25,483 research outputs found

    Taxes and Pensions

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    Pension benefit rules depend on individual history far more than taxes do, and age plays a much larger role in pension determination than in tax determination. Apart from some simulation studies, theoretical studies of optimal tax design typically contain neither a mandatory pension system nor the behavioral dimensions that lie behind justifications commonly offered for mandatory pensions. Conversely, optimizing models of pension design typically do not include annual taxation of labor and capital incomes. After spelling out this contrast and reviewing (and rejecting) zero taxation of capital income based on the Atkinson-Stiglitz and Chamley-Judd results, this article raises the issue of tax-favored retirement savings, a topic where the two subjects come together.pension, income tax, social security

    Autobiography

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    My grandparents immigrated to the U.S. around the turn of the last century. My mother’s parents and six older siblings came from Poland. My father’s parents met in New York, she having come from Russia and he from Romania. My parents, both born in 1908, grew up in New York and never lived outside the metropolitan area. Both finished high school and went to work, my father studying at Brooklyn Law School at night while selling shoes during the day. When they married in 1929, my mother was earning 15aweekasabookkeeperandmyfather,15 a week as a bookkeeper and my father, 5 a week as a novice lawyer.Search frictions;

    Unemployment, Vacancies, Wages

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    Peter A. Diamond delivered his Prize Lecture on 8 December 2010 at Aula Magna, Stockholm University.Search frictions;

    Turbulence model reduction by deep learning

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    A central problem of turbulence theory is to produce a predictive model for turbulent fluxes. These have profound implications for virtually all aspects of the turbulence dynamics. In magnetic confinement devices, drift-wave turbulence produces anomalous fluxes via cross-correlations between fluctuations. In this work, we introduce a new, data-driven method for parameterizing these fluxes. The method uses deep supervised learning to infer a reduced mean-field model from a set of numerical simulations. We apply the method to a simple drift-wave turbulence system and find a significant new effect which couples the particle flux to the local \emph{gradient} of vorticity. Notably, here, this effect is much stronger than the oft-invoked shear suppression effect. We also recover the result via a simple calculation. The vorticity gradient effect tends to modulate the density profile. In addition, our method recovers a model for spontaneous zonal flow generation by negative viscosity, stabilized by nonlinear and hyperviscous terms. We highlight the important role of symmetry to implementation of the new method.Comment: To be published in Phys. Rev. E Rap. Comm. 6 pages, 7 figure
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