Immigration,Growth, and Unemployment: Panel VAR Evidence from OECD Countries

This paper examines empirically the interaction between immigration and host country economic conditions. We employ panel vector autoregression (VAR) techniques for a large annual data set on 22 OECD countries over the period 1987–2009. The VAR approach addresses the endogeneity problem by allowing for endogenous interactions between the variables in the system. Our results provide evidence of migration contribution to host economic prosperity (positive impact on GDP per capita and negative impact on aggregate unemployment, native‐ and foreign‐born unemployment rates). We also find that migration is influenced by host economic conditions (migration responds positively to host GDP per capita and negatively to host total unemployment rate).     

Personal development plans supporting employee learning and perceived performance: the moderating role of self-directedness

Due to developments in the current dynamic business environment which is more global and competitive than ever, employees’ lifelong learning and the way organizations support their continuous development are pivotal. Many organizations introduce personal development plans (PDPs) to support employees’ learning, however, not always taking into account their personal characteristics. This study had a twofold purpose as it was designed to explore on the one hand the relationship between PDPs in the workplace with employee’s undertaking of learning activities and perceived performance, and on the other hand the possible moderating role of employee self-directedness on this relationship. For this study, data collected by means of a questionnaire at two different organizations were used, one organization foregoing profit and one non-profit activity. The results indicated significant positive relations between the individual supporting factor ‘employee learning and reflection’ and the undertaking of learning activities and perceived performance and between employee self-directedness and the undertaking of learning activities and perceived performance. The same accounted for the moderating effect of employee self-directedness on the interaction between learning and reflection and perceived performance. Some findings were surprising, nevertheless resulted in valuable insights and implications for practice.     

Influence functions of the Spearman and Kendall correlation measures

Nonparametric correlation estimators as the Kendall and Spearman correlation are widely used in the applied sciences. They are often said to be robust, in the sense of being resistant to outlying observations. In this paper we formally study their robustness by means of their influence functions and gross-error sensitivities. Since robustness of an estimator often comes at the price of an increased variance, we also compute statistical efficiencies at the normal model. We conclude that both the Spearman and Kendall correlation estimators combine a bounded and smooth influence function with a high efficiency. In a simulation experiment we compare these nonparametric estimators with correlations based on a robust covariance matrix estimator.     

The Impact of Growth,Labour Cost and Working Time on Employment: Lessons from the French Experience

Abstract. In order to account for the evolution of employment at the macro‐economic level, we have developed a model wherein employment is explained by added value, working time and real labour cost. Estimations using quarterly French macro‐economic data are carried out in a multivariate framework for three sets of sectors. We obtain a relationship in which employment rises when there is growth and falls when labour cost or working time rises, in both industrial and non‐industrial sectors. This model then allows retrospective measurement of the contributions of each of the variables with respect to employment inflections since the mid‐1980s.     

Consistent procedures for multiclass classification of discrete diffusion paths

The recent advent of modern technology has generated a large number of datasets which can be frequently modeled as functional data. This paper focuses on the problem of multiclass classification for stochastic diffusion paths. In this context we establish a closed formula for the optimal Bayes rule. We provide new statistical procedures which are built either on the plug-in principle or on the empirical risk minimization principle. We show the consistency of these procedures under mild conditions. We apply our methodologies to the parametric case and illustrate their accuracy with a simulation study through examples.     

On Bivariate Risk Premia

This note examines the conditions under which the bivariate risk premium for one risk may be negative even if both risks are positively correlated, using a mean variance setting. The link between the bivariate risk premium and the partial bivariate risk premia is also investigated.     

On the adaptive wavelet deconvolution of a density for strong mixing sequences

This paper studies the estimation of a density in the convolution density model from strong mixing observations. The ordinary smooth case is considered. Adopting the minimax approach under the mean integrated square error over Besov balls, we explore the performances of two wavelet estimators: a linear one based on projections and a non-linear one based on a hard thresholding rule. The feature of the non-linear one is to be adaptive, i.e., it does not require any prior knowledge of the smoothness class of the unknown density in its construction. We prove that it attains a fast rate of convergence which corresponds to the optimal one obtained in the standard i.i.d. case up to a logarithmic term.