1,804 research outputs found
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 -- minus/plus structure in Planck TT power spectrum, as
well as features spanning along the higher 's (--).
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
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