22,481 research outputs found
Uncovering Offshore Financial Centers: Conduits and Sinks in the Global Corporate Ownership Network
Multinational corporations use highly complex structures of parents and
subsidiaries to organize their operations and ownership. Offshore Financial
Centers (OFCs) facilitate these structures through low taxation and lenient
regulation, but are increasingly under scrutiny, for instance for enabling tax
avoidance. Therefore, the identification of OFC jurisdictions has become a
politicized and contested issue. We introduce a novel data-driven approach for
identifying OFCs based on the global corporate ownership network, in which over
98 million firms (nodes) are connected through 71 million ownership relations.
This granular firm-level network data uniquely allows identifying both
sink-OFCs and conduit-OFCs. Sink-OFCs attract and retain foreign capital while
conduit-OFCs are attractive intermediate destinations in the routing of
international investments and enable the transfer of capital without taxation.
We identify 24 sink-OFCs. In addition, a small set of five countries -- the
Netherlands, the United Kingdom, Ireland, Singapore and Switzerland -- canalize
the majority of corporate offshore investment as conduit-OFCs. Each conduit
jurisdiction is specialized in a geographical area and there is significant
specialization based on industrial sectors. Against the idea of OFCs as exotic
small islands that cannot be regulated, we show that many sink and conduit-OFCs
are highly developed countries
An Alternative Approach to the Calculation and Analysis of Connectivity in the World City Network
Empirical research on world cities often draws on Taylor's (2001) notion of
an 'interlocking network model', in which office networks of globalized service
firms are assumed to shape the spatialities of urban networks. In spite of its
many merits, this approach is limited because the resultant adjacency matrices
are not really fit for network-analytic calculations. We therefore propose a
fresh analytical approach using a primary linkage algorithm that produces a
one-mode directed graph based on Taylor's two-mode city/firm network data. The
procedure has the advantage of creating less dense networks when compared to
the interlocking network model, while nonetheless retaining the network
structure apparent in the initial dataset. We randomize the empirical network
with a bootstrapping simulation approach, and compare the simulated parameters
of this null-model with our empirical network parameter (i.e. betweenness
centrality). We find that our approach produces results that are comparable to
those of the standard interlocking network model. However, because our approach
is based on an actual graph representation and network analysis, we are able to
assess cities' position in the network at large. For instance, we find that
cities such as Tokyo, Sydney, Melbourne, Almaty and Karachi hold more strategic
and valuable positions than suggested in the interlocking networks as they play
a bridging role in connecting cities across regions. In general, we argue that
our graph representation allows for further and deeper analysis of the original
data, further extending world city network research into a theory-based
empirical research approach.Comment: 18 pages, 9 figures, 2 table
A survey on Human Mobility and its applications
Human Mobility has attracted attentions from different fields of studies such
as epidemic modeling, traffic engineering, traffic prediction and urban
planning. In this survey we review major characteristics of human mobility
studies including from trajectory-based studies to studies using graph and
network theory. In trajectory-based studies statistical measures such as jump
length distribution and radius of gyration are analyzed in order to investigate
how people move in their daily life, and if it is possible to model this
individual movements and make prediction based on them. Using graph in mobility
studies, helps to investigate the dynamic behavior of the system, such as
diffusion and flow in the network and makes it easier to estimate how much one
part of the network influences another by using metrics like centrality
measures. We aim to study population flow in transportation networks using
mobility data to derive models and patterns, and to develop new applications in
predicting phenomena such as congestion. Human Mobility studies with the new
generation of mobility data provided by cellular phone networks, arise new
challenges such as data storing, data representation, data analysis and
computation complexity. A comparative review of different data types used in
current tools and applications of Human Mobility studies leads us to new
approaches for dealing with mentioned challenges
Commuting and Panel Spatial Interaction Models: Evidence of Variation of the Distance-Effect over Time and Space
We apply spatial interaction models using panel data to explain commuting behaviour in the Netherlands. Our main conclusion is that the distance-decay effect is not constant over time and that changes in this effect are region specific. In more densely populated regions the change in the distance-decay parameter is small suggesting that regional increases in congestion have a large negative effect on the increases in average commuting distance. The panel spatial interaction model we derive is well-suited for testing significance of the centrality index (an often used variable in spatial interaction models). Although evidence is found for competition effects in a pooled cross section framework, controlling for time invariant unobserved heterogeneity renders this relation spurious.
Complex network analysis and nonlinear dynamics
This chapter aims at reviewing complex network and nonlinear dynamical
models and methods that were either developed for or applied to socioeconomic
issues, and pertinent to the theme of New Economic Geography. After an introduction
to the foundations of the field of complex networks, the present summary
introduces some applications of complex networks to economics, finance, epidemic
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issue
Dynamical Patterns of Cattle Trade Movements
Despite their importance for the spread of zoonotic diseases, our
understanding of the dynamical aspects characterizing the movements of farmed
animal populations remains limited as these systems are traditionally studied
as static objects and through simplified approximations. By leveraging on the
network science approach, here we are able for the first time to fully analyze
the longitudinal dataset of Italian cattle movements that reports the mobility
of individual animals among farms on a daily basis. The complexity and
inter-relations between topology, function and dynamical nature of the system
are characterized at different spatial and time resolutions, in order to
uncover patterns and vulnerabilities fundamental for the definition of targeted
prevention and control measures for zoonotic diseases. Results show how the
stationarity of statistical distributions coexists with a strong and
non-trivial evolutionary dynamics at the node and link levels, on all
timescales. Traditional static views of the displacement network hide important
patterns of structural changes affecting nodes' centrality and farms' spreading
potential, thus limiting the efficiency of interventions based on partial
longitudinal information. By fully taking into account the longitudinal
dimension, we propose a novel definition of dynamical motifs that is able to
uncover the presence of a temporal arrow describing the evolution of the system
and the causality patterns of its displacements, shedding light on mechanisms
that may play a crucial role in the definition of preventive actions
Dynamical Patterns of Cattle Trade Movements
Despite their importance for the spread of zoonotic diseases, our
understanding of the dynamical aspects characterizing the movements of farmed
animal populations remains limited as these systems are traditionally studied
as static objects and through simplified approximations. By leveraging on the
network science approach, here we are able for the first time to fully analyze
the longitudinal dataset of Italian cattle movements that reports the mobility
of individual animals among farms on a daily basis. The complexity and
inter-relations between topology, function and dynamical nature of the system
are characterized at different spatial and time resolutions, in order to
uncover patterns and vulnerabilities fundamental for the definition of targeted
prevention and control measures for zoonotic diseases. Results show how the
stationarity of statistical distributions coexists with a strong and
non-trivial evolutionary dynamics at the node and link levels, on all
timescales. Traditional static views of the displacement network hide important
patterns of structural changes affecting nodes' centrality and farms' spreading
potential, thus limiting the efficiency of interventions based on partial
longitudinal information. By fully taking into account the longitudinal
dimension, we propose a novel definition of dynamical motifs that is able to
uncover the presence of a temporal arrow describing the evolution of the system
and the causality patterns of its displacements, shedding light on mechanisms
that may play a crucial role in the definition of preventive actions
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