to use when analyzing large‐scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed.