Taxation and foreign direct investment; a synthesis of empirical research

This paper reviews the empirical literature on the impact of company taxes on the allocation of foreign direct investment. We make the outcomes of 25 empirical studies comparable by computing the tax rate elasticity under a uniform definition. Read also the accompanying press release .The mean value of the tax rate elasticity in the literature is around 3.3, i.e. a 1%-point reduction in the host-country tax rate raises foreign direct investment in that country by 3.3%. There exists substantial variation across studies, however. By performing a meta analysis, the paper aims to explain this variation by the differences in characteristics of the underlying studies. Systematic differences between studies are found with respect to the type of foreign capital data used, and the type of tax rates adopted. We find no systematic differences in the responsiveness of investors from tax credit countries and tax exemption countries.

Robust predictions for an oscillatory bispectrum in Planck 2015 data from transient reductions in the speed of sound of the inflaton

We update the search for features in the Cosmic Microwave Background (CMB) power spectrum due to transient reductions in the speed of sound, using Planck 2015 CMB temperature and polarisation data. We enlarge the parameter space to much higher oscillatory frequencies of the feature, and define a robust prior independent of the ansatz for the reduction, guaranteed to reproduce the assumptions of the theoretical model and exhaustive in the regime in which the feature is easily distinguishable from the baseline cosmology. We find a fit to the $\ell\approx20$--$40$ minus/plus structure in Planck TT power spectrum, as well as features spanning along the higher $\ell$'s ($\ell\approx100$--$1500$). For the last ones, we compute the correlated features that we expect to find in the CMB bispectrum, and asses their signal-to-noise and correlation to the ISW-lensing secondary bispectrum. We compare our findings to the shape-agnostic oscillatory template tested in Planck 2015, and we comment on some tantalising coincidences with some of the traits described in Planck's 2015 bispectrum data.Comment: 19 pages - matches published versio

The merger of vertically offset quasi-geostrophic vortices

We examine the critical merging distance between two equal-volume, equal-potential-vorticity quasi-geostrophic vortices. We focus on how this distance depends on the vertical offset between the two vortices, each having a unit mean height-to-width aspect ratio. The vertical direction is special in the quasi-geostrophic model (used to capture the leading-order dynamical features of stably stratified and rapidly rotating geophysical flows) since vertical advection is absent. Nevertheless vortex merger may still occur by horizontal advection. In this paper, we first investigate the equilibrium states for the two vortices as a function of their vertical and horizontal separation. We examine their basic properties together with their linear stability. These findings are next compared to numerical simulations of the nonlinear evolution of two spheres of potential vorticity. Three different regimes of interaction are identified, depending on the vertical offset. For a small offset, the interaction differs little from the case when the two vortices are horizontally aligned. On the other hand, when the vertical offset is comparable to the mean vortex radius, strong interaction occurs for greater horizontal gaps than in the horizontally aligned case, and therefore at significantly greater full separation distances. This perhaps surprising result is consistent with the linear stability analysis and appears to be a consequence of the anisotropy of the quasi-geostrophic equations. Finally, for large vertical offsets, vortex merger results in the formation of a metastable tilted dumbbell vortex.Publisher PDFPeer reviewe

Superconductor-insulator transition in nanowires and nanowire arrays

Superconducting nanowires are the dual elements to Josephson junctions, with quantum phase-slip processes replacing the tunneling of Cooper pairs. When the quantum phase-slip amplitude ES is much smaller than the inductive energy EL, the nanowire responds as a superconducting inductor. When the inductive energy is small, the response is capacitive. The crossover at low temperatures as a function of ES/EL is discussed and compared with earlier experimental results. For one-dimensional and two-dimensional arrays of nanowires quantum phase transitions are expected as a function of ES/EL. They can be tuned by a homogeneous magnetic frustration.Comment: 15 pages, 10 figure

Explaining the Variation in Empirical Estimates of Tax Elasticities of Foreign Direct Investment

This study aims to explain the variation in empirical estimates in the literature on the elasticity of foreign direct investment with respect to company tax levels. To that end, we extend the meta analysis of De Mooij and Ederveen (2003) by considering an alternative classification of the literature, including new studies that have recently become available, and by paying more systematic attention to various control variables in primary studies. We find that the type of capital data and tax data exert a systematic impact on reported elasticities. Also controlling for openness and agglomeration tendencies appears to significantly affect the elasticity values

Quantum oscillations of rectified dc voltage as a function of magnetic field in an "almost" symmetric superconducting ring

Periodic quantum oscillations of a rectified dc voltage Vdc(B) vs the perpendicular magnetic field B were measured near the critical temperature Tc in a single superconducting aluminum almost symmetric ring (without specially created circular asymmetry) biased by alternating current with a zero dc component. With varying bias current and temperature, these Vdc(B) oscillations behave like the Vdc(B) oscillations observed in a circular-asymmetric ring but are of smaller amplitude. The Fourier spectra of the Vdc(B) functions exhibit a fundamental frequency, corresponding to the ring area, and its higher harmonics. Unexpectedly, satellite frequencies depending on the structure geometry and external parameters were found next to the fundamental frequency and around its higher harmonics.Comment: author english version, 2 pages, 3 figires, Proc. of the XXXIV Conference on Low-Temperature Physics "NT-34" (Russia, 2006

Statistical-mechanical iterative algorithms on complex networks

The Ising models have been applied for various problems on information sciences, social sciences, and so on. In many cases, solving these problems corresponds to minimizing the Bethe free energy. To minimize the Bethe free energy, a statistical-mechanical iterative algorithm is often used. We study the statistical-mechanical iterative algorithm on complex networks. To investigate effects of heterogeneous structures on the iterative algorithm, we introduce an iterative algorithm based on information of heterogeneity of complex networks, in which higher-degree nodes are likely to be updated more frequently than lower-degree ones. Numerical experiments clarified that the usage of the information of heterogeneity affects the algorithm in BA networks, but does not influence that in ER networks. It is revealed that information of the whole system propagates rapidly through such high-degree nodes in the case of Barab{\'a}si-Albert's scale-free networks.Comment: 7 pages, 6 figure

Joint Causal Inference from Multiple Contexts

The gold standard for discovering causal relations is by means of experimentation. Over the last decades, alternative methods have been proposed that can infer causal relations between variables from certain statistical patterns in purely observational data. We introduce Joint Causal Inference (JCI), a novel approach to causal discovery from multiple data sets from different contexts that elegantly unifies both approaches. JCI is a causal modeling framework rather than a specific algorithm, and it can be implemented using any causal discovery algorithm that can take into account certain background knowledge. JCI can deal with different types of interventions (e.g., perfect, imperfect, stochastic, etc.) in a unified fashion, and does not require knowledge of intervention targets or types in case of interventional data. We explain how several well-known causal discovery algorithms can be seen as addressing special cases of the JCI framework, and we also propose novel implementations that extend existing causal discovery methods for purely observational data to the JCI setting. We evaluate different JCI implementations on synthetic data and on flow cytometry protein expression data and conclude that JCI implementations can considerably outperform state-of-the-art causal discovery algorithms.Comment: Final version, as published by JML