Foreign Participation and Hiring Patterns after Privatization

Critics of globalization claim that foreign ownership of privatized firms is linked to negative post‐privatization labor outcomes, such as more firing and less hiring. This paper uses new firm‐level data for a cross‐section of countries to test this idea and provides evidence that foreign purchasers of state‐owned enterprises tend to acquire firms that were already better restructured before privatization. Additionally, this paper does not find evidence that foreign participation in privatized firms is linked to negative labor outcomes.     

Pay Dispersion and Executive Behaviour: Evidence from Innovation

Compensation packages are widely used to motivate top executives. Pay dispersion among a firm's executives, however, can be associated with the antithetic effects of social comparison and individual motivation, with unclear implications for the company. We focus on innovation activities, which represent an important channel through which pay dispersion can affect firm performance, and test our predictions by exploring innovative output as a function of executives’ pay dispersion in a panel of US firms. We find that executive pay dispersion acts as a double‐edged sword. On the one hand, the higher the dispersion in variable pay, the higher the innovation. On the other hand, the larger the dispersion in fixed pay, the lower the innovation. Results are robust to a number of tests, such as restricting the analysis to executives with direct responsibility for innovation projects and considering individual incentives in the form of cash pay.     

Markov chain Monte Carlo with the Integrated Nested Laplace Approximation

The Integrated Nested Laplace Approximation (INLA) has established itself as a widely used method for approximate inference on Bayesian hierarchical models which can be represented as a latent Gaussian model (LGM). INLA is based on producing an accurate approximation to the posterior marginal distributions of the parameters in the model and some other quantities of interest by using repeated approximations to intermediate distributions and integrals that appear in the computation of the posterior marginals. INLA focuses on models whose latent effects are a Gaussian Markov random field. For this reason, we have explored alternative ways of expanding the number of possible models that can be fitted using the INLA methodology. In this paper, we present a novel approach that combines INLA and Markov chain Monte Carlo (MCMC). The aim is to consider a wider range of models that can be fitted with INLA only when some of the parameters of the model have been fixed. We show how new values of these parameters can be drawn from their posterior by using conditional models fitted with INLA and standard MCMC algorithms, such as Metropolis–Hastings. Hence, this will extend the use of INLA to fit models that can be expressed as a conditional LGM. Also, this new approach can be used to build simpler MCMC samplers for complex models as it allows sampling only on a limited number of parameters in the model. We will demonstrate how our approach can extend the class of models that could benefit from INLA, and how the R-INLA package will ease its implementation. We will go through simple examples of this new approach before we discuss more advanced applications with datasets taken from the relevant literature. In particular, INLA within MCMC will be used to fit models with Laplace priors in a Bayesian Lasso model, imputation of missing covariates in linear models, fitting spatial econometrics models with complex nonlinear terms in the linear predictor and classification of data with mixture models. Furthermore, in some of the examples we could exploit INLA within MCMC to make joint inference on an ensemble of model parameters.     

A Unified Approach for Calculating Core Melt Frequency Caused by Internal and External Initiating Events

A unified approach for calculating the core melt frequency of a specific reactor caused by both internal and external accident initiators is demonstrated. Two classes of internal initiators are examined: transients, of which turbine trip is the chosen example, and loss-of-coolant events of various sizes. The concepts of hazard and fragility analysis first proposed for seismic risk analysis are linked to the frequencies of internal initiating events, and to the plant response as a function of the event intensity. Uncertainties are propagated using discrete probability distribution (DPD) arithmetic. Advantages of this approach include mathematical and conceptual consistency, and an improved uncertainty analysis, which are important considerations if risk studies are to be utilized in decision-making based on quantitative safety goals.