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In this article we improve on the literature dealing with the polarising effects of technological change on wages by proposing more rigorous definitions of wage dispersion within industries and of the different types and effects of innovation.\ud We carry out an analysis across 10 manufacturing and service sectors in seven\ud European countries (France, Italy, Germany, the Netherlands, Portugal, Spain and\ud the UK), for two time periods. In addition to structural economic variables, we\ud draw data from two waves of the Community Innovation Surveys (CIS 2, 1994–\ud 1996 and CIS3, 1998–2000) and from two waves of the European Community\ud Household Panel (ECHP, 1994 and 2001) providing information on employment,\ud wages, education and other individual’s characteristics, that we grouped in three\ud skill groups: managers and professionals, white-collar and blue-collar workers.\ud We set up econometric models to study the impact that different technological\ud strategies, labour market patterns, education and training have on the levels of\ud wage polarisation within industries. Higher wage polarisation is found in\ud industries with strong product innovation and high shares of workers with\ud university education. Wage compression is associated to the diffusion of new\ud process technologies and to high shares of workers with secondary education

The paper addresses large-scale, convex optimization problems that need to be solved in a distributed way by agents communicating according to a random time-varying graph. Specifically, the goal of the network is to minimize the sum of local costs, while satisfying local and coupling constraints. Agents communicate according to a time-varying model in which edges of an underlying connected graph are active at each iteration with certain nonuniform probabilities. By relying on a primal decomposition scheme applied to an equivalent problem reformulation, we propose a novel distributed algorithm in which agents negotiate a local allocation of the total resource only with neighbors with active communication links. The algorithm is studied as a subgradient method with block-wise updates, in which blocks correspond to the graph edges that are active at each iteration. Thanks to this analysis approach, we show almost sure convergence to the optimal cost of the original problem and almost sure asymptotic primal recovery without resorting to averaging mechanisms typically employed in dual decomposition schemes. Explicit sublinear convergence rates are provided under the assumption of diminishing and constant step-sizes. Finally, an extensive numerical study on a plug-in electric vehicle charging problem corroborates the theoretical results.

We present a distributed optimization algorithm for solving online personalized optimization problems over a network of computing and communicating nodes, each of which linked to a specific user. The local objective functions are assumed to have a composite structure and to consist of a known time-varying (engineering) part and an unknown (userspecific) part. Regarding the unknown part, it is assumed to have a known parametric (e.g., quadratic) structure a priori, whose parameters are to be learned along with the evolution of the algorithm. The algorithm is composed of two intertwined components: (i) a dynamic gradient tracking scheme for finding local solution estimates and (ii) a recursive least squares scheme for estimating the unknown parameters via user's noisy feedback on the local solution estimates. The algorithm is shown to exhibit a bounded regret under suitable assumptions. Finally, a numerical example corroborates the theoretical analysis.